reprompter

name	reprompter
description	Transform rough prompts into structured, high-scoring prompts for coding agents. Use when: "reprompt", "clean up this prompt", "before /goal", "Codex /goal", "Claude Code /goal", "Hermes /goal", "repromptverse", "reprompter teams", "smart run", "engineering/ops/research/marketing swarm", "compile to workflow", "workflow preflight", "dynamic workflow", multi-agent tasks, audits, parallel work, "reverse reprompt", "prompt dna", "extract prompt from". Don't use for simple Q&A, casual chat, or execution-only tasks. Outputs: structured XML/Markdown prompt + before/after score; /goal command card (Codex/Claude Code/Hermes); optional team brief + per-agent prompts + Agent Cards; Reverse Extraction Card; Workflow Command Card + runnable .workflow.js (Workflow preflight lane / Option H). Target score: Single and Goal preflight >= 7/10; Repromptverse per-agent >= 8/10; Reverse >= 7/10.
compatibility	Single mode works on Claude surfaces, OpenClaw, Codex, Grok CLI, and Hermes Agent. `/goal` preflight mode works on Codex CLI (any version exposing the `goals` feature), Claude Code CLI v2.1.139+, and Hermes Agent; all three runtimes accept the same `/goal <objective>` shape. Disabled on Claude surfaces without `/goal` support, OpenClaw, and Grok CLI. Repromptverse mode supports Claude Code (TeamCreate or tmux → Option B/A), OpenClaw (sessions_spawn → Option C), Codex CLI (native subagents or `codex exec` → Option D), Grok CLI 4.3+ (spawn_subagent F1 or `grok -p` F2 → Option F), and Hermes Agent (delegate_task G1, shell-level G2, Kanban G3 → Option G). Sequential fallback (Option E) works with any LLM runtime. Workflow preflight lane + Repromptverse Option H target Claude Code's dynamic `Workflow` tool (JS-scripted background fan-out with schema-validated returns and resume); additive, detected by tool presence, with first-class ultracode. A post-output delivery step can — offered once as a structured choice (plain-text fallback) over the relay targets headless-relay's preflight marks available (built-in lanes plus user-connected custom/local targets), never auto-executed — hand a finished Single/Reverse prompt to the headless-relay skill; the orchestrator reviews the relayed answer against the prompt's success criteria by default. When the relay skill is not installed, no target is available, or availability cannot be verified, the step is invisible.
metadata	{"author":"AytuncYildizli","version":"12.17.0"}

RePrompter v12.17.0

Your prompt sucks. Let's fix that. Single prompts, /goal preflight, full agent teams, reverse-engineer from great outputs, or compile to a Claude dynamic Workflow — one skill, five output lanes. v12.17.0 makes relay delivery availability-gated and structured (only live targets offered, custom/local targets included) with orchestrator review of the relayed answer by default.

---

Five output lanes

Lane	Trigger	What happens
Single	"reprompt this", "clean up this prompt"	Interview → structured prompt → score
/goal preflight	"before /goal", "for /goal", "Codex /goal", "Claude Code /goal", "Hermes /goal", "/goal preflight", "Codex goal prompt"	Codex CLI, Claude Code CLI v2.1.139+, or Hermes Agent: infer user intent → build expanded prompt → compress into exact /goal <summary of expanded prompt> command
Repromptverse	"reprompter teams", "repromptverse", "run with quality", "smart run", "smart agents", "campaign swarm", "engineering swarm", "ops swarm", "research swarm"	Dimension Interview → Plan team → Agent Cards → reprompt each agent → execute → Result Cards → evaluate → retry
Reverse	"reverse reprompt", "reprompt from example", "learn from this", "extract prompt from", "prompt dna", "prompt genome"	Analyze exemplar → classify → extract prompt DNA → generate XML prompt → score → inject into flywheel
Workflow preflight	"workflow preflight", "compile to workflow", "build a workflow script", "dynamic workflow", "run via workflow tool", "make a workflow"	Reprompt task → build expanded prompt → compile to a runnable .workflow.js (pure-literal meta, schema returns, bounded retry; ultracode adds adversarial verify + completeness critic) → emit Workflow Command Card. Also Repromptverse Phase-3 Option H.

Auto-detection: if task mentions 2+ systems, "audit", or "parallel" → ask: "This looks like a multi-agent task. Want to use Repromptverse mode?"

Definition — 2+ systems means at least two distinct technical domains that can be worked independently. Examples: frontend + backend, API + database, mobile app + backend, infrastructure + application code, security audit + cost audit.

Don't use when

• User wants a simple direct answer (no prompt generation needed)
• User wants casual chat/conversation
• Task is immediate execution-only with no reprompting step
• Scope does not involve prompt design, structure, or orchestration

Clarification: RePrompter does support code-related tasks (feature, bugfix, API, refactor) by generating better prompts. It does not directly apply code changes in Single mode. Direct code execution belongs to coding-agent unless Repromptverse execution mode is explicitly requested.

---

Lane: /goal preflight

When the user mentions /goal, before /goal, for /goal, "Codex /goal", "Claude Code /goal", "Hermes /goal", or asks to improve a goal prompt, run RePrompter before the goal is submitted.

This lane works on Codex CLI (any version exposing the goals feature), Claude Code CLI v2.1.139+ (the release that shipped a native /goal slash command on 2026-05-11), and Hermes Agent (persistent goals documented in the v0.13.0 / 2026.5.7 release). These runtimes accept the same /goal <objective> shape, so the compression flow is identical; only the setup check and a few runtime-specific operational notes differ. If the target runtime is Claude surfaces without /goal support, OpenClaw, Grok CLI, Gemini, or another LLM, use Single mode or Repromptverse instead; do not emit a /goal command for runtimes that have no /goal surface.

Detecting the target runtime

Pick the runtime once, at the start of the lane, and pass it through to the Card:

User signal	Runtime
"Codex /goal", "for Codex /goal", explicit codex mention	Codex CLI
"Claude Code /goal", "/goal in Claude Code", explicit claude / claude-code mention	Claude Code CLI (≥ v2.1.139)
"Hermes /goal", "/goal in Hermes", explicit hermes / hermes-agent mention	Hermes Agent
Bare "/goal" or "before /goal" with no runtime marker	ASK which runtime, with the three options as buttons; default to the user's primary CLI if known from session context

Process:

1. Treat the input as Single prompt mode unless it clearly needs Repromptverse.
2. Detect the target runtime (table above). Carry the runtime label through the rest of the lane.
3. Render the Goal Command Card first, with Runtime populated from step 2.
4. Infer the user's real intent from the rough prompt: desired outcome, hidden constraint, success signal, and likely risk.
5. Build the rich expanded prompt first, using the normal RePrompter structure: goal/task, context, assumptions, requirements, constraints, execution notes, and success criteria.
6. Compress that expanded prompt into a dense one-line goal summary. This should feel like a summary of a long XML prompt, not a slightly polished copy of the user's rough sentence. The compression rule is identical across runtimes — both Codex's alpha /goal and Claude Code's v2.1.139+ /goal consume <objective> as a single argument.
7. Generate an exact copy-paste command: /goal <summary of expanded prompt>.
8. Do not put the full XML or Markdown document after /goal; only the compressed summary belongs in the command.
9. Include the expanded prompt basis after the command so the user can inspect what was compressed or send it as a follow-up normal message after the goal is set.
10. Tell the user to run the exact /goal <summary of expanded prompt> command in the runtime chosen at step 2.
11. Do not claim RePrompter can automatically intercept /goal; slash commands are user-invoked in both Codex and Claude Code unless the local runtime adds a separate hook.

Goal Command Card

Both runtimes shape the slash command as /goal <objective>. Render this card before the generated command:

Field	Content
Goal Command	Exact one-line /goal <summary of expanded prompt> command
Compressed From	Expanded RePrompter prompt
Objective	One sentence naming the reprompted intent the runtime should pursue
Runtime	Codex CLI, Claude Code CLI (≥ v2.1.139), or Hermes Agent — whichever was detected in step 2 above
Mode	/goal preflight
Paste Into	Codex TUI prompt, Claude Code TUI prompt, or Hermes TUI prompt, as-is
Risk Level	low / medium / high, based on blast radius
Missing Inputs	Up to 3 unresolved unknowns; use documented assumptions for reasonable defaults and write none when the prompt is ready
Verification	2-4 checks the agent should run while pursuing the goal
Quality	Before score → after score, with the weakest remaining dimension

Then output:

/goal {dense single-line summary of the expanded prompt}

Then show the expanded prompt basis:

<goal>{specific outcome}</goal>
<context>
- {known repo/runtime/user context}
</context>
<assumptions>
- {reasonable default applied because this autonomous goal should not block on a low-value question}
</assumptions>
<requirements>
- {measurable requirement}
</requirements>
<constraints>
- {boundary or non-goal}
</constraints>
<execution_notes>
- Start with discovery before edits.
- Keep changes scoped and reversible.
- Run the verification checks listed in the Goal Command Card.
</execution_notes>
<success_criteria schema_version="1">
  <criterion id="{kebab-case-id}" verification_method="manual">
    <description>{testable pass condition}</description>
  </criterion>
</success_criteria>

Runtime-specific operational notes

The compression flow is shared, but the two /goal surfaces have small behavioral differences worth surfacing in the expanded prompt's <execution_notes> block:

Claude Code CLI (≥ v2.1.139):

• /goal sets a thread-level persistent objective that survives /resume, terminal close, and context compaction. Only one goal per session — setting a new /goal replaces the previous one.
• After each turn a separate fast evaluator model (Haiku) checks the completion condition against the transcript. If not met, the runtime triggers another turn without user input.
• The evaluator only judges what Claude surfaces in the transcript, so the expanded prompt should require the agent to print artifact paths, file contents, or test results — proof must be visible.
• Pause / resume controls: /goal pause and /goal resume (handy for long-running goals interrupted by ad-hoc work).
• Optional budget constraints (token or wall-clock) prevent runaway costs.
• /goal requires hooks. When disableAllHooks or allowManagedHooksOnly is set in settings.json, /goal is unavailable. v2.1.139 silently hung in this case; v2.1.140 changed the failure mode to a clear error message but did not make /goal work under those settings. If you operate in a managed environment that blocks hooks, the /goal preflight lane cannot run on Claude Code until hooks are permitted — use Single mode in that case.

Codex CLI:

• /goal is an experimental alpha feature gated by features.goals = true in ~/.codex/config.toml. The local alpha binary exposes Usage: /goal <objective>, ThreadGoal.objective, tokenBudget, /goal pause, /goal resume, and /goal clear.
• Codex's /goal is invoked the same way (/goal <objective>), but config-gated — a fresh session is required after enabling.

Hermes Agent:

• /goal sets a persistent objective that continues across turns until the runtime's goal judge considers it complete, the user pauses/clears it, or the configured turn budget is reached.
• Goal state survives /resume, and user messages preempt the continuation loop.
• Useful controls: /goal status, /goal pause, /goal resume, and /goal clear.
• Default continuation budget is bounded (goals.max_turns, documented default 20), so the expanded prompt should make success criteria and verification visible.
• Hermes supports /goal in both CLI and messaging-command surfaces; RePrompter still only emits the copy-paste command and does not intercept slash commands.

The Card's Risk Level and Verification fields apply equally to all supported /goal runtimes.

Setup check

Pick the block matching the detected runtime.

Codex CLI:

npm install -g @openai/codex@latest
codex features list | grep '^goals'

If the feature exists but is disabled, configure:

[features]
goals = true

Then start a fresh Codex session so the slash-command surface reloads.

Claude Code CLI:

claude --version
# Expect "2.1.139" or later. If older, upgrade:
#   curl -sL https://claude.ai/install.sh | bash
# or follow the install path you used originally.

No config flag is required — /goal is enabled by default once Claude Code is at v2.1.139 or later. However, /goal depends on Claude Code's hooks layer: if disableAllHooks or allowManagedHooksOnly is set in ~/.claude/settings.json, the command is unavailable on any version. v2.1.139 silently hung in that case; v2.1.140 surfaces a clear error message instead. Upgrading does not re-enable /goal under hook-blocking settings — permitting hooks is the only way to use /goal on Claude Code. Managed environments that block hooks should use Single mode for goal-shaped work.

Hermes Agent:

hermes --version
# Expect a release with persistent goals support (v0.13.0 / 2026.5.7 or later).

No feature flag is required for normal /goal use. Optional tuning lives in Hermes config:

[goals]
max_turns = 20

---

Lane: Workflow preflight

When the user says "compile to workflow", "build a workflow script", "workflow preflight", "make a workflow", "run via workflow tool", or "dynamic workflow", reprompt the task and compile it into a runnable Claude dynamic Workflow script. This is the execution-compilation sibling of the /goal preflight lane: RePrompter builds the expanded prompt first, then emits a .workflow.js the user runs via the Workflow tool — RePrompter does not run it.

This lane is the same surface as Repromptverse Option H; use this lane when the user wants the compiled script directly, and Option H when Phase 3 auto-picks the Workflow tool during a Repromptverse run.

Compatibility

Single Claude-native surface: requires the Workflow tool in the current toolset. There is no /goal-style command — the output is a Workflow({ scriptPath, args }) invocation. Other runtimes (Codex, Grok, Hermes, OpenClaw) use their own Repromptverse options (D/F/G/C) and the /goal preflight lane instead.

Runtime detection

Signal	Runtime
A tool named Workflow is present in the current toolset	Claude dynamic Workflow tool — proceed with this lane
No Workflow tool	Fall back to Repromptverse (Option B/A/etc.) or /goal preflight

Process

1. Treat the input as a team task. Run routeIntent — a workflow-lane trigger returns mode: "workflow".
2. Infer the real intent and build the rich expanded prompt (the XML basis below) with all eight base tags + <assumptions> + <success_criteria>. Because Workflow preflight is autonomous execution, skip clarification questions when a reasonable default exists and document the default in <assumptions>.
3. Reprompt one prompt per role (each owns ONE domain, no overlap), exactly as Repromptverse Phase 2.
4. Compile to a .workflow.js via scripts/workflow-command.js (buildWorkflowCommand): pure-literal meta, schema-validated agent() returns, parallel()/pipeline() per the H1/H2 heuristic, runId/taskname from args, model omitted, filter(Boolean), bounded delta-retry (max 2/role).
5. Render the Workflow Command Card first, then the emitted script, then the expanded-prompt basis.
6. High-risk forbidden surfaces (prod/auth/secret/...) block emission — set blocked: true, script: null. There is no in-tool override; rescope the task (remove the high-risk surface) to compile a script. Same block-gate as /goal.
7. Tell the user to run Workflow({ scriptPath, args }); resume an interrupted run with resumeFromRunId (cached agent() prefix short-circuits).

Workflow Command Card

Field	Content
Workflow Command	Exact Workflow({ scriptPath, args: { taskname, runId } }) invocation
Compiled From	Expanded RePrompter prompt
Objective	One sentence naming the reprompted intent
Runtime	Claude dynamic Workflow tool
Mode	Workflow preflight
Paste Into	Workflow tool (scriptPath + args), as-is
Script Path	/tmp/reprompter-workflow/rpt-{taskname}.workflow.js
Execution Pattern	parallel fan-out + bounded delta-retry (ultracode: + adversarial verify + completeness critic)
Budget	directive total / inherit / none
Risk Level	low / medium / high
Missing Inputs	Up to 3 unresolved unknowns; use documented assumptions for reasonable defaults, or none
Verification	2-4 checks the run should surface (per-role scores, missing roles)
Quality	Before score → after score

Then output the emitted script:

export const meta = {
  name: "rpt-{taskname}",
  description: "{one-line objective}",
  phases: [
    { title: "Plan" },
    { title: "Execute" },
    { title: "Evaluate" },
  ],
}

const taskname = (args && args.taskname) || "{taskname}"   // bare fallback == command args; only meta.name is prefixed (resume id stability)
const runId = (args && args.runId) || taskname

const FINDINGS_SCHEMA = {
  type: "object",
  additionalProperties: false,
  required: ["role", "findings", "self_score"],
  properties: {
    role: { type: "string" },
    findings: { type: "array", items: { type: "string" } },
    self_score: { type: "integer", minimum: 1, maximum: 10 },
  },
}

const AGENTS = [ /* one reprompted prompt per role; model omitted */ ]

phase("Plan")
log(`Workflow ${runId}: dispatching ${AGENTS.length} reprompted agents`)

phase("Execute")
const results = (await parallel(
  AGENTS.map((a) => () => agent(a.prompt, { label: a.label, phase: "Execute", schema: FINDINGS_SCHEMA }))
)).filter(Boolean)

phase("Evaluate")
const ACCEPT = 8
const final = []
for (const r of results) {
  let current = r, attempts = 0
  while (current && current.self_score < ACCEPT && attempts < 2) {
    attempts += 1
    current = await agent(`Previous ${current.role} attempt scored ${current.self_score}/10 (need ${ACCEPT}). Fix the gaps; return the improved structured result.`,
      { label: `retry:${current.role}`, phase: "Evaluate", schema: FINDINGS_SCHEMA })
  }
  if (current) final.push(current)
}

return {
  schema_version: "reprompter.workflow_outcome.v1",
  runId, taskname,
  results: final,
  missing: AGENTS.length - final.length,
  scores: final.map((f) => ({ role: f.role, score: f.self_score })),
}

Then the expanded-prompt basis (the reprompted XML that authors the workflow):

<role>{Workflow architect for this domain}</role>
<context>
- Raw operator request, target = Claude dynamic Workflow tool, route mode/profile
</context>
<assumptions>
- {Documented default used instead of blocking workflow compilation on a low-value question}
</assumptions>
<task>{Compile the request into a runnable .workflow.js fan-out.}</task>
<motivation>{Why this matters}</motivation>
<requirements>
- One reprompted agent per role; schema returns are the source of truth.
- meta pure-literal; runId/taskname from args; bounded retry.
</requirements>
<constraints>
- No wall-clock/randomness in-script; model omitted; filter(Boolean).
- High-risk forbidden surfaces block emission (no in-tool override; rescope to proceed).
</constraints>
<output_format>A .workflow.js script + a Workflow Command Card.</output_format>
<success_criteria schema_version="1">
  <criterion id="schema-returns-source-of-truth" verification_method="manual">
    <description>In-run data flows through schema-validated agent() returns; tmp files are never read back as a handoff.</description>
  </criterion>
</success_criteria>

Schema-truth + parent-written mirror

The emitted script returns a reprompter.workflow_outcome.v1 payload; it never reads /tmp/rpt-{taskname}-{role}.md back. The parent writes those tmp artifacts from the returned objects after the run completes, so the existing Status Line count, Phase-4 evaluation, and outcome-record.js --role flywheel path keep working unchanged — and the throwing wall-clock/randomness calls stay out of the sandbox.

Setup check

Confirm the Workflow tool is present in the current toolset (Claude dynamic Workflow runtime). If absent, use Repromptverse Option B/A or the /goal preflight lane instead.

Compile with the workflow compiler (scripts/workflow-command.js) on the rough task with an --out-dir: it writes workflow-command.json, the runnable rpt-{taskname}.workflow.js, workflow-command-card.json, and reprompter-expanded-prompt.md. Add --ultracode / --no-ultracode to force the emission tier.

See references/workflow-template.md and references/runtime/claude-workflow-runtime.md for the full template and runtime contract.

---

Lane: Single prompt

Process

1. Receive raw input
2. Input guard — if input is empty, a single word with no verb, or clearly not a task → ask the user to describe what they want to accomplish
   • Reject examples: "hi", "thanks", "lol", "what's up", "good morning", random emoji-only input
   • Accept examples: "fix login bug", "write API tests", "improve this prompt"
3. Quick Mode gate — under 20 words, single action, no complexity indicators → generate immediately
4. Smart Interview — use AskUserQuestion with clickable options (2-5 questions max) for interactive Single mode. For prompts destined for autonomous execution (goal/workflow/team lanes), skip questions with reasonable defaults and emit an <assumptions> block the user can veto before running.
5. Flywheel bias check (optional, read-only) — if REPROMPTER_FLYWHEEL_BIAS=1 is set in the environment, consult past outcomes before choosing a template. See "Flywheel bias injection" below.
6. Generate + Score — apply template, show before/after quality metrics. Generated prompts include a <success_criteria schema_version="1"> block with 3-6 <criterion> entries. Each criterion has id (kebab-case slug, unique in block), verification_method (rule | llm_judge | manual), a one-sentence <description>, and — depending on method — an inline <rule type="regex|predicate"> or <judge_prompt> (neither for manual). Schema of record: references/outcome-schema.md.
7. Single-pass evaluator — run self-eval rubric and do one delta rewrite if score < 7

Why criteria are emitted: so every prompt carries its own testable assertions; outcome records produced by scripts/outcome-record.js (added in the same PR) join criteria to results for flywheel learning.

Flywheel bias injection (v3 read-path)

Default: off. Enable explicitly with REPROMPTER_FLYWHEEL_BIAS=1 so runs with and without bias can be compared apples-to-apples until it earns the default.

When the flag is set, between the interview and the template pick:

1. Run npm run flywheel:query -- --task-type <slug> where <slug> is the task type identified from the interview (e.g. fix_bug, write_code).
2. Read the command's stdout. It's either null (cold start / low N) or a single JSON object with recipe, confidence, sampleCount.
3. Only bias on confidence ∈ {"medium", "high"} AND sampleCount >= 3. Low-confidence recommendations add noise without signal; treat them as cold start.
4. When biasing:
   • Prefer recipe.vector.templateId over the default intent-routed template.
   • Adopt recipe.vector.patterns alongside anything you would have picked from references/patterns/.
   • Match recipe.vector.capabilityTier in your reasoning about downstream execution.
5. Announce the decision in one line before the generate step so the user sees what happened:

   Flywheel: preferring <template> + [patterns] based on N past runs (score X/10, confidence) Or, if no bias applied: Flywheel: no bias (cold start / low confidence)

6. The bias changes which template/patterns you start from. The rest of the pipeline (interview content, generated prompt's XML structure, criteria emission) is unchanged. The flywheel never rewrites Claude's output.
7. Attribution (v3 part 3). When bias is applied, remember the chosen recipe's hash, confidence, and sampleCount until the outcome is recorded for this run. Then stamp them onto the record via scripts/outcome-record.js --applied-recommendation '{"recipe_hash":"<hash>","confidence":"<low|medium|high>","sample_count":<N>,"applied_at":"prompt_gen"}'. Use applied_at="phase_2" for Repromptverse team-wide bias. If no bias was applied (flag off, query returned null, or low confidence) OMIT the flag entirely — the absence of applied_recommendation on a record is what marks it as the bias-off control group for npm run flywheel:ab analysis. Never stamp a zero/placeholder block; absence is the signal.

Fleet sync (v12.14 privacy boundary)

Fleet sync shares only sanitized aggregate ledger rows from .reprompter/flywheel/outcomes.ndjson. It never reads .reprompter/outcomes/, and a pack contains no prompt text, no raw prompt hashes, no raw task slugs, no raw role/domain labels outside the coarse allowlist, and no hostnames.

npm run flywheel:export -- --origin o-laptop
npm run flywheel:import -- .reprompter/flywheel/packs/o-laptop-20260703.ndjson

Exported rows deterministically hash runId/taskId, coarsen non-allowlisted recipe.vector.domain labels, and recompute the recipe fingerprint from the sanitized vector. Identical sanitized recipes from different machines still group together in flywheel:query/flywheel:report, but raw prompt fingerprints and local task labels do not leave the machine.

Transport is user-owned: put packs in a shared directory, rsync them, or move them through your own git/Tailscale/mesh workflow. RePrompter itself does not network for fleet sync. The default flywheel cap remains 500 rows; active fleets can opt into a larger local ledger with REPROMPTER_FLYWHEEL_MAX_OUTCOMES=5000.

Generate after interview

After interview completes, immediately:

1. Select template based on task type
2. Generate the full polished prompt
3. Show quality score (before/after table)
4. Ask if user wants to execute or copy

❌ WRONG: Ask interview questions → stop
✅ RIGHT: Ask interview questions → generate prompt → show score → offer to execute

Interview questions

Ask via AskUserQuestion. Max 5 questions total.

Standard questions (priority order — drop lower ones if task-specific questions are needed):

1. Task type: Build Feature / Fix Bug / Refactor / Write Tests / API Work / UI / Security / Docs / Content / Research / Multi-Agent
   • If user selects Multi-Agent while currently in Single mode, immediately transition to Repromptverse Phase 1 (Team Plan) and confirm team execution mode (Parallel vs Sequential).
2. Execution mode: Single Agent / Team (Parallel) / Team (Sequential) / Let RePrompter decide
3. Motivation: User-facing / Internal tooling / Bug fix / Exploration / Skip (drop first if space needed)
4. Output format: XML Tags / Markdown / Plain Text / JSON (drop first if space needed)

Task-specific questions (required for compound prompts — replace lower-priority standard questions):

• Extract keywords from prompt → generate relevant follow-up options
• Example: prompt mentions "telegram" → ask about alert type, interactivity, delivery
• Vague prompt fallback: if input has no extractable keywords (e.g., "make it better"), ask open-ended: "What are you working on?" and "What's the goal?" before proceeding

Single mode pattern pack (Microsoft-inspired)

Apply these patterns even without multi-agent execution:

1. Intent router — map task to template with explicit priority rules
2. Constraint normalizer — convert vague goals into measurable requirements/limits
3. Spec contract — enforce role/context/task/requirements/constraints/output/success structure
4. Evaluator loop — score clarity/specificity/structure/constraints/verifiability/decomposition; if score < 7, produce one delta rewrite

This keeps Single mode deterministic and compatible across Claude, OpenClaw, and Codex runtimes.

Auto-detect complexity

Signal	Suggested mode
2+ distinct systems (e.g., frontend + backend, API + DB, mobile + backend)	Team (Parallel)
Pipeline (fetch → transform → deploy)	Team (Sequential)
Single file/component	Single Agent
"audit", "review", "analyze" across areas	Team (Parallel)
"campaign", "launch", "growth", "SEO", "content calendar", "funnel"	Team (Parallel, Marketing Swarm)
"architecture", "feature delivery", "refactor", "migration", "test coverage"	Team (Parallel, Engineering Swarm)
"incident", "uptime", "gateway", "latency", "cron", "SLO", "health"	Team (Parallel, Ops Swarm)
"benchmark", "compare", "tradeoff", "options", "analysis", "research"	Team (Parallel, Research Swarm)

Quick mode

⚠️ Force interview signals (check first)

If ANY of the following signals are present, SKIP Quick Mode and go directly to interview — no exceptions:

Signal category	Keywords / patterns
Scope keywords	system, platform, service, pipeline, dashboard, module, suite, management
Ownership / existing state	our, existing, the current, fresh, updated
Integration verbs	integrate, merge, connect, combine, sync
Compound tasks	"and", "plus", "also", "as well as"
State management	track, sync, manage
Vague modifiers	better, improved, some, maybe, kind of
Ambiguous pronouns	"it", "this", "that" without a clear referent in the same sentence
Comprehensiveness	comprehensive, complete, full, end-to-end, overall

Clause detection: Treat any prompt with two or more independent clauses (comma-separated actions, semicolon-joined tasks, or consecutive imperative verbs) as a compound task — force interview.

Broad-scope noun enforcement (count_distinct_systems()): Count the number of distinct systems/modules implied by broad-scope nouns (system, module, suite, platform, pipeline, dashboard, management). If count >= 1 AND the prompt does not name a single, specific identifier — force interview.

Enable Quick Mode (only when NO force-interview signals are present)

Enable when ALL true:

• < 20 words (excluding code blocks)
• Exactly 1 action verb from: add, fix, remove, rename, move, delete, update, create
• Single target (one specific, named file, component, or identifier — NOT a broad-scope noun such as system, module, suite, or management)
• No conjunctions (and, or, plus, also)
• No vague modifiers (better, improved, some, maybe, kind of)

Task types & templates

Detect task type from input. Each type has a dedicated template in references/:

Type	Template	Use when
Feature	feature-template.md	New functionality (default fallback)
Bugfix	bugfix-template.md	Debug + fix
Refactor	refactor-template.md	Structural cleanup
Testing	testing-template.md	Test writing
API	api-template.md	Endpoint/API work
UI	ui-template.md	UI components
Security	security-template.md	Security audit/hardening
Docs	docs-template.md	Documentation
Content	content-template.md	Blog posts, articles, marketing copy
Research	research-template.md	Analysis/exploration
Marketing Swarm	marketing-swarm-template.md	Marketing-first multi-agent orchestration
Engineering Swarm	engineering-swarm-template.md	Engineering-first multi-agent orchestration
Ops Swarm	ops-swarm-template.md	Reliability/infra multi-agent orchestration
Research Swarm	research-swarm-template.md	Analysis/benchmark multi-agent orchestration
Repromptverse	repromptverse-template.md	Multi-agent routing + termination + evaluator loop
Multi-Agent	swarm-template.md	Basic multi-agent coordination
Reverse	reverse-template.md	Reverse-engineered prompt from exemplar output
Team Brief	team-brief-template.md	Team orchestration brief

Priority (most specific wins): marketing-swarm > engineering-swarm > ops-swarm > research-swarm > repromptverse > api > security > ui > testing > bugfix > refactor > content > docs > research > feature. For multi-agent tasks, use the best-fit swarm template + repromptverse-template + team-brief-template, then type-specific templates for each agent sub-prompt.

How it works: Read the matching template from references/{type}-template.md, then fill it with task-specific context. Templates are NOT loaded into context by default — only read on demand when generating a prompt. If the template file is not found, fall back to the Base XML Structure below.

To add a new task type: create references/{type}-template.md following the XML structure below, then add it to the table above.

Base XML structure

All templates follow this core section structure (8 required fields). XML is the default emitted format; Markdown headers are equally valid when requested by the user or runtime. Use as fallback if no specific template matches:

Exception: team-brief-template.md uses Markdown format for orchestration briefs. This is intentional — see template header for rationale.

<role>{Expert role matching task type and domain}</role>

<context>
- Working environment, frameworks, tools
- Available resources, current state
</context>

<task>{Clear, unambiguous single-sentence task}</task>

<motivation>{Why this matters — priority, impact}</motivation>

<requirements>
- {Specific, measurable requirement 1}
- {At least 3-5 requirements}
</requirements>

<constraints>
- {Load-bearing boundary or limit}
- {What to do instead of an unsafe or out-of-scope action}
</constraints>

<output_format>{Expected format, structure, length. If the target runtime supports structured-output APIs, name the shape here and enforce it through the API; embed full schemas only as fallback.}</output_format>

<success_criteria schema_version="1">
  <criterion id="no-regression" verification_method="rule">
    <description>Output does not reintroduce the original error signature.</description>
    <rule type="regex"><![CDATA[^(?!.*TypeError: cannot read property 'id' of undefined).*$]]></rule>
  </criterion>
  <criterion id="guards-null-user" verification_method="llm_judge">
    <description>Fix guards against the null-user edge case from the bug report.</description>
    <judge_prompt><![CDATA[Does the diff check that `user` is non-null before reading `user.id`? Reply pass or fail.]]></judge_prompt>
  </criterion>
  <criterion id="regression-test-added" verification_method="manual">
    <description>At least one regression test covers the previously failing scenario.</description>
  </criterion>
</success_criteria>

(The Base XML <success_criteria> example above matches the v1 schema in references/outcome-schema.md; real generated prompts should adapt the ids, descriptions, and rules to the task at hand.)

Project context detection

Auto-detect tech stack from current working directory ONLY:

• Scan package.json, tsconfig.json, prisma/schema.prisma, etc.
• Session-scoped — different directory = fresh context
• Opt out with "no context", "generic", or "manual context"
• Never scan parent directories or carry context between sessions

After the final prompt

Apply Deliver via headless-relay (post-output step): offer delivery once, only when that skill is installed; otherwise stay completely silent about it.

---

Lane: Repromptverse (Agent Teams)

TL;DR

Raw task in → quality output out. Every agent gets a reprompted prompt.

Phase 1: Score raw prompt, dimension interview if needed, plan team, show Agent Cards (YOU do this, ~45s)
Phase 2: Write XML-structured prompt per agent (YOU do this, ~2min)
Phase 3: Launch agents (tmux, TeamCreate, Workflow tool, sessions_spawn, Codex, or sequential) (AUTOMATED)
Phase 4: Show Result Cards, score, retry if needed (YOU do this)

Key insight: The reprompt phase costs ZERO extra tokens — YOU write the prompts, not another AI.

Repromptverse control plane (Microsoft-inspired)

Every multi-agent run must include:

1. Routing policy — who speaks next and why (selector-style routing for non-trivial teams)
2. Termination policy — max turns, max wall time, and no-progress stop condition
3. Artifact contract — one writer per output file, fixed schema for handoffs
4. Evaluator loop — score each artifact, retry only with delta prompts (max 2 retries)

Use references/repromptverse-template.md to enforce this contract.

Domain profile auto-load rules (lazy-load, on demand):

• Marketing intent (campaign, launch, growth, seo, content calendar, funnel) -> references/marketing-swarm-template.md
• Engineering intent (architecture, feature delivery, refactor, migration, test coverage) -> references/engineering-swarm-template.md
• Ops intent (incident, uptime, gateway, latency, cron, slo, health) -> references/ops-swarm-template.md
• Research intent (benchmark, compare, tradeoff, analysis, research) -> references/research-swarm-template.md

Then merge with references/repromptverse-template.md for routing/termination/evaluation contract and add task-specific constraints.

Canonical implementation for deterministic routing lives in scripts/intent-router.js. If docs and code ever diverge, the script is the source of truth for benchmark/testing paths.

Phase 1: Team plan (~45 seconds)

1. Score raw prompt (1-10): Clarity, Specificity, Structure, Constraints, Decomposition
   • Phase 1 uses 5 quick-assessment dimensions. The full 6-dimension scoring (adding Verifiability) is used in Phase 4 evaluation.
2. Dimension Interview gate — check which askable dimensions scored < 5 (see Dimension Interview section below). In autonomous or batch-destined runs, prefer documented assumptions over blocking questions when a reasonable default exists.
3. Pick mode: parallel (independent agents) or sequential (pipeline with dependencies)
4. Define team: 2-5 agents max, each owns ONE domain, no overlap (informed by interviewContext if interview ran)
5. Show Plan Cards (see Agent Cards section below)
6. User confirmation gate — "Team plan ready. Proceed to execution?" User can approve, adjust, or cancel. In automated/batch runs, auto-proceed.
7. Write team brief to /tmp/rpt-brief-{taskname}.md (use unique tasknames to avoid collisions; includes interviewContext section if interview ran)

Dimension Interview (Repromptverse only)

Score-driven interview for Repromptverse mode. Distinct from Single mode's "Smart Interview" (which uses a standard question list). The Dimension Interview derives questions from low-scoring raw prompt dimensions.

Trigger logic

scores = score_raw_prompt(rawInput)  # 5 dimensions from step 1

# Structure is EXCLUDED — reprompter fixes structure via templates.
# Only 4 dimensions are interview-eligible:
askable = [d for d in scores if d.name != "Structure" and d.value <= 5]

# Threshold: less-than-or-equal. Scores of 5 ARE borderline and trigger questions.
if len(askable) == 0:
    SKIP interview → proceed to step 3 (pick mode)
elif len(askable) <= 2:
    ASK 1-2 questions (one per low dimension)
else:
    ASK 3-4 questions (max 4, prioritized by lowest score first)

Dimension-to-question mapping

Dimension	Score < 5 triggers	Question approach
Clarity	Task is ambiguous or multi-interpretable	Open-ended with dynamic options extracted from prompt keywords
Specificity	Scope is vague, no concrete targets	Dynamic options from prompt keywords + top-level directory names
Constraints	No boundaries defined	"Any areas to exclude?" with context-aware options
Decomposition	Unclear work split	"How many independent streams?" with suggested splits

Question rules:

• Use AskUserQuestion with clickable options (consistent with Single mode)
• Options are dynamic: extracted from prompt keywords + codebase context (config files + top-level dirs only — no deep analysis)
• Every question includes a free-text escape hatch option
• Priority order: lowest scoring dimension first
• Language follows user's input language

Skip/dismiss handling

• User skips all questions → proceed with empty interviewContext. Plan Cards note: "Interview: skipped by user"
• User answers some, skips others → populate only answered fields

Interview output (interviewContext)

Responses merge into an interviewContext written to the team brief file:

interviewContext = {
  scope: [from Specificity answer],
  excludes: [from Constraints answer],
  successCriteria: [from answers, or omitted — Phase 2 derives from requirements],
  taskClarification: [from Clarity answer, if asked]
}

When successCriteria is not gathered (question not asked or user skipped), omit the field. Phase 2 derives success criteria from requirements as it does today.

For autonomous execution lanes, record safe defaults in the generated prompt instead of asking low-value clarification questions:

<assumptions>
- Scope defaults to the files and systems named or strongly implied by the request.
- Excludes default to unrelated refactors, new dependencies, and destructive production changes.
- Verification defaults to the smallest local checks that prove the requested outcome.
</assumptions>

The user can veto or edit these assumptions before execution. Interactive Single mode still asks when ambiguity changes the requested outcome.

How interviewContext feeds into later phases:

• Agent count and roles — scope determines which agents are created
• Per-agent <constraints> — excludes injected into each agent's prompt
• Per-agent <success_criteria> — user expectations propagated
• Template selection — clarified task type may route to a different swarm profile

Precedence: Interview responses override auto-detected codebase context. Conflicts noted in Plan Cards.

Flywheel: interviewContext is excluded from recipe fingerprint hash. The fingerprint captures strategy (template + patterns + tier), not user scope answers.

Agent Cards (transparency layer)

Three fixed-format card types rendered at different phases. Templates are exact — do not invent new formats.

Plan Cards — rendered at end of Phase 1 (step 5)

After team plan is complete, before Phase 2 prompt writing. Use this exact table format:

## Team: {N} Opus Agents ({Parallel|Sequential})

| # | Agent | Scope | Excludes | Output |
|---|-------|-------|----------|--------|
| 1 | {role} | {scope} | {excludes or "-"} | {output path} |
| 2 | {role} | {scope} | {excludes or "-"} | {output path} |

Interview context applied: {summary of influence, including override conflicts, or "No interview (high-quality prompt)", or "Interview: skipped by user"}

Rules:

• Render before any agent is launched
• If interview ran, show which constraints came from interview vs auto-detected
• If user requests agent adjustments at confirmation gate, re-render Plan Cards with updated team
• Single-agent runs: table renders with one row (valid)

Status Line — rendered during Phase 3 polling

Compact one-line status with each poll cycle:

Agents: ✅ 2/4  ⏳ 1/4  🔄 1/4 (retry 1)

Emoji mapping: ✅ = completed, ⏳ = in-progress, 🔄 = retrying

Rules:

• Replace verbose poll output with this compact format
• Platform-dependent: TeamCreate uses TaskList status; tmux uses best-effort pane parsing; sequential is trivial
• Show retry count for retrying agents
• Each poll cycle MAY consult node scripts/run-supervisor.js --advise --run-id {runId} --json and fold its verdict into the Status Line. On stalled, follow the current Option's stall runbook; on failing-evals, begin drafting Phase-4 delta prompts early. The supervisor is advisory and read-only.

Result Cards — rendered at start of Phase 4

After reading all agent outputs, before synthesis. Use this exact table format:

## Results

| Agent | Score | Findings | Key Insight |
|-------|-------|----------|-------------|
| {role} | {score}/10 {pass/retry emoji} | {count} findings | {one-sentence top finding} |

Total: {N} findings | {accepted}/{total} accepted | {retry_count} retries

Rules:

• Render before synthesis is written
• "Key Insight" = single most important finding per agent (forces prioritization)
• Retry agents show retry reason in findings column

Token budget (Agent Cards + Dimension Interview)

Phase	Extra tokens	Source
Phase 1 (interview)	100-400	AskUserQuestion calls (0-4 questions) + option generation from config/directory scan
Phase 1 (plan cards)	100-300	Table render (varies by team size)
Phase 3 (status)	~20/poll	Compact status line
Phase 4 (result cards)	150-250	Summary table
Total	~400-1000	0.5-2% of typical 50K-200K run

Phase 2: Repromptverse prompt pack (~2 minutes)

Flywheel bias check (optional, read-only): Same rules as Mode 1 (see "Flywheel bias injection" in Mode 1). When REPROMPTER_FLYWHEEL_BIAS=1, run npm run flywheel:query -- --task-type <team-task-slug> once for the overall team task before per-agent adaptation. If confidence ∈ {"medium", "high"} with sampleCount >= 3, prefer the recommended templateId/patterns as the team-wide starting point; each agent still picks its own role-specific template on top. Announce the bias decision once at the start of Phase 2, not per agent, to keep the output readable. Per-role bias queries are a v3 follow-up once enough role-stamped records exist.

For EACH agent:

1. Pick the best-matching template from references/ (or use base XML structure)
2. Read it, then apply these per-agent adaptations:

• <role>: Specific expert title for THIS agent's domain
• <context>: Add exact file paths (verified with ls), what OTHER agents handle (boundary awareness)
• <requirements>: At least 5 specific, independently verifiable requirements
• <constraints>: Scope boundary with other agents, read-only vs write, file/directory boundaries
• <output_format>: Exact path /tmp/rpt-{taskname}-{agent-domain}.md, required sections
• <success_criteria>: use the v1 structured shape (same as Mode 1) — see references/outcome-schema.md. Include 3–6 <criterion> entries scoped to this agent's artifact (not the whole team's output). Each criterion has id, verification_method (rule | llm_judge | manual), a one-sentence <description>, and an inline <rule> or <judge_prompt> per the method. Bullet-list placeholders in the template files are acceptable scaffolding but the generated per-agent prompt upgrades them to the structured form.

Score each prompt — target 8+/10. If under 8, add more context/constraints.

Write all to /tmp/rpt-agent-prompts-{taskname}.md

Flywheel hook (per-agent): after Phase 3 execution, each agent's artifact at /tmp/rpt-{taskname}-{agent-domain}.md can be recorded separately with scripts/outcome-record.js --role <agent-name> (one record per agent, mode="repromptverse", and --role set to the teammate's name so the flywheel bridge uses it as the domain when building the recipe fingerprint). Score each record with scripts/evaluate-outcome.js. Without --role, all agents on the same task_type collapse into the same recipe bucket and the strategy learner can't tell which roles consistently win vs struggle — so always pass it for Repromptverse records.

Reprompt quality scorecard (mandatory)

After writing all agent prompts, show the before/after comparison so the user sees the improvement:

## Reprompt Quality

| Metric | Raw prompt | After reprompt | Change |
|--------|-----------|----------------|--------|
| Overall | {raw}/10 | {after}/10 | +{pct}% |
| Per-agent avg | - | {avg}/10 | - |
| Agents | - | {N} | - |

Raw prompt scored {raw}/10. After reprompting, each agent prompt scores {min}-{max}/10 (avg {avg}/10).

Rules:

• Render after Phase 2 prompt generation, before Phase 3 execution
• Shows the user exactly how much reprompter improved their input
• If any agent prompt scores < 8, note which ones and what was added to fix them

Phase 3: Execute

Phase 3 has platform-specific execution methods. The reprompted prompts from Phase 2 work with any method — you just need to pick which one to run. In most runs you should not ask the user; auto-pick below and announce the decision so they can redirect if they want.

Status Line (all platforms): During polling, show compact agent status with each cycle. See Agent Cards section for format.

Runtime auto-pick (default behaviour — do this first)

If the user explicitly named an option in their request (e.g. "use tmux", "run it sequentially", "via sessions_spawn"), honour that and skip the detection. Otherwise run the decision tree below top-to-bottom and use the first option whose capability is available.

Order	Capability check	If true, use
1	spawn_subagent is present and at least two of run_command, todo_write, ask_user_question are in the current toolset (unambiguous Grok 4.3+ signature).	Option F — Grok CLI native parallel (F1: spawn_subagent with fork_context=true, persona, capability_mode; F2: shell-level grok -p "..." --yolo --sandbox workspace & then wait). Full contract and gotchas in references/runtime/grok-cli-runtime.md.
2	delegate_task is present and at least two of terminal, process, read_file, write_file, patch, search_files, todo, skills_list, or skill_view are in the current toolset (Hermes Agent signature).	Option G — Hermes Agent native parallel (G1: delegate_task batch; G2: shell-level hermes -z / hermes chat -q then wait; G3: Kanban only for durable workflows). Full contract and gotchas in references/runtime/hermes-agent-runtime.md.
3	All four of TeamCreate, Agent, SendMessage, and TeamDelete are listed in your current toolset. (Gating on TeamCreate alone is not enough — Option B's spawn/shutdown path needs the whole set; without it the run fails mid-execution rather than falling through to another option.)	Option B — native Claude Code teams; teammates can message each other; no tmux init or send-keys timing risk
4	A tool named Workflow is present in the current toolset (Claude dynamic Workflow runtime) — JS-scripted background orchestration with agent()/parallel()/pipeline() and schema-validated returns. Sits below Option B because the Workflow tool has no mid-run cross-agent messaging.	Option H — Claude dynamic Workflow tool; deterministic background fan-out via pipeline/parallel with schema-return handoffs and resumable runs; no mid-run cross-agent messaging. Full contract in references/runtime/claude-workflow-runtime.md.
5	sessions_spawn tool is listed in your current toolset	Option C — OpenClaw
6	bash -c 'command -v tmux && { v=$(claude --version 2>/dev/null | awk "{print \$1}"); [[ "$v" =~ ^(2\.[1-9]|[3-9]) ]]; }' exits 0. (Binary presence alone is insufficient — Option A needs claude ≥ 2.1 so CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is honoured; older CLIs accept the env var but don't enable team mode.)	Option A — tmux + child claude --model opus, visible panes
7	Running inside Codex (parallel sessions available)	Option D
8	None of the above	Option E — sequential fallback (works with any LLM)

After picking, announce the selected option in one short line before starting Phase 3 work, so the user can redirect. Use this shape with the actual option and runtime you selected:

Auto-picked Option {letter} ({runtime}) — {short detection reason}. Override by saying "use Option B", "use Option H (Workflow)", "use Option A (tmux)", "use Option D", "use Option G (Hermes)", or "use Option E" (sequential).

Why F is first for Grok: when an unambiguous Grok signature is detected (spawn_subagent + at least two of the supporting tools), Repromptverse must use Grok-native execution (Option F) to honour the "full Grok runtime support" claim. This check is intentionally strict to prevent false positives on other runtimes. Option B (Claude native teams with cross-agent SendMessage) is preferred on Claude Code surfaces because it offers richer inter-agent messaging than Grok subagents currently provide. The rest of the priority order is unchanged.

Why G is next for Hermes: delegate_task is a Hermes-specific fork/join primitive. When that tool appears with Hermes' file, terminal, skills, or todo tools, Repromptverse should use the native Hermes path instead of falling through to OpenClaw, tmux, Codex, or sequential mode. Hermes workers receive fresh context, so the parent must pass the full per-agent prompt and artifact path in each task's context.

Why H sits just below B on Claude surfaces: both are Claude-native, but the dynamic Workflow tool has no mid-run cross-agent messaging — workers cannot talk; data flows only through pipeline()/parallel() return values. So Option B stays the default when teammates must negotiate during the run (review/audit teams), and Option H wins when you want deterministic background fan-out, schema-validated return handoffs, and resumable runs (the script's agent() prefix is cached on resumeFromRunId). Full contract in references/runtime/claude-workflow-runtime.md.

Tool-schema guard (all options)

Before invoking any tool named in Options A–H, verify it appears in your current toolset and that the call signature matches the schema loaded for the current runtime. Modern CLI runtimes reject calls against an unknown tool or a non-matching signature instead of inferring intent. If a named tool is unfamiliar, halt and report back rather than substituting a similar-looking one.

Known pitfalls captured from 4.6 → 4.7 drift in this skill:

• Task → Agent. The legacy spawn tool was named Task and took subagent_type as a keyword argument. It has been split into Agent(...) for spawn and TaskCreate / TaskUpdate / TaskList for todos. Any example still calling the old spawn name is broken under 4.7.
• SendMessage signature. Current shape is SendMessage(to=<name-or-"*">, message=<str-or-obj>). Legacy type= and recipient= kwargs do not exist on the current tool.
• Broadcast restriction. SendMessage(to="*", ...) accepts plain strings only. Structured payloads such as {"type": "shutdown_request"} must be sent per-agent by name; the runtime rejects structured broadcasts.
• TeamDelete ordering. TeamDelete() fails if any teammate is still active. Shutdown is async; in-process teammates need a turn yield to approve each shutdown_request before cleanup succeeds.
• TeamCreate precedence. Agent(team_name=...) errors if that team was not created first. Always call TeamCreate before any Agent with a team_name argument.

Canonical signatures Option B depends on. These are reference documentation, not a schema enforced by the validator. npm run validate:tool-refs is a blocklist — it catches known-bad shapes from this repo's history (obsolete tool names, reordered broadcast calls, hardcoded model pins) but does not positively verify that every call here matches its schema. If you change a signature below, update the linter's check set in scripts/validate-tool-refs.js in the same PR (and also any Option B flow that relies on the old shape).

TeamCreate(team_name=<string>, description=<string>)

TaskCreate(subject=<string>, description=<string>)

Agent(
  description=<string>,           # required
  prompt=<string>,                 # required
  subagent_type=<string>,          # optional, e.g. "general-purpose"
  team_name=<string>,              # optional — requires prior TeamCreate
  name=<string>,                   # optional — used as SendMessage target
  model=<string>,                  # optional — "opus" / "sonnet" / "haiku"
  run_in_background=<bool>,        # optional — default false
)

SendMessage(to=<name-or-"*">, message=<str-or-obj>)

TaskList()  # used during polling; returns current task statuses

TeamDelete()

Never hardcode a specific model version string of the form claude-<family>-<major>-<minor> — use the bare alias (opus, sonnet, haiku) so the CLI resolves to the current latest automatically. The linter also enforces this.

Option A: tmux (Claude Code)

# 1. Start Claude Code with Agent Teams
tmux new-session -d -s {session} "cd /path/to/workdir && CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 claude --model opus"
# placeholders:
# - {session}: unique tmux session name (example: rpt-auth-audit)
# - /path/to/workdir: absolute repository path for the target project (example: /tmp/reprompter-check)

# 2. Wait for startup
sleep 12

# 3. Send prompt — use -l (literal), Enter SEPARATE
# Include POLLING RULES to prevent lead TaskList loop bug
tmux send-keys -t {session} -l 'Create an agent team with N teammates. Use model opus for all tasks.

POLLING RULES:
- After sending tasks, poll TaskList at most 10 times
- If all tasks show "done" status, stop polling immediately
- After 3 consecutive TaskList calls showing the same status, stop polling regardless
- Once you stop polling: read the output files, then write synthesis
- Do not call TaskList more than 20 times total under any circumstances

Teammate 1 (ROLE): TASK. Write output to /tmp/rpt-{taskname}-{domain}.md. ... After all complete, synthesize into /tmp/rpt-{taskname}-final.md'
sleep 0.5
tmux send-keys -t {session} Enter

# 4. Monitor (poll every 15-30s) — show Status Line: Agents: ✅ N/T ⏳ N/T 🔄 N/T
tmux capture-pane -t {session} -p -S -100

# 5. Verify outputs
ls -la /tmp/rpt-{taskname}-*.md

# 6. Cleanup
tmux kill-session -t {session}

Critical tmux rules

⚠️ WARNING: Default teammate model is HAIKU unless explicitly overridden. Always set --model opus in both CLI launch command and team prompt.

Rule	Why
Always send-keys -l (literal flag)	Without it, special chars break
Enter sent SEPARATELY	Combined fails for multiline
sleep 0.5 between text and Enter	Buffer processing time
sleep 12 after session start	Claude Code init time
--model opus in CLI AND prompt	Default teammate = HAIKU
Each agent writes own file	Prevents file conflicts
Unique taskname per run	Prevents collisions between concurrent sessions

Phase 4: Evaluate + retry

Before deciding retries, MAY consult node scripts/run-supervisor.js --advise --run-id {runId} --json; use its advisory verdict as context, not as actuation.

1. Read each agent's report

2. Score against success criteria from Phase 2:

   • 8+/10 → ACCEPT
   • 4-6/10 → RETRY with delta prompt (tell them what's missing)
   • < 4/10 → RETRY with full rewrite

   Accept checklist (use alongside score — all must pass):

   • All required output sections present
   • Requirements from Phase 2 independently verifiable
   • No hallucinated file paths or line numbers
   • Scope boundaries respected (no overlap with other agents)

3. Max 2 retries (3 total attempts)

4. Show Result Cards — render summary table before synthesis (see Agent Cards section for format)

5. Deliver final report to user

Delta prompt pattern:

Previous attempt scored 5/10.
✅ Good: Sections 1-3 complete
❌ Missing: Section 4 empty, line references wrong
This retry: Focus on gaps. Verify all line numbers.

Expected cost & time

Team size	Time	Cost
2 agents	~5-8 min	~$1-2
3 agents	~8-12 min	~$2-3
4 agents	~10-15 min	~$2-4

Estimates cover Phase 3 (execution) only. Add ~3 minutes for Phases 1-2 and ~5-8 minutes per retry. Each agent uses ~25-70% of their 200K token context window.

Option B: TeamCreate (Claude Code native)

When using Claude Code with TeamCreate/SendMessage tools (native agent teams, no tmux needed):

# 1. Create team
TeamCreate(team_name="rpt-{taskname}", description="Repromptverse: {task summary}")

# 2. Create tasks (one per agent)
TaskCreate(subject="Agent 1 task", description="Full reprompted prompt from Phase 2")
TaskCreate(subject="Agent 2 task", description="Full reprompted prompt from Phase 2")

# 3. Spawn teammates with the Agent tool (specify model=opus)
#    Note: In Claude Code ≥2.1, the tool is `Agent`. The old `Task` name referred
#    to the same spawn primitive but no longer exists as a callable tool. Using
#    `Task(...)` here causes the model to either fail the call or skip the spawn.
Agent(description="Agent 1 on rpt-{taskname}", subagent_type="general-purpose",
      team_name="rpt-{taskname}", name="agent-1", model="opus",
      prompt="You are {role} on the rpt-{taskname} team. Your task is Task #1. [full prompt]",
      run_in_background=true)
Agent(description="Agent 2 on rpt-{taskname}", subagent_type="general-purpose",
      team_name="rpt-{taskname}", name="agent-2", model="opus",
      prompt="You are {role} on the rpt-{taskname} team. Your task is Task #2. [full prompt]",
      run_in_background=true)

# 4. Wait for teammates to complete — show Status Line per poll cycle
# Status Line: Agents: ✅ N/T ⏳ N/T 🔄 N/T (derived from TaskList status)
# 5. Compile synthesis from teammate reports
# 6. Shutdown teammates and delete team
#    Two hard rules on the current runtime (verified on Claude Code 2.1+):
#    - SendMessage(to="*") ONLY accepts plain-string messages. Structured
#      payloads like {"type": "shutdown_request"} are rejected on broadcast,
#      so shutdown is sent per-agent by name.
#    - TeamDelete() errors if any teammate is still active. shutdown is
#      asynchronous (each teammate needs a turn to approve the request and
#      terminate), so wait for each agent to acknowledge before calling it.
#      Retry TeamDelete with a small backoff if needed.
SendMessage(to="agent-1", message={"type": "shutdown_request"})
SendMessage(to="agent-2", message={"type": "shutdown_request"})
# ... one SendMessage per spawned teammate
# (wait for each shutdown_response — in-process teammates need a turn yield)
TeamDelete()

Advantages over tmux: Teammates can message each other (cross-agent flags), shared TaskList for progress tracking, no tmux/terminal dependency, built-in idle/shutdown protocol.

When to use TeamCreate vs tmux: Use TeamCreate when agents need to communicate (review teams, audit teams). Use tmux when agents are fully independent and you want visible terminal panes.

Option C: sessions_spawn (OpenClaw only)

When tmux/Claude Code is unavailable but running inside OpenClaw:

sessions_spawn(task: "<per-agent prompt>", model: "opus", label: "rpt-{role}")

Note: sessions_spawn is an OpenClaw-specific tool. Not available in standalone Claude Code.

Option D: Codex CLI

Codex CLI 0.121.0+ offers two valid patterns for Repromptverse fan-out. Pick based on whether orchestration happens inside or outside the Codex session.

Pattern	When to use	Mechanism
D1: Native subagents	In-session orchestration, shared context, single synthesis, per-agent TOML role definitions	[agents] config + prompt-driven spawn
D2: Shell-level codex exec	External orchestration, per-agent model/profile, structured stdout/stderr logs, total isolation	codex exec --ephemeral --sandbox <mode> ... & + wait
Neither — cross-agent messaging required mid-run	Agents must talk while running	Use Option B (TeamCreate in Claude Code) — Codex has no cross-agent messaging primitive

See references/runtime/codex-runtime.md for the full runtime contract (invocation, artifacts, retries, known gotchas).

D1 — Native subagents (Codex 0.121.0+; multi_agent feature flag stabilized in 0.115.0 on 2026-03-16)

Enable in ~/.codex/config.toml:

[features]
multi_agent = true

[agents]
max_threads = 6       # concurrent workers (default)
max_depth = 1         # no sub-subagents by default
job_max_runtime_seconds = 1800

Define each repromptverse role once as ~/.codex/agents/<name>.toml:

name = "rpt_audit_explorer"
description = "Read-only exploration for Repromptverse audit fan-out."
model = "gpt-5.4"
model_reasoning_effort = "high"
sandbox_mode = "read-only"
developer_instructions = """
You are one of N parallel audit workers. Write your findings to the
artifact path specified by the orchestrator. Cite file:line for every
claim. Do not speculate. Finish by going idle; the orchestrator reads
the artifact file, not a tool call.
"""

Note: report_agent_job_result is a Codex tool required only by spawn_agents_on_csv batch workers, not by ordinary prompt-spawned subagents. Do not add it to the normal D1 developer_instructions above — the tool is not registered for standard subagent roles.

Subagents are prompt-driven in Codex (not flag-driven). The orchestrator prompt fans out in natural language:

Spawn one rpt_audit_explorer subagent per audit dimension
(methodology, code, stats, narrative, attack-surface, claims).
Each subagent writes to /tmp/rpt-{taskname}-{dimension}.md.
After all six complete, read their artifacts and synthesize
the final report to /tmp/rpt-{taskname}-final.md.

The [agents] max_threads cap is enforced natively — no FIFO semaphore needed. Note: normal spawn_agent calls past the cap fail with an AgentLimitReached error rather than queueing, so keep the orchestrator's fan-out size ≤ max_threads. Known gotchas: issue #14866 (stuck "awaiting instruction", closed with linked fix) and issue #15177 (still open: model override metadata may leak back to parent model — prefer the default role when override fidelity matters).

D2 — Shell-level codex exec (portable shell-level path, any Codex version with codex exec + --ephemeral)

Shell-level parallelism works on any POSIX shell. codex exec is one-shot, so backgrounding each agent and waiting is the portable pattern:

# 0. Materialize per-agent prompt files (Phase 2 split).
#    Convention: /tmp/rpt-{taskname}-{agent}.prompt.md
ls /tmp/rpt-{taskname}-*.prompt.md

# 1. Launch each agent in the background. Workers must write their
#    artifact to /tmp/rpt-{taskname}-{agent}.md, so they need write
#    access to /tmp. --sandbox workspace-write permits this. In
#    `codex exec`, --full-auto is an alias for the same sandbox and
#    approval stays at `never` either way (verified in codex 0.121.0
#    source: exec/src/cli.rs + exec/src/lib.rs). Pick whichever flag
#    reads cleaner in your scripts.
#    Switch to --sandbox read-only ONLY if your workers are pure
#    analysis that captures findings via --output-last-message instead
#    of writing the .md artifact themselves (rename the .log to .md
#    after `wait`).
#    `--ephemeral` skips on-disk session state; recommended for
#    isolated parallel runs (historical reference: closed issue #11435).
MODEL="gpt-5.4"
for agent in planner critic synthesizer; do
  codex exec \
    --model "$MODEL" \
    --ephemeral \
    --sandbox workspace-write \
    --output-last-message "/tmp/rpt-{taskname}-${agent}.log" \
    "$(cat /tmp/rpt-{taskname}-${agent}.prompt.md)" \
    > "/tmp/rpt-{taskname}-${agent}.stdout" 2>&1 &
done

# 2. Wait for all background sessions.
wait

# 3. Verify each agent wrote its artifact (exclude .prompt.md inputs).
ls /tmp/rpt-{taskname}-*.md 2>/dev/null | grep -v '\.prompt\.md$'

# 4. Run Phase 4 evaluator loop.

Status Line during execution: Codex CLI has no built-in TaskList. Derive status from artifact presence — crucially, exclude the .prompt.md input files or the counter will report "done" before any agent writes output:

# Zero-match-safe, POSIX-compatible loop. Does not abort under
# `set -euo pipefail` when no artifacts exist yet or only .prompt.md
# inputs are present, and runs in dash (/bin/sh) as well as bash/zsh.
done=0
for f in /tmp/rpt-{taskname}-*.md; do
  [ -e "$f" ] || continue             # glob returned literal (no matches)
  case "$f" in *.prompt.md) continue ;; esac
  done=$((done + 1))
done
total=3
echo "Agents: ✅ $done/$total  ⏳ $((total-done))/$total"

Retries: Re-run codex exec for the failing agent with the delta prompt (Phase 4). Do NOT re-run the whole fleet.

Concurrency cap (D2): Default to 4 or the CPU count, whichever is lower. On Linux use nproc; on macOS use sysctl -n hw.ncpu. More than 4 concurrent Codex sessions against the same account can hit rate limits.

If wait hangs (D2): one agent stalled. Inspect /tmp/rpt-{taskname}-*.stdout, kill that PID, retry just that agent.

Single-session fallback: if the environment doesn't allow backgrounding or subagents (sandboxed shells, notebook runners), use Option E — the reprompted prompts are plain text and run identically in one session, just slower.

When to pick D1 vs D2:

Situation	Pick
Agents share context; one summary output expected	D1
Need per-agent log files or model/profile overrides	D2
Orchestrating from CI or shell script outside Codex	D2
You want fresh context per worker without re-ingesting the codebase	D1
Codex < 0.121.0 or multi_agent feature disabled	D2
Cross-agent messaging required mid-run	Neither — use Option B (TeamCreate in Claude Code)

Option H: Claude dynamic Workflow tool

When a tool named Workflow is present in the current toolset (Claude dynamic Workflow runtime), Phase 3 can compile the Phase-2 per-agent prompts into a single runnable .workflow.js and run it via Workflow({ scriptPath, args }). Picked at Order 4 — below Option B, because the Workflow tool has no mid-run cross-agent messaging; data flows only through pipeline()/parallel() return values.

Because Option H has no mid-run messaging seam, node scripts/run-supervisor.js --advise --run-id {runId} --json applies only after the workflow returns.

Each Phase-2 reprompted prompt becomes an agent(prompt, { schema }) call. Three emission patterns:

Pattern	When	Shape
H1: pipeline() default	Sequential dependencies (fetch → transform → deploy); each item independent, no barrier	pipeline(items, stage1, stage2)
H2: parallel() barrier	Independent-domain agents whose results are synthesized/evaluated together (the common Repromptverse shape)	(await parallel(roles.map(r => () => agent(r.prompt, { schema })))).filter(Boolean) then synthesize
H3: budget-aware depth	A +Nk budget directive	As the compiler emits it: a fixed roster + agent-count caps (maxItems: 20, VERIFY_CAP: 24) and a completeness critic gated on !budget.total || budget.remaining() > 30000 — depth dials off near the ceiling, no roster scaling. (A literal while (budget.remaining() > N) { … } loop-until-budget is a valid hand-authored shape, but the compiler does not emit one.)

Hard rules for the emitted script (full contract: references/runtime/claude-workflow-runtime.md): meta is a pure literal with phase() titles matching meta.phases; runId/taskname come from args (never generated in-script — wall-clock and randomness throw and break resume); model is omitted so agents inherit the main-loop model; filter(Boolean) after every parallel()/pipeline(). Schema-validated returns are the single source of truth — the script never reads the /tmp/rpt-*.md files back; the parent writes that compatibility mirror after the run returns (so Status Line / Phase-4 / flywheel keep working). High-risk forbidden surfaces (prod/auth/secret/...) block script emission (blocked: true, script: null); there is no in-tool override, so rescope the task to proceed.

Ultracode: when ultracode is on, the emitted script defaults to the thorough body — adversarial / perspective-diverse verify (3 distinct lenses: correctness/completeness/risk, a finding kept only on ≥2/3 non-refutation) plus a completeness critic. Agent-count caps keep it under the Workflow 1000-agent lifetime cap: maxItems: 20 findings per role and VERIFY_CAP = 24 (≤72 verify agents), with truncation logged. Budget scaling (H3, as shipped): the critic is gated on !budget.total || budget.remaining() > 30000 so the extra pass dials off near the token ceiling — the roster itself is not budget-scaled. A budget directive (+Nk / budget: only — clamped to 100M; a bare Nk tokens is ambiguous with exfiltration and is not a cue) rides the command as args.budget, while the script prefers the live budget global. Lean off-ramp (REPROMPTER_ULTRACODE=0 / --no-ultracode) keeps trivial reprompts cheap. Compiler: scripts/workflow-command.js.

Option G: Hermes Agent

Hermes Agent supports three valid Repromptverse execution patterns. Pick G1 by default for normal interactive runs.

Pattern	When to use	Mechanism
G1: delegate_task batch	In-session parallel Repromptverse; parent needs final summaries before synthesis	delegate_task(tasks=[...]) with one task per role
G2: Shell-level Hermes	External orchestration, per-worker logs, CI/headless scripts	Write prompt files with single-quoted heredocs, then run hermes -z "$prompt_text" or hermes chat -q "$prompt_text" in the background, then wait
G3: Kanban	Durable, restart-surviving, multi-profile, human-in-loop workflows	kanban_create + worker agents pulling/listing/completing cards

See references/runtime/hermes-agent-runtime.md for the full runtime contract (invocation, artifacts, retries, /goal, Kanban, and known gotchas).

G1 — Native delegation via delegate_task (recommended)

Hermes child agents start with fresh conversation context. The parent must pass all relevant context in each task's goal and context; do not assume the child can see the parent's full transcript.

delegate_task(tasks=[
  {
    "goal": "Repromptverse researcher worker for {taskname}",
    "context": "You are the researcher agent on rpt-{taskname}.\n\n[PASTE THE FULL PHASE-2 REPROMPTED XML PROMPT HERE]\n\nWrite your complete findings to /tmp/rpt-{taskname}-researcher.md. Use file:line citations. Do not speculate.",
    "toolsets": ["terminal", "file", "web", "skills"]
  },
  {
    "goal": "Repromptverse reviewer worker for {taskname}",
    "context": "You are the reviewer agent on rpt-{taskname}.\n\n[PASTE THE FULL PHASE-2 REPROMPTED XML PROMPT HERE]\n\nWrite your complete findings to /tmp/rpt-{taskname}-reviewer.md. Use file:line citations. Do not speculate.",
    "toolsets": ["terminal", "file", "web", "skills"]
  }
])

Default Hermes concurrency is bounded by delegation.max_concurrent_children (documented default 3). If the planned team is larger than the limit, split into batches or use G2/G3. Oversized batches return an error rather than silently queueing.

Status Line during G1: track the parent plan with Hermes todo, then combine returned child summaries with artifact checks (ls /tmp/rpt-{taskname}-*.md, excluding .prompt.md and .stdout) before Phase 4 synthesis.

G2 — Shell-level hermes -z / hermes chat -q

Use this when the parent is orchestrating from a shell script or needs separate stdout/stderr logs:

TASKNAME="audit-2026-05"
AGENTS=(researcher implementer reviewer)

for role in "${AGENTS[@]}"; do
  prompt_file="/tmp/rpt-${TASKNAME}-${role}.prompt.md"
  {
    printf 'You are the %s agent on the rpt-%s team.\n\n' "$role" "$TASKNAME"
    cat <<'REPROMPTER_PROMPT'
[PASTE THE FULL PHASE-2 REPROMPTED XML PROMPT FOR THIS ROLE]
REPROMPTER_PROMPT
    printf '\n\nWrite your complete findings to the exact file /tmp/rpt-%s-%s.md.\n' "$TASKNAME" "$role"
    printf 'Use file:line citations. Do not speculate.\n'
  } > "$prompt_file"

  prompt_text="$(cat "$prompt_file")"
  hermes -z "$prompt_text" \
    --toolsets terminal,file,web,skills \
    > "/tmp/rpt-${TASKNAME}-${role}.stdout" 2>&1 &
done

wait

Use hermes chat -q "$prompt_text" instead of hermes -z "$prompt_text" when you want the normal chat one-shot path rather than pure final text. Workers must still write /tmp/rpt-{taskname}-{role}.md.

G3 — Hermes Kanban (explicit opt-in only)

Do not auto-select Kanban for normal Repromptverse. Use it only when the user wants durable work that survives restarts, spans multiple Hermes profiles, needs human-in-loop checkpoints, or should be visible as a board. Kanban agents use the kanban_* toolset directly: task workers normally use lifecycle tools such as kanban_show, kanban_complete, kanban_block, kanban_heartbeat, and kanban_comment, while profiles that explicitly enable the Kanban toolset and are not scoped to one dispatcher task can also use orchestration tools such as kanban_list, kanban_create, kanban_link, and kanban_unblock.

Known Hermes gotchas:

• delegate_task is synchronous from the parent's perspective; the parent waits for child summaries before continuing.
• Child summaries are the only child state automatically returned to the parent. Intermediate tool outputs do not enter the parent context unless the child writes artifacts or summarizes them.
• Normal child agents cannot themselves use delegate_task, clarify, memory, send_message, or execute_code unless Hermes is configured for orchestrator/nested roles.
• Cross-agent messaging during a running G1 batch is not the default coordination surface. Use artifact files and parent synthesis, or use G3 Kanban for durable coordination.

Option E: Sequential (any LLM)

No parallel execution tools available? Run each agent's reprompted prompt one at a time in the same session. Works with any LLM (Claude, GPT, Gemini, Codex, etc.). Slower but fully platform-agnostic.

The reprompted prompts from Phase 2 are pure text. They work regardless of execution method.

---

Lane: Reverse Reprompter

TL;DR

Great output in → optimal prompt out. Extract the DNA that produced excellence.

Phase 1: EXTRACT — structural analysis of the exemplar (~5s)
Phase 2: ANALYZE — classify task type, domain, tone, quality (~5s)
Phase 3: SYNTHESIZE — generate full XML prompt matching the exemplar's pattern (~10s)
Phase 4: INJECT — seed flywheel with pre-graded exemplar outcome (optional, ~2s)

Key insight: Users encounter great outputs constantly but can't reproduce the quality. Reverse Reprompter closes that gap by extracting the prompt that would have produced it.

Trigger words

• "reverse reprompt", "reverse reprompter"
• "reprompt from example", "reprompt from this"
• "learn from this"
• "extract prompt from"
• "reverse engineer prompt"
• "prompt from output", "prompt dna", "prompt genome"

Process

1. Receive exemplar — user provides text (paste, file path, or points to an existing output)
2. Input guard — must be substantial output (>50 chars, has structure). Reject raw prompts (use Single mode instead), empty text, or single-word inputs
3. Quick interview (max 2 questions via AskUserQuestion):
   • "What do you love about this output?" (with options: Structure / Depth / Tone / Coverage / Everything)
   • "What context produced it?" (with options: Code review / Architecture / API work / Research / Other) — skip if task type is detectable with high confidence
4. Analyze — extract structure, classify type, detect domain and tone
5. Extract criteria — derive a v1 <success_criteria schema_version="1"> block from the exemplar's observable features (see "Criteria extraction from exemplars" below). The exemplar is the target, so the criteria encode "future outputs should match this exemplar's distinguishing properties."
6. Generate — produce full XML prompt using reverse template + best-fit task template; embed the extracted <success_criteria> block.
7. Score — show quality dimensions of the generated prompt
8. Flywheel injection — offer to save as pre-graded exemplar outcome

Generate after analysis

After analysis completes, immediately:

1. Extract <success_criteria> from the exemplar (see "Criteria extraction from exemplars" below — 3–6 criteria anchored to observable features of the exemplar)
2. Generate the full reverse-engineered prompt, embedding the extracted <success_criteria> block
3. Show the Extraction Card (see below)
4. Show the generated prompt in XML format
5. Show quality score
6. Ask: "Save to flywheel? / Execute with this prompt? / Copy?"

❌ WRONG: Analyze exemplar → stop
❌ WRONG: Analyze exemplar → generate prompt → stop (skipping criteria extraction)
✅ RIGHT: Analyze exemplar → extract criteria → generate prompt with criteria → show Extraction Card → show score → offer actions

Extraction Card (transparency layer)