reprompter

name

reprompter

description

Transform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.

RePrompter v7.0

Your prompt sucks. Let's fix that. Single prompts or full agent teams — one skill, two modes.

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Two Modes

Mode	Trigger	What happens
Single	"reprompt this", "clean up this prompt"	Interview → structured prompt → score
Repromptception	"reprompter teams", "repromptception", "run with quality", "smart run", "smart agents"	Plan team → reprompt each agent → tmux Agent Teams → evaluate → retry

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

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Mode 1: 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)
5. Generate + Score — apply template, show before/after quality metrics

⚠️ MUST 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 Repromptception 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 (MANDATORY 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

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)

Quick Mode

Enable when ALL true:

• < 20 words (excluding code blocks)
• Exactly 1 action verb from: add, fix, remove, rename, move, delete, update, create, implement, write, change, configure, test, run
• Single target (one file, component, or identifier)
• No conjunctions (and, or, plus, also)
• No vague modifiers (better, improved, some, maybe, kind of)

Force interview if ANY present: compound tasks ("and", "plus"), state management ("track", "sync"), vague modifiers ("better", "improved"), integration work ("connect", "combine", "sync"), broad scope nouns after any action verb, ambiguous pronouns ("it", "this", "that" without clear referent).

Task Types & Templates

Detect task type from input. Each type has a dedicated template in docs/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
Multi-Agent	swarm-template.md	Multi-agent coordination
Team Brief	team-brief-template.md	Team orchestration brief

Priority (most specific wins): api > security > ui > testing > bugfix > refactor > content > docs > research > feature. For multi-agent tasks, use swarm-template for the team brief and the type-specific template for each agent's sub-prompt.

How it works: Read the matching template from docs/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 docs/references/{type}-template.md following the XML structure below, then add it to the table above.

Base XML Structure

All templates follow this core structure (8 required tags). 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>
- {What NOT to do}
- {Boundaries and limits}
</constraints>

<output_format>{Expected format, structure, length}</output_format>

<success_criteria>
- {Testable condition 1}
- {Measurable outcome 2}
</success_criteria>

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

---

Mode 2: Repromptception (Agent Teams)

TL;DR

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

Phase 1: Score raw prompt, plan team, define roles (YOU do this, ~30s)
Phase 2: Write XML-structured prompt per agent (YOU do this, ~2min)
Phase 3: Launch tmux Agent Teams (AUTOMATED)
Phase 4: Read results, score, retry if needed (YOU do this)

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

Phase 1: Team Plan (~30 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. Pick mode: parallel (independent agents) or sequential (pipeline with dependencies)
3. Define team: 2-5 agents max, each owns ONE domain, no overlap
4. Write team brief to /tmp/rpt-brief-{taskname}.md (use unique tasknames to avoid collisions between concurrent runs)

Phase 2: Repromptception (~2 minutes)

For EACH agent:

1. Pick the best-matching template from docs/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>: Minimum N findings, file:line references, no hallucinated paths

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

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

Phase 3: Execute (tmux Agent Teams)

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

POLLING RULES — YOU MUST FOLLOW THESE:
- After sending tasks, poll TaskList at most 10 times
- If ALL tasks show "done" status, IMMEDIATELY stop polling
- 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)
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

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. 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.

Fallback: 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.

No tmux or OpenClaw? Run agents sequentially: execute each agent's prompt one at a time in the same Claude Code session. Slower but works everywhere.

---

Quality Scoring

Always show before/after metrics:

Dimension	Weight	Criteria
Clarity	20%	Task unambiguous?
Specificity	20%	Requirements concrete?
Structure	15%	Proper sections, logical flow?
Constraints	15%	Boundaries defined?
Verifiability	15%	Success measurable?
Decomposition	15%	Work split cleanly? (Score 10 if task is correctly atomic)

| Dimension | Before | After | Change |
|-----------|--------|-------|--------|
| Clarity | 3/10 | 9/10 | +200% |
| Specificity | 2/10 | 8/10 | +300% |
| Structure | 1/10 | 10/10 | +900% |
| Constraints | 0/10 | 7/10 | new |
| Verifiability | 2/10 | 8/10 | +300% |
| Decomposition | 0/10 | 8/10 | new |
| **Overall** | **1.45/10** | **8.35/10** | **+476%** |

Bias note: Scores are self-assessed. Treat as directional indicators, not absolutes.

---

Closed-Loop Quality (v6.0+)

For both modes, RePrompter supports post-execution evaluation:

1. IMPROVE — Score raw → generate structured prompt
2. EXECUTE — Repromptception mode only: route to agent(s), collect output. Single mode does not execute code/commands; it only generates prompts.
3. EVALUATE — Score output/prompt against success criteria (0-10)
4. RETRY — Thresholds: Single mode retry if score < 7; Repromptception retry if score < 8. Max 2 retries.

---

Advanced Features

Reasoning-Friendly Prompting (Claude 4.x)

Prompts should be less prescriptive about HOW. Focus on WHAT — clear task, requirements, constraints, success criteria. Let the model's own reasoning handle execution strategy.

Example: Instead of "Step 1: read the file, Step 2: extract the function" → "Extract the authentication logic from auth.ts into a reusable middleware. Requirements: ..."

Response Prefilling (API only)

Prefill assistant response start to enforce format:

• { → forces JSON output
• ## Analysis → skips preamble, starts with content
• | Column | → forces table format

Context Engineering

Generated prompts should COMPLEMENT runtime context (CLAUDE.md, skills, MCP tools), not duplicate it. Before generating:

1. Check what context is already loaded (project files, skills, MCP servers)
2. Reference existing context: "Using the project structure from CLAUDE.md..."
3. Add ONLY what's missing — avoid restating what the model already knows

Token Budget

Keep generated prompts under ~2K tokens for single mode, ~1K per agent for Repromptception. Longer prompts waste context window without improving quality. If a prompt exceeds budget, split into phases or move detail into constraints.

Uncertainty Handling

Always include explicit permission for the model to express uncertainty rather than fabricate:

• Add to constraints: "If unsure about any requirement, ask for clarification rather than assuming"
• For research tasks: "Clearly label confidence levels (high/medium/low) for each finding"
• For code tasks: "Flag any assumptions about the codebase with TODO comments"

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Settings (for Repromptception mode)

Note: CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS is an experimental flag that may change in future Claude Code versions. Check Claude Code docs for current status.

In ~/.claude/settings.json:

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  },
  "preferences": {
    "teammateMode": "tmux",
    "model": "opus"
  }
}

Setting	Values	Effect
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS	"1"	Enables agent team spawning
teammateMode	"tmux" / "default"	tmux: each teammate gets a visible split pane. default: teammates run in background
model	"opus" / "sonnet"	Teammates default to Haiku. Always set model: opus explicitly in your prompt — do not rely on runtime defaults.

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Proven Results

Single Prompt (v6.0)

Rough crypto dashboard prompt: 1.6/10 → 9.0/10 (+462%)

Repromptception E2E (v6.1)

3 Opus agents, sequential pipeline (PromptAnalyzer → PromptEngineer → QualityAuditor):

Metric	Value
Original score	2.15/10
After Repromptception	9.15/10 (+326%)
Quality audit	PASS (99.1%)
Weaknesses found → fixed	24/24 (100%)
Cost	$1.39
Time	~8 minutes

Repromptception vs Raw Agent Teams (v7.0)

Same audit task, 4 Opus agents:

Metric	Raw	Repromptception	Delta
CRITICAL findings	7	14	+100%
Total findings	~40	104	+160%
Cost savings identified	$377/mo	$490/mo	+30%
Token bloat found	45K	113K	+151%
Cross-validated findings	0	5	—

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Tips

• More context = fewer questions — mention tech stack, files
• "expand" — if Quick Mode gave too simple a result, re-run with full interview
• "quick" — skip interview for simple tasks
• "no context" — skip auto-detection
• Context is per-project — switching directories = fresh detection

---

Test Scenarios

See TESTING.md for 13 verification scenarios + anti-pattern examples.

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Appendix: Extended XML Tags

Templates may add domain-specific tags beyond the 8 required base tags. Always include all base tags first.

Extended Tag	Used In	Purpose
<symptoms>	bugfix	What the user sees, error messages
<investigation_steps>	bugfix	Systematic debugging steps
<endpoints>	api	Endpoint specifications
<component_spec>	ui	Component props, states, layout
<agents>	swarm	Agent role definitions
<task_decomposition>	swarm	Work split per agent
<coordination>	swarm	Inter-agent handoff rules
<research_questions>	research	Specific questions to answer
<methodology>	research	Research approach and methods
<reasoning>	research	Reasoning notes space (non-sensitive, concise)
<current_state>	refactor	Before state of the code
<target_state>	refactor	Desired after state
<coverage_requirements>	testing	What needs test coverage
<threat_model>	security	Threat landscape and vectors
<structure>	docs	Document organization
<reference>	docs	Source material to reference