harness-master

name	harness-master
description	Audit harness configs, discover gaps, usage signals, apply approved fixes. Use when tuning Claude, Codex, Copilot, Cursor, Gemini, Grok, OpenCode, or Cherry. NOT agents, MCP, or app telemetry.
argument-hint	[discover|audit|usage|apply|install|resume|list] [harness|all] [project|global|both]
model	opus
license	MIT
compatibility	Dry-run audit first. Web access recommended. Python 3.12+ for discovery scripts.
metadata	{"author":"wyattowalsh","version":"1.1.0"}

Harness Master

Audit AI harness configuration quality, usage posture, and ecosystem options, then apply approved config fixes in the same session.

NOT for: creating agents (agent-conventions), building MCP servers (mcp-creator), generic application telemetry (observability-advisor), or generic code review without harness/config focus (review).

Input: harness names, level, or natural language. Modes: Audit, Discover (gap + bounded research), Usage, Apply/Install, Journal. Run scripts/discovery/classify_intent.py --args "$ARGUMENTS" --json when mode is ambiguous.

Dispatch

$ARGUMENTS	Mode	First move
Empty	Intake	Ask for harnesses or all, then ask for project, global, or both
all	Intake	Ask only for level
all <level> / <level> all	Audit	Dry-run all supported harnesses in deterministic order
<harness...>	Intake	Resolve one or more harnesses, then ask only for missing level
<level>	Intake	Resolve level, then ask only for missing harnesses
<harness...> <level> / <level> <harness...>	Audit	Run dry-run review for the selected harnesses and level
apply approved	Apply Approved	Apply the last matching dry-run batch only if the user already approved it in this session
apply <harness...> <level> / apply <level> <harness...>	Apply Approved	Apply only if the same scope was already reviewed and approved in this session; otherwise rerun audit first
install <harness...>	Install Guidance	Show exact npx skills add ... commands only; do not edit configs
discover	Discover full	W0 scans → scouts → ideate → report per references/discovery-pipeline.md
discover audit / audit gaps	Discover W0	Gap report only; no scouts
discover resume / discover list	Journal	Load or list ~/.agents/harness-master/discovery/ sessions
usage <harness|all> [level] [days]	Usage Review	Collect safe aggregate usage/cost/token/tool signals; read-only
NL: missing skills / expand / find best X for harness	Discover (inferred)	Depth: full, focused, candidate, or compare — see discovery-pipeline.md
Natural language: "review/audit/check/tune config"	Audit	Normalize harnesses + level, then run dry-run review
Natural language: "review/analyze usage/cost/tokens/tools for "	Usage Review	Normalize harnesses + level, collect safe metadata only, and report optimization lanes
Natural language: "find/latest/best plugins/extensions/MCPs/skills for "	Discover focused	W0 + category scouts + source_probe.py; read-only
Natural language approval like "approved", "do it", "apply those changes"	Apply Approved	Continue only if the immediately preceding harness-master review matches the scope and is still current
Requests to create agents or MCP servers	Refuse + redirect	Redirect to the correct specialized skill
Unsupported harness names	Refuse + clarify	List supported harnesses and ask the user to choose from that set

Empty-Args Handler

If the user invokes /harness-master with no arguments:

1. Ask which harnesses to review or whether to use all.
2. Ask for project, global, or both.
3. Explain that the first pass is always dry-run only.

Input Normalization

• Supported harnesses: claude-code, claude-desktop, chatgpt, codex, github-copilot-web, github-copilot-cli, cursor, gemini-cli, antigravity, grok-build, opencode, perplexity-desktop, cherry-studio
• Supported levels: project, global, both
• Supported research categories: config, plugin, extension, mcp, skill, all
• Supported usage windows: positive day counts such as 7, 14, 30, or days=14; default to 14 when absent
• Harness aliases:
  • claude, claude-code -> claude-code
  • claude-desktop -> claude-desktop
  • chatgpt, chatgpt-desktop, openai-chatgpt -> chatgpt
  • codex -> codex
  • cursor, cursor-agent, cursor-editor, cursor-desktop, cursor-cli, agent-cli, cursor-cloud, cursor-cloud-agent, cursor-background-agent, cursor-web -> cursor
  • gemini, gemini-cli -> gemini-cli
  • antigravity, google-antigravity -> antigravity
  • github-copilot, copilot, gh-copilot -> expand to github-copilot-web and github-copilot-cli
  • github-copilot-web, copilot-web, copilot-cloud, copilot-coding-agent -> github-copilot-web
  • github-copilot-cli, copilot-cli -> github-copilot-cli
  • grok, grok-build, grok-cli -> grok-build
  • opencode, open-code -> opencode
  • perplexity, perplexity-desktop, perplexity-mac -> perplexity-desktop
  • cherry, cherrystudio, cherry-ai, cherry-studio -> cherry-studio
• Level aliases:
  • project, repo, local -> project
  • global, user -> global
  • both, all-levels -> both
• Split multiple harnesses on commas and whitespace.
• If the user supplies both all and named harnesses, ask which form they want.
• Deterministic all order: claude-code, claude-desktop, chatgpt, codex, github-copilot-web, github-copilot-cli, cursor, gemini-cli, antigravity, grok-build, opencode, perplexity-desktop, cherry-studio
• Unknown tokens are never guessed. Ask one focused clarification question.

Grok Build (grok-build)

• Instruction entry: AGENTS.md -> instructions/grok-global.md -> instructions/global.md
• Policy template: config/grok-config.toml merged into ~/.grok/config.toml via repo Grok platform adapter sync
• Ownership: replace-owned (models, mcp_servers, plugins, compat, telemetry) vs blend-owned (ui, features, session, tools, toolset, subagents, memory)
• Skills: no native Skills CLI adapter; install/sync uses claude-code alias + mirror to ~/.grok/skills; inventory also scans repo .grok/skills (project scope)
• Plannotator: CLI + skills + hooks (no Grok npm plugin). Install: grok plannotator install (repo CLI). Policy: config/grok-plannotator-hooks.json -> ~/.grok/hooks/plannotator.json; home shim maps block -> deny. Curated in docs/src/authoring/skills/*.mdx. Contrast OpenCode @plannotator/opencode@latest.
• Env (not in TOML): GROK_WEB_FETCH, GROK_MEMORY, GROK_SUBAGENTS, GROK_LSP_TOOLS — document in config/grok-env.sh; verify with /grok-delegate preflight
• Cross-harness delegate: /grok-delegate — native headless grok -p / -r / worktrees / leader for Codex/OpenCode task graphs (not config sync)
• Isolated sync: repo stack sync with --platforms grok --targets home (skips other harness home merges; includes Plannotator hook refresh)

Classification Gate

1. Parse $ARGUMENTS into harnesses, level, all, install intent, apply intent, and unresolved tokens.
2. If unresolved tokens remain, ask a single clarification question before continuing.
3. If the request is discover ..., gap/missing/expand intent, or find-best-for-harness NL, run Discover (infer depth via classify_intent.py or discovery-pipeline.md).
4. If the request is usage ..., run Usage Review only.
5. If the request is install ..., run Install Guidance only.
6. If ad-hoc "find skill for X", redirect to find-skills. If create skill, redirect to skill-creator. If open-ended non-harness research, redirect to /research.
7. If the request is apply ... or an approval phrase, run Apply Approved only if the matching dry-run review already exists in this session. Otherwise rerun audit first.
8. If the request includes unsupported harnesses, refuse cleanly and list the supported set.
9. If harnesses are missing (audit/usage), ask only for harnesses or all.
10. If the level is missing (audit/usage), ask only for project, global, or both.
11. Otherwise run Audit in dry-run mode.

Classification Logic

1. Route explicit mode tokens first: discover, usage, install, apply, resume, list.
2. Route gap/missing/expand/find-best NL to Discover depths (not Audit).
3. Route complete harness/level scopes to Audit when config tune/fix intent and no discover signal.
4. Route usage/cost/token NL to Usage Review.
5. Route approvals to Apply Approved only when the immediately preceding dry-run audit matches the requested scope.

Workflow

Mode A: Audit (default, dry-run first)

1. Gate 0 — Discover surfaces

   • Run:

     uv run python scripts/discover_surfaces.py --repo-root . --level <level> --harness <canonical-harness> [--harness ...]
     

   • Parse the JSON output and classify each surface as present, missing, generated, merged, repo-observed, or blind-spot.
   • If the script cannot run, fall back to manual discovery with Glob, Read, and Grep.

2. Gate 1 — Inspect project context before recommending fixes

   • Read the repo's intent and operating model first: README.md, AGENTS.md, harness-facing project files, key manifests, CI/workflow signals, and repo-native orchestration logic when present.
   • In this repository, treat the repo-level harness sync script together with config/tooling-policy.json and config/sync-manifest.json as canonical context for managed/generated/merged harness surfaces.

3. Gate 2 — Refresh latest official guidance

   • Read references/latest-doc-sources.md.
   • Use llms.txt first when available, then first-party docs, then canonical vendor repo docs, then web fallback only if needed.
   • If official docs are unavailable, enter degraded mode and lower confidence.

4. Gate 3 — Audit each selected harness independently

   • Load references/harness-surfaces.md, references/harness-checklists.md, and references/evidence-boundaries.md selectively for the selected harnesses only.
   • For both, compare project/global precedence before recommending edits.
   • Mark generated surfaces explicitly. Prefer fixing the canonical source over directly editing generated output.
   • Treat missing native surfaces as evidence to assess, not automatic failure.

5. Gate 4 — Report dry-run findings

   • Use the structure in references/output-format.md.
   • Every finding must include severity, affected surface, evidence tag, why it matters for this project, and the correct home for the fix.
   • Show concrete patch previews when the correct change is clear.
   • Stop after the dry-run report and wait for explicit user approval.

Mode B: Apply Approved

1. Apply only after an explicit approval gate.
2. Approval is valid only when the immediately preceding harness-master dry-run review in the same session already covered the same harness set and level.
3. If the reviewed scope is stale, missing, ambiguous, or contradicted by new evidence, rerun Audit before editing.
4. Restate the approved batch before making changes.
5. Apply the smallest approved batch only. Do not broaden scope silently.
6. If the approved remediation is an install step, use the exact npx skills add ... guidance from references/install-guidance.md.
7. After edits, rerun surface discovery and targeted dry-run checks for the touched harnesses, then summarize remaining gaps.

Mode C: Discover

Read-only gap expansion and harness-bounded research. Load references/discovery-pipeline.md and references/discovery/coordinator-contract.md.

1. Classify depth (full, focused, candidate, compare, w0only, ideate, journal).
2. Run W0 scripts before any scouts when depth requires gaps.
3. Use source_probe.py and candidate_score.py inside W2/W2b — not as separate user-facing modes.
4. Block apply/install until user confirms; config fixes require Audit + apply approved.

Mode D: Usage Review

Use this mode for usage or natural-language requests to review token cost, quota, tool friction, MCP use, skill fit, plugin fit, context health, session attribution, or harness telemetry posture.

1. Treat the entire mode as read-only. Do not edit files, install packages, create jobs, export raw sessions, print raw prompts, print raw traces, or mutate harness configs.
2. Normalize harnesses, level, and usage window. Default missing window to 14 days.
3. Run surface discovery plus the safe usage probe:

   uv run python scripts/usage_probe.py --repo-root . --harness <canonical-harness> --level <level> --days <days> --json
   

4. Load references/usage-review.md, references/evidence-boundaries.md, and references/output-format.md.
5. Use runtime tools such as token_stats, token_history, token_export, insights_collect, agent_attribution, quota_status, gemini_quota, workspace-summary, and git-smart-status only when they match the requested harness and privacy class.
6. Normalize findings into usage signals and score source coverage, privacy safety, cost/token posture, tool efficiency, MCP hygiene, skill fit, plugin fit, context health, approval safety, and recommendation actionability.
7. Report recommendation lanes: keep, tune-config, tune-skill, tune-mcp, tune-plugin, tune-workflow, instrument, defer, or do-not-change.
8. If the user asks to apply a Usage Review recommendation, block the apply step and require a matching dry-run Audit plus explicit approval first.

Scaling Strategy

Scope	Strategy
small	1 harness and 1 level: inline review with one per-harness report
medium	2-3 harnesses or one both scope: review each harness independently, then add a short synthesis
large	4+ harnesses or all: batch discovery where possible, keep per-harness sections strict, then finish with cross-harness synthesis and ranked cleanup order
Discover (full/focused)	Use Pattern F semantics: explore sources, score candidates, synthesize evidence, and stop before apply
Team-capable Discover	Use Pattern E or nested waves for source-family scouts, with accounting for every dispatched scout
Usage review	Prefer aggregate probes and runtime summaries first; avoid raw transcripts, raw traces, secret files, and message-table reads

Latest-Doc Lookup Policy

Use the most current authoritative guidance available:

1. llms.txt or equivalent official index
2. First-party docs pages for config, instructions, rules, MCP, and permissions
3. Canonical vendor repo docs if first-party product docs are incomplete
4. Web fallback only when the above are unavailable

Never claim latest without evidence from a current source.

Per-Harness Review Contract

• Review each harness independently before writing any synthesis.
• Tag every finding as verified-file, verified-doc, repo-observed, or blind-spot.
• Surface the authoritative, secondary, generated, and merged config surfaces for the selected level.
• For both, report project/global conflicts before recommending changes.
• If a harness-specific native surface is not observable from the current session, mark it as a blind spot instead of inventing behavior.
• Antigravity project-level advice must clearly separate first-party documented global behavior from repo-observed compatibility assumptions.

Install Guidance Contract

• Load references/install-guidance.md only when the user asks how to install harness-master, or when missing skill availability is the actual root cause.
• Default surfaced form:

  npx skills add <source> --skill harness-master -y -g --agent <agent>
  

• Do not recommend install commands for native config problems.
• Do not suggest project-local install by default. Mention python scripts/install_skills.py --local --execute only if the user explicitly asks for project-local installation.

Validation Contract

After changing this skill, run:

python scripts/check.py
uv run python scripts/usage_probe.py --repo-root . --harness opencode --level both --days 14 --json
uv run python scripts/discover_surfaces.py --repo-root . --level both --harness claude-code --harness claude-desktop --harness chatgpt --harness codex --harness github-copilot-web --harness github-copilot-cli --harness cursor --harness gemini-cli --harness antigravity --harness opencode --harness perplexity-desktop --harness cherry-studio

Completion criteria:

• metadata and eval manifests validate
• packaging remains portable
• surface discovery smoke-check accepts every canonical harness
• dry-run/apply behavior still requires approval before edits
• Discover remains read-only and blocks apply until a matching audit is approved
• usage review remains read-only, redacts sensitive sources, and blocks apply until a matching audit is approved

Cross-Harness Synthesis Contract

After all per-harness reviews, synthesize:

• Shared issues across harnesses
• Conflicting conventions or precedence rules
• Duplicated instruction sources or MCP definitions
• Generated-vs-canonical drift patterns
• Highest-leverage cleanup order if the user wants a follow-up apply batch

Output Contract

Every per-harness report must include:

1. Harness
2. Level
3. Files Reviewed
4. Docs Checked
5. Project Context Summary
6. Blind Spots
7. Scorecard
8. Findings
9. Recommended Changes
10. Proposed Patch Preview
11. Confidence

Then add a cross-harness synthesis section when 2+ harnesses were reviewed.

Reference File Index

File	Content	Read When
references/workflow.md	Gate-by-gate audit/apply workflow, precedence rules, degraded mode, and approval gate details	Audit, Apply Approved
references/latest-doc-sources.md	Official llms.txt and docs URLs per harness, plus fallback order	Latest-doc refresh
references/harness-surfaces.md	Project/global surfaces, precedence, install agent names, generated/merged notes	Surface interpretation
references/harness-checklists.md	Per-harness audit checklist and edge cases	Per-harness review
references/evidence-boundaries.md	Evidence tags, blind-spot handling, and contradiction policy	Reporting findings
references/install-guidance.md	Exact npx skills add ... commands, when to surface them, and anti-patterns	Install Guidance
references/output-format.md	Per-harness and cross-harness report templates	Final output
references/discovery-pipeline.md	W0–W4 discover waves, depth inference, evidence vs contracts, redirects	Discover
references/discovery/	Coordinator contract, scout templates, output formats, research integration	Discover scouts and reports
references/ecosystem-research.md	Legacy source-family notes; prefer discovery-pipeline.md for dispatch	Discover W2 source planning
references/source-profiles.md	Source families, programmatic access, confidence roles, and degraded-source behavior	Discover W2 source planning
references/usage-review.md	Read-only usage sources, privacy classes, scorecard, signal schema, and recommendation lanes	Usage Review
data/research-sources.json	Machine-readable source registry for source planning and probe scripts	Discover W2 source planning

Read only the references needed for the active step. Do not preload all references.

Progressive Disclosure

Load reference files as indicated by the active mode. Do not load all references at once; use the dispatch table, source plan, and final output needs to choose the smallest relevant set.

Canonical Vocabulary

These terms are canonical for harness-master reports and should be used exactly.

Use these terms exactly throughout:

Term	Meaning	NOT
harness	One supported agent/runtime target	editor, toolchain
level	project, global, or both scope for the review	environment
project surface	Repo-local file or generated artifact used by a harness	global config
global surface	User-level harness config outside the repo	project file
dry-run	Findings + patch preview only; no edits	apply
approval gate	Explicit user consent required before edits	implied approval
blind-spot	A surface or behavior that is not observable in the current session	guess
repo-observed	Behavior inferred from the current codebase's real harness wiring	official-doc evidence
patch preview	Proposed diff or snippet shown before edits	applied change
canonical source	The file or config that should be changed instead of a generated output	generated surface
source plan	Read-only list of sources, URLs/commands, env vars, evidence fields, and failure modes	apply plan
candidate dossier	Normalized evidence bundle for one ecosystem candidate	endorsement
support tier	Adoption readiness class backed by evidence and validation state	popularity rank
quarantine	Candidate class for credential, proxy, destructive, offensive, or broad-permission risk	rejection without review
usage signal	Sanitized aggregate evidence about token, cost, quota, tool, MCP, skill, plugin, or workflow behavior	raw transcript
privacy class	Sensitivity class that determines whether a source can be collected, summarized, or must stay manual-only	permission bypass

Critical Rules

1. Every new harness-master run starts in dry-run audit mode.
2. Never edit before explicit user approval.
3. Apply Approved is valid only after a matching dry-run review in the current session.
4. Ask only for missing inputs. Never re-ask resolved harnesses or levels.
5. Always inspect project context before recommending harness changes.
6. Always refresh latest official guidance before claiming a configuration is current or stale.
7. Mark blind spots explicitly. Do not guess hidden or UI-only settings.
8. Prefer edits to canonical sources over generated or merged outputs when this is clear from evidence.
9. Every finding must carry an evidence tag: verified-file, verified-doc, repo-observed, or blind-spot.
10. Show concrete patch previews whenever the correct edit is clear.
11. Use npx skills add ... only when installation is genuinely the right next step.
12. Unsupported harnesses must be refused cleanly with the supported set listed.
13. Ecosystem research is advisory and read-only. Never apply from a research report alone.
14. Credentialed APIs are optional enrichments. Missing credentials lower confidence but do not block baseline source planning.
15. GitHub GraphQL enriches known candidate repositories; use REST/search or code/file lookup for broad discovery.
16. Usage Review is advisory and read-only. Never apply config changes, install tools, export raw sessions, or print raw prompts/traces from usage findings alone.
17. Usage Review may use aggregate-safe and sanitized metadata sources, but secret files, credential values, raw transcripts, raw traces, and raw message-table bodies are out of scope.