investigation-review
Independently review a debugging conclusion against risk-critical evidence; return PASS or REVISE with concrete numbered blockers, separate from suggestions.
Skill by blakesims
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/blakesims-investigation-review
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/investigation-review
What's inside
- Skill name
investigation-review- Description
- Independently review a debugging conclusion against risk-critical evidence; return PASS or REVISE with concrete numbered blockers, separate from suggestions.
- License
- MIT
- Instructions
- 37 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Investigation Review
Review the investigation, not the investigator. Stay independent, evidence-led and read-only; do not implement fixes.
## Process
1. Read the report and identify the claims supporting its PROVED / DISPROVED / INCONCLUSIVE verdict.
2. Independently inspect source evidence and run selected checks where needed to establish the conclusion. Prioritize correctness, security, data integrity and operational risk; no fixed rerun count or negative control for every assertion. Distinguish checks you ran from evidence you evaluated.
3. Test material gaps: does the evidence distinguish competing explanations? Could the experiment detect the claimed effect? Is the right version/environment actually running? Are scope claims supported by measured results? A compilation or symptom check alone does not establish a runtime fix.
4. On re-review, check prior blockers, changed evidence and affected conclusions/dependencies. Retain the full investigation question and required evidence; reference still-valid prior report sections instead of replaying the history. Expand review if new evidence undermines earlier conclusions.
5. Return the gate with concise evidence and limitations. Preserve earlier reports/attempts; use exact report/section pointers for unchanged evidence.
## Gates
- **PASS** — the conclusion is supported within its stated scope and limitations; no material blockers remain. Nonblocking suggestions may remain. A well-supported INCONCLUSIVE verdict can pass; it is not proof of a fix.
- **REVISE** — a concrete error or evidence gap undermines the conclusion or a safety decision. Number each blocker with the claim/path, impact, evidence and smallest resolving check. Tooling/editorial preferences alone are nonblocking suggestions.
After 3 REVISE cycles without convergence, escalate to the user; do not expand proof machinery indefinitely.
## Safe checks
Never run source-mutating tests in the authoring tree. For isolation and candidate completeness, read `${CLAUDE_PLUGIN_ROOT}/skills/task-workflow/SKILL.md` → Review scope and safe validation. Report unavailable safe verification honestly; this is a prompt boundary, not a tool sandbox.
## Output
```md
## Review: PASS | REVISE
**Scope / attempt:** report and current evidence identity
**Evidence:** checks run/evaluated, actual results or exact report pointers
**Blockers:** numbered claim/path — impact, evidence, smallest resolving check (or None)
**Nonblocking suggestions:** optional improvements (or None)
**Limitations:** what remains unverified and why it matters
```
Use a fresh context or independent subagent, not the investigator's self-review. Write the full review once to the supplied report path; return a short gate/path handoff.From blakesims/task-workflow-plugin, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 18, 2026 (1 month ago)
- Last release
- Sep 14, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Done for you
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Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
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Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
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Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
investigation-review skill. Agent Mart. https://agentmart.dev/skills/blakesims-investigation-review (last updated 2026-10-24)