a11y-ally
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse,…
Evidence-based investigative code review using deductive reasoning to determine what actually happened versus what was claimed. Use when verifying implementation claims, investigating bugs, validating fixes, or conducting root cause analysis. Elementary approach to finding truth
$ npx -y skills add proffesor-for-testing/agentic-qe --skill sherlock-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/sherlock-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Evidence-based investigative code review using deductive reasoning to determine what actually happened versus what was claimed. Use when verifying implementation claims, investigating bugs, validating fixes, or conducting root cause analysis. Elementary approach to finding truth
name: sherlock-review description: "Evidence-based investigative code review using deductive reasoning to determine what actually happened versus what was claimed. Use when verifying implementation claims, investigating bugs, validating fixes, or conducting root cause analysis. Elementary approach to finding truth through systematic observation." category: quality-review priority: high tokenEstimate: 1100 agents: [qe-code-reviewer, qe-security-auditor, qe-performance-validator] implementation_status: optimized optimization_version: 1.0 last_optimized: 2025-12-03 dependencies: [] quick_reference_card: true tags: [investigation, evidence-based, code-review, root-cause, deduction] trust_tier: 2 validation: schema_path: schemas/output.json validator_path: scripts/validate-config.json
<default_to_action> When investigating code claims: 1. OBSERVE: Gather all evidence (code, tests, history, behavior) 2. DEDUCE: What does evidence actually show vs. what was claimed? 3. ELIMINATE: Rule out what cannot be true 4. CONCLUDE: Does evidence support the claim? 5. DOCUMENT: Findings with proof, not assumptions
**The 3-Step Investigation:**
# 1. OBSERVE: Gather evidence git diff <commit> npm test -- --coverage # 2. DEDUCE: Compare claim vs reality # Does code match description? # Do tests prove the fix/feature? # 3. CONCLUDE: Verdict with evidence # SUPPORTED / PARTIALLY SUPPORTED / NOT SUPPORTED
**Holmesian Principles:**
</default_to_action>
| Category | What to Check | How | |----------|---------------|-----| | **Claim** | PR description, commit messages | Read thoroughly | | **Code** | Actual file changes | `git diff` | | **Tests** | Coverage, assertions | Run independently | | **Behavior** | Runtime output | Execute locally | | **Timeline** | When things happened | `git log`, `git blame` |
| Verdict | Meaning | |---------|---------| | ✓ **TRUE** | Evidence fully supports claim | | ⚠ **PARTIALLY TRUE** | Claim accurate but incomplete | | ✗ **FALSE** | Evidence contradicts claim | | ? **NONSENSICAL** | Claim doesn't apply to context |
---
## Sherlock Investigation: [Claim] ### The Claim "[What PR/commit claims to do]" ### Evidence Examined - Code changes: [files, lines] - Tests added: [count, coverage] - Behavior observed: [what actually happens] ### Deductive Analysis **Claim**: [specific assertion] **Evidence**: [what you found] **Deduction**: [logical conclusion] **Verdict**: ✓/⚠/✗ ### Findings - What works: [with evidence] - What doesn't: [with evidence] - What's missing: [gaps in implementation/testing] ### Recommendations 1. [Action based on findings]
Every investigation MUST surface at least 3 weighted observations (CRITICAL=3, HIGH=2, MEDIUM=1, LOW=0.5). Elementary observations count at INFORMATIONAL=0.25 weight. A Sherlock investigation that finds nothing is a failed investigation -- Holmes always finds clues.
---
**Steps:** 1. Reproduce bug on commit before fix 2. Verify bug is gone on commit with fix 3. Check if fix addresses root cause or symptom 4. Test edge cases not in original report
**Red Flags:**
**Steps:** 1. Run benchmark on baseline commit 2. Run same benchmark on optimized commit 3. Compare in identical conditions 4. Verify measurement methodology
**Red Flags:**
**Steps:** 1. List all edge cases in code path 2. Check each has test coverage 3. Test boundary conditions 4. Verify error handling paths
**Red Flags:**
---
## Case: PR #123 "Fix race condition in async handler" ### Claims Examined: 1. "Eliminates race condition" 2. "Adds mutex locking" 3. "100% thread safe" ### Evidence: - File: src/handlers/async-handler.js - Changes: Added `async/await`, removed callbacks - Tests: 2 new tests for async flow - Coverage: 85% (was 75%) ### Analysis: **Claim 1: "Eliminates race condition"** Evidence: Added `await` to sequential operations. No actual mutex. Deduction: Race avoided by removing concurrency, not synchronization. Verdict: ⚠ PARTIALLY TRUE (solved differently than claimed) **Claim 2: "Adds mutex locking"** Evidence: No mutex library, no lock variables, no sync primitives. Verdict: ✗ FALSE **Claim 3: "100% thread safe"** Evidence: JavaScript is single-threaded. No worker threads used. Verdict: ? NONSENSICAL (meaningless in this context) ### Conclusion: Fix works but not for reasons claimed. Race condition avoided by making operations sequential, not by adding synchronization. ### Recommendations: 1. Update PR description to accurately reflect solution 2. Add test for concurrent request handling 3. Remove incorrect technical claims
---
// Evidence-based code review
await Task("Sherlock Review", {
prNumber: 123,
claims: [
"Fixes memory leak",
"Improves performance 30%"
],
verifyReproduction: true,
testEdgeCases: true
}, "qe-code-reviewer");
// Bug fix verification
await Task("Verify Fix", {
bugCommit: 'abc123',
fixCommit: 'def456',
reproductionSteps: steps,
testBoundaryConditions: true
}, "qe-code-reviewer");---
aqe/sherlock/ ├── inves
AI-powered quality engineering agents that generate tests, find coverage gaps, detect flaky tests, and learn your codebase patterns — across 11 coding agent platforms.
Repo: proffesor-for-testing/agentic-qe
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse,…
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use…
Master advanced AgentDB features including QUIC synchronization, multi-database management,…
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms.…
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory,…
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x…