Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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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.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<default_to_action> When investigating code claims:
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:
| 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:
Red Flags:
Steps:
Red Flags:
Steps:
Red Flags:
catch {} swallowing errors## 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/
├── investigations/* - Investigation reports
├── evidence/* - Collected evidence
├── verdicts/* - Claim verdicts
└── patterns/* - Common deception patterns
const investigationFleet = await FleetManager.coordinate({
strategy: 'evidence-investigation',
agents: [
'qe-code-reviewer', // Code analysis
'qe-security-auditor', // Security claim verification
'qe-performance-validator' // Performance claim verification
],
topology: 'parallel'
});
"It is a capital mistake to theorize before one has data." Trust only reproducible evidence. Don't trust commit messages, documentation, or "works on my machine."
The Sherlock Standard: Every claim must be verified empirically. What does the evidence actually show?
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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