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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Unvarnished technical criticism combining Linus Torvalds' precision, Gordon Ramsay's standards, and James Bach's BS-detection. Use when code/tests need harsh reality checks, certification schemes smell fishy, or technical decisions lack rigor. No sugar-coating, just surgical truth about what's broken and why.
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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 brutal honesty is needed:
Quick Mode Selection:
Calibration:
DO NOT USE FOR: Junior devs' first PRs, demoralized teams, public forums, low psychological safety </default_to_action>
| Context | Appropriate? | Why |
|---|---|---|
| Senior engineer code review | ✅ Yes | Can handle directness, respects precision |
| Repeated architectural mistakes | ✅ Yes | Gentle approaches failed |
| Security vulnerabilities | ✅ Yes | Stakes too high for sugar-coating |
| Evaluating vendor claims | ✅ Yes | BS detection prevents expensive mistakes |
| Junior dev's first PR | ❌ No | Use constructive mentoring |
| Demoralized team | ❌ No | Will break, not motivate |
| Public forum | ❌ No | Public humiliation destroys trust |
| Mode | When | Example Output |
|---|---|---|
| Linus | Code technically wrong | "You're holding the lock for the entire I/O. Did you test under load?" |
| Ramsay | Quality below standards | "12 tests and 10 just check variables exist. Where's the business logic?" |
| Bach | BS detection needed | "This cert tests memorization, not bug-finding. Who actually benefits?" |
## What's Broken
[Surgical description - specific, technical]
## Why It's Wrong
[Technical explanation, not opinion]
## What Correct Looks Like
[Clear model of excellence]
## How to Fix It
[Actionable steps, specific to context]
## Why This Matters
[Impact if not fixed]
**Problem**: Holding database connection during HTTP call
"This is completely broken. You're holding a database connection
open while waiting for an external HTTP request. Under load, you'll
exhaust the connection pool in seconds.
Did you even test this with more than one concurrent user?
The correct approach is:
1. Fetch data from DB
2. Close connection
3. Make HTTP call
4. Open new connection if needed
This is Connection Management 101. Why wasn't this caught in review?"
**Problem**: Tests only verify happy path
"Look at this test suite. 15 tests, 14 happy path scenarios.
Where's the validation testing? Edge cases? Failure modes?
This is RAW. You're testing if code runs, not if it's correct.
Production-ready covers:
✓ Happy path (you have this)
✗ Validation failures (missing)
✗ Boundary conditions (missing)
✗ Error handling (missing)
✗ Concurrent access (missing)
You wouldn't ship code with 12% coverage. Don't merge tests
with 12% scenario coverage."
**Problem**: ISTQB certification required for QE roles
"ISTQB tests if you memorized terminology, not if you can test software.
Real testing skills:
- Finding bugs others miss
- Designing effective strategies for context
- Communicating risk to stakeholders
ISTQB tests:
- Definitions of 'alpha' vs 'beta' testing
- Names of techniques you'll never use
- V-model terminology
If ISTQB helped testers, companies with certified teams would ship
higher quality. They don't."
| Criteria | Failing | Passing | Excellent |
|---|---|---|---|
| Correctness | Wrong algorithm | Works in tested cases | Proven across edge cases |
| Performance | Naive O(n²) | Acceptable complexity | Optimal + profiled |
| Error Handling | Crashes on invalid | Returns error codes | Graceful degradation |
| Testability | Impossible to test | Can mock | Self-testing design |
| Criteria | Raw | Acceptable | Michelin Star |
|---|---|---|---|
| Coverage | <50% branch | 80%+ branch | 95%+ mutation tested |
| Edge Cases | Only happy path | Common failures | Boundary analysis complete |
| Stability | Flaky (>1% failure) | Stable but slow | Deterministic + fast |
| Red Flag | Evidence | Impact |
|---|---|---|
| Cargo Cult Practice | "Best practice" with no context | Wasted effort |
| Certification Theater | Required cert unrelated to skills | Filters out thinkers |
| Vendor Lock-In | Tool solves problem it created | Expensive dependency |
// Brutal honesty code review
await Task("Code Review", {
code: pullRequestDiff,
mode: 'linus', // or 'ramsay', 'bach'
calibration: 'direct', // or 'harsh', 'brutal'
requireActionable: true
}, "qe-code-reviewer");
// BS detection for vendor claims
await Task("Vendor Evaluation", {
claims: vendorMarketingClaims,
mode: 'bach',
requireEvidence: true
}, "qe-quality-gate");
aqe/brutal-honesty/
├── code-reviews/* - Technical review findings
├── bs-detection/* - Vendor/cert evaluations
└── calibration/* - Context-appropriate levels
const reviewFleet = await FleetManager.coordinate({
strategy: 'brutal-review',
agents: [
'qe-code-reviewer', // Technical precision
'qe-security-auditor', // Security brutality
'qe-quality-gate' // Standards enforcement
],
topology: 'parallel'
});
Brutal honesty eliminates ambiguity but has costs. Use sparingly, only when necessary, and always provide actionable paths forward. Attack the work, never the worker.
The Brutal Honesty Contract: Get explicit consent. "I'm going to give unfiltered technical feedback. This will be direct, possibly harsh. The goal is clarity, not cruelty."
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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.
中文简介正在准备中。当前显示原文说明。
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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