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。
Use when validating security findings from SAST/DAST scans, proving exploitability of reported vulnerabilities, eliminating false positives, or running the 4-phase pentest pipeline (recon, analysis, validation, report).
中文简介正在准备中。当前显示原文说明。
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 validating security findings:
Quality Gates:
| Phase | Agent(s) | Purpose | Parallelism |
|---|---|---|---|
| 1. Recon | qe-security-scanner | SAST, DAST, dependency scan, secrets | Internal parallel |
| 2. Analysis | qe-security-reviewer + qe-security-auditor | Code review + compliance check | Both in parallel |
| 3. Validation | qe-pentest-validator | Graduated exploit validation | Per-vuln-type parallel |
| 4. Report | qe-quality-gate | "No Exploit, No Report" filter | Sequential |
| Tier | Handler | Cost | Latency | Use When |
|---|---|---|---|---|
| 1 | Agent Booster (WASM) | $0 | <1ms | Code pattern is conclusive (eval, innerHTML, hardcoded creds) |
| 2 | Haiku | $0.0002 | ~500ms | Need payload test against live target |
| 3 | Sonnet/Opus | $0.003-$0.015 | 2-5s | Full exploit chain with data proof |
| Scenario | Tier | Estimated Cost |
|---|---|---|
| PR security review (source only) | 1 | $0 |
| Pre-release validation (staging) | 1-2 | $1-5 |
| Full pentest validation | 1-3 | $5-15 |
| Compliance audit evidence | 1-3 | $5-15 |
pentest:
target_url: https://staging.app.com # REQUIRED for Tier 2-3
source_repo: ./src # REQUIRED for Tier 1+
exploitation_tier: 2 # 1=pattern-only, 2=payload-test, 3=full-exploit
vuln_types: # Which pipelines to run
- injection # SQL, NoSQL, command injection
- xss # Reflected, stored, DOM XSS
- auth # Auth bypass, session, JWT
- ssrf # URL scheme abuse, metadata
max_cost_usd: 15 # Budget cap per run
timeout_minutes: 30 # Time cap per run
require_authorization: true # MUST confirm target ownership
no_production: true # Block production URLs
production_patterns: # URL patterns to block
- "*.prod.*"
- "api.*"
- "www.*"
Every pentest validation run MUST:
| Attack | Tier 1 (Pattern) | Tier 2 (Payload) | Tier 3 (Full) |
|---|---|---|---|
| SQL injection | String concat in query | ' OR '1'='1 response diff | UNION SELECT data extraction |
| NoSQL injection | $where, $gt in query | Operator injection test | Collection enumeration |
| Command injection | exec(), system() calls | Command delimiter test | Reverse shell proof |
| LDAP injection | String concat in filter | Wildcard injection | Directory enumeration |
| Attack | Tier 1 (Pattern) | Tier 2 (Payload) | Tier 3 (Full) |
|---|---|---|---|
| Reflected XSS | No output encoding | <img onerror> reflection | Browser JS execution via qe-browser (Vibium) |
| Stored XSS | innerHTML assignment | Payload stored + retrieved | Cookie theft PoC |
| DOM XSS | document.write(location) | Fragment injection | DOM manipulation proof |
| Attack | Tier 1 (Pattern) | Tier 2 (Payload) | Tier 3 (Full) |
|---|---|---|---|
| JWT none | No algorithm validation | Modified JWT accepted | Admin access with forged token |
| Session fixation | No session rotation | Pre-set session reused | Cross-user session hijack |
| Credential stuffing | No rate limiting | 100 attempts unblocked | Valid credential discovery |
| IDOR | No authorization check | Access other user data | Full CRUD on foreign resources |
| Attack | Tier 1 (Pattern) | Tier 2 (Payload) | Tier 3 (Full) |
|---|---|---|---|
| Internal URL | User-controlled URL fetch | http://169.254.169.254 | Cloud metadata extraction |
| DNS rebinding | URL validation bypass | Rebind to internal IP | Internal service access |
| Protocol smuggling | URL scheme not restricted | file:///etc/passwd | File content in response |
// Phase 1: Recon (parallel scans)
await Task("Security Scan", {
target: "./src",
layers: { sast: true, dast: true, dependencies: true, secrets: true }
}, "qe-security-scanner");
// Phase 2: Analysis (parallel review)
await Promise.all([
Task("Code Security Review", {
findings: phase1Results,
depth: "comprehensive"
}, "qe-security-reviewer"),
Task("Compliance Audit", {
findings: phase1Results,
frameworks: ["owasp-top-10"]
}, "qe-security-auditor")
]);
// Phase 3: Validation (graduated exploitation)
await Task("Exploit Validation", {
findings: [...phase1Results, ...phase2Results],
target_url: "https://staging.app.com",
exploitation_tier: 2,
vuln_types: ["injection", "xss", "auth", "ssrf"],
max_cost_usd: 15,
timeout_minutes: 30
}, "qe-pentest-validator");
// Phase 4: Report ("No Exploit, No Report" gate)
await Task("Security Quality Gate", {
findings: phase3Results.confirmedFindings,
gate: "no-exploit-no-report",
require_poc: true
}, "qe-quality-gate");
| Status | Meaning | Action |
|---|---|---|
confirmed-exploitable | Exploitation succeeded with PoC | Report with evidence |
likely-exploitable | Partial exploitation, defenses detected | Report with caveats |
not-exploitable | All exploitation attempts failed | Filter from report |
inconclusive | WAF/defense blocked, unclear if vulnerable | Report for manual review |
aqe/pentest/
playbook/
exploit/{vuln_type}/{tech_stack}/{technique}
bypass/{defense_type}/{technique}
payload/{vuln_type}/{variant}
results/
validation-{timestamp}
poc/
{finding_id}-poc
| Scenario | Tier Mix | Findings | Est. Cost | Est. Time |
|---|---|---|---|---|
| PR check (source only) | 100% Tier 1 | 5 | $0 | <5s |
| Sprint validation | 70% T1, 30% T2 | 15 | $2-5 | 5-10 min |
| Release validation | 40% T1, 40% T2, 20% T3 | 25 | $8-15 | 15-30 min |
| Full pentest | 20% T1, 30% T2, 50% T3 | 40 | $15-30 | 30-60 min |
| Metric | Shannon | AQE Pentest Validation |
|---|---|---|
| Cost per run | ~$50 | $5-15 (graduated tiers) |
| Runtime | 60-90 min | 15-30 min (parallel pipelines) |
| False positive rate | Low (exploit-proven) | Low (same principle) |
| Learning | None (static prompts) | ReasoningBank playbook |
| Metric | Target | Measurement |
|---|---|---|
| False positive reduction | >60% of findings eliminated | Pre/post validator comparison |
| Exploit confirmation rate | >80% of confirmed findings truly exploitable | Manual PoC verification |
| Cost per run | <$15 USD | Token tracking per pipeline |
| Time per run | <30 minutes | Execution time metrics |
| Playbook growth | 100+ patterns after 6 months | Memory namespace count |
"No Exploit, No Report." A vulnerability scanner that can't prove exploitation delivers uncertain value. This skill transforms security findings from theoretical risks into proven vulnerabilities with evidence. Every confirmed finding comes with a reproducible proof-of-concept. Every false positive is eliminated before it reaches the report.
Think proof, not prediction. Don't report what MIGHT be vulnerable. Prove what IS vulnerable.
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。