analyze
GitHub利用AI分析侦察数据,识别高价值目标并推荐攻击策略。通过映射皇冠宝石、排名攻击路径及检测盲区,结合权重评估生成可执行的狩猎建议,辅助红队高效发现漏洞。
Trigger Scenarios
Install
npx skills add H-mmer/pentest-agents --skill analyze -g -y
SKILL.md
Frontmatter
{
"name": "analyze",
"description": "Analyze recon output with AI to suggest high-value targets and attack strategies. Usage: \/analyze <target>",
"disable-model-invocation": false
}
AI-powered analysis of recon data for: $ARGUMENTS
Process
- Read all recon data:
ls recon/and read key files - Read brain data:
uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py brief $ARGUMENTS - Read tech stack intel:
uv run python3 $CLAUDE_PROJECT_DIR/tools/intel_engine.py suggest <detected-stack> - Read hacktivity patterns:
uv run python3 $CLAUDE_PROJECT_DIR/tools/intel_engine.py analyze
Analysis Tasks (do all of these)
Crown Jewel Mapping
What's the most valuable thing an attacker could access on this target?
- Financial data? → hunt IDOR on payment/billing endpoints
- User PII? → hunt IDOR on profile/export endpoints
- Admin access? → hunt auth bypass on admin endpoints
- Infrastructure? → hunt SSRF → cloud metadata
Attack Path Ranking
Given the tech stack and recon output, rank the top 5 attack paths by:
- Likelihood of vulnerability existing (based on tech stack patterns)
- Impact if exploited (based on endpoint function)
- Competition (based on hacktivity — avoid heavily-reported vuln classes)
- Your past success (from brain patterns)
Blind Spot Detection
What has NOT been tested? What endpoints have no brain data? Cross-reference recon output against brain tested endpoints. Flag untested high-value endpoints.
Output
ANALYSIS: target.com
═════════════════════
Crown Jewels: [what's most valuable]
Top 5 Attack Paths:
1. [endpoint] × [vuln class] — likelihood: HIGH, impact: CRITICAL
2. ...
Blind Spots (untested P1 surface):
- /api/v2/payments/* — NO DATA in brain
- /api/v2/admin/* — NO DATA in brain
Recommendation: /hunt target.com --vuln-class [best bet]
Top-Tier Operator Addendum
Treat /analyze as a thesis generator, not a summary command. The output must make the next hour of hunting obvious.
- Build a weighted table before recommending anything:
asset_value: revenue, PII, admin, secrets, infrastructure, tenant boundaryexploit_likelihood: stack age, exposed methods, auth complexity, parser surface, prior bug class fitnovelty: low hacktivity overlap, new endpoint, changed JS, unusual integration, weak vendor patternproof_path: exact request needed to prove impact, required accounts, required evidence artifactpolicy_friction: rate limits, forbidden data access, third-party scope, credential validation rules
- Prefer attack paths with a short proof path over impressive theory. A boring IDOR with two accounts and a readback beats a speculative SSRF with no egress signal.
- Include negative evidence. If
/api/admin/*looks valuable but all routes are 403 with no differential, say that and explain what would change the ranking. - Separate
P1 now,P2 if time, andKill for this session. Top-tier analysis saves time by deleting tempting dead ends. - Every recommendation must name the next command and the exact first test:
/hunt target --vuln-class idorplus the endpoint pair, account pair, and field to compare.
Version History
- 41d49b6 Current 2026-07-24 11:56


