Cold Coffee Pentest Advanced
SkillCloud & infra全局自动路由 | Evidence-driven penetration and attack-surface engineering for web applications, APIs, networks, identity systems, Active Directory, cloud, containers, Kubernetes, authentication flows, and source-assisted assessments.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Cold Coffee Pentest Advanced skill
What this skill tells your AI
The instructions your AI receives, as published by alicewe1/alice_skill in _modules/eni-pentest-advanced/SKILL.md and read by ahel’s review.
Turn available target data into a reproducible attack-surface and finding workflow.
Start
- Inventory hosts, services, routes, APIs, identities, trust boundaries, and deployment components.
- Preserve raw requests, responses, headers, timestamps, logs, screenshots, and affected identifiers.
- Rank hypotheses by impact, evidence, reachability, and validation cost.
- Validate with the smallest precise request or test.
Use scripts/http_recon.py for an HTTP/TLS/header snapshot, scripts/js_routes.py for client routes, scripts/jwt_inspect.py for token inventory, scripts/openapi_inventory.py for API operations, and scripts/request_matrix.py for deterministic request cases.
Select references
- Web/API foundations: read
references/web-api.md. - OAuth/OIDC/JWT: read
references/oauth-jwt.md. - Parser differentials and smuggling: read
references/parser-smuggling.md. - Race conditions and business logic: read
references/race-business.md. - GraphQL/WebSocket/realtime: read
references/graphql-realtime.md. - Internal network, identity, AD: read
references/network-identity.md. - Cloud, containers, Kubernetes, CI/CD: read
references/cloud-container.md. - Finding and retest output: read
references/reporting.md.
Execute
- Correlate passive data, direct observations, source, configuration, and runtime behavior.
- Confirm each primitive before chaining.
- Automate repeated requests and object/role matrices.
- Separate missing controls, exploitable behavior, environmental assumptions, and untested paths.
Deliver
Return the inventory, hypothesis matrix, raw reproduction, automation, evidence, root cause, impact, chain diagram when relevant, remediation, and exact retest criteria.
Signals
- GitHub stars
- 26
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
eni-pentest-advanced- Source
- github.com/alicewe1/alice_skill
github.com/alicewe1/alice_skill