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概要と使いどころ
Exploit development curriculum covering core vulnerability classes with real-world CVE case studies: stack/heap buffer overflows, use-after-free, integer overflows, format strings, type confusion, and race conditions. Use when learning or teaching vuln classes, researching specific CVE patterns, or building exploit dev knowledge.
日本語の概要は準備中です。原文の説明を表示しています。
Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation ...
日本語の概要は準備中です。原文の説明を表示しています。
Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass...
日本語の概要は準備中です。原文の説明を表示しています。
Git-Notes-Based knowledge graph memory system. Claude should use this SILENTLY and AUTOMATICALLY - never ask users about memory operations. Branch-aware persistent memory using git notes. Handles context, decisions, tasks, and learnings across sessions.
日本語の概要は準備中です。原文の説明を表示しています。
Development skill from everything-claude-code
日本語の概要は準備中です。原文の説明を表示しています。
Converts the learning Markdown into a standalone, split-screen interactive HTML page. Left pane for EN/CN text, right pane for deep linguistic analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Generates comprehension questions and language exercises based on the analyzed content to create a complete learning loop.
日本語の概要は準備中です。原文の説明を表示しています。
Captures executable contracts and coding conventions into .trellis/spec/ documents. Use when learning something valuable from debugging, implementing, or discussion that should be preserved for future sessions.
日本語の概要は準備中です。原文の説明を表示しています。
Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.
日本語の概要は準備中です。原文の説明を表示しています。
Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern analysis'. NOT for failure analysis (use dag-failure-analyzer) or performance profiling (use dag-performance-profiler).
日本語の概要は準備中です。原文の説明を表示しています。
Conduct Critical Decision Method (CDM) interviews to elicit expert reasoning strategies (L3 knowledge) from practitioners about specific challenging past incidents. Use when capturing expertise for skill development, preserving knowledge before practitioner departure, building post-incident learning artifacts, or investigating decisions in complex sociotechnical systems. Outputs structured incident reconstructions with cues, decisions, alternatives considered, and counterfactual analysis suitable for distillation into skills or training material.
日本語の概要は準備中です。原文の説明を表示しています。
Design RL simulation sandboxes, trajectory datasets, QLoRA/LoRA adaptation plans, eval harnesses, and rollback unhooks for agents learning specific coding behaviors beyond the base model. Use when building or reviewing tool-use training loops, offline trajectories, reward functions, self-improvement harnesses, or Port Daddy agent behavior curricula. NOT for generic ML tutorials, full model training operations, or production deployment without eval gates and safety unhooks.
日本語の概要は準備中です。原文の説明を表示しています。
Apply AlphaGo-style patterns: learned priors plus search, mixed evaluators, self-play, and asynchronous fast/slow loops. Use for intractable search and compute allocation. NOT for basic supervised learning, tractable optimization, or domains without search bottlenecks.
日本語の概要は準備中です。原文の説明を表示しています。
Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.
日本語の概要は準備中です。原文の説明を表示しています。
Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
pytest + httpx + TestClient patterns for the FastAPI backend. Dependency overrides for auth, FakeSupabase mocking, HTTPException assertions. Use when writing or reviewing backend/tests/test_*.py.
日本語の概要は準備中です。原文の説明を表示しています。
Integrate with the FastAPI backend through the typed wrapper in frontend/lib/api.ts, attach Supabase JWTs for admin calls, and handle errors via ApiError. Use when adding/modifying API calls or wiring auth.
日本語の概要は準備中です。原文の説明を表示しています。
Build FastAPI endpoints in the layered router → service → Supabase pattern, with Pydantic v2 schemas, JWT/admin gating, and consistent HTTPException-based domain errors. Use when adding/modifying any endpoint under backend/.
日本語の概要は準備中です。原文の説明を表示しています。
Build pages and components in the Next.js 15 App Router (RSC by default, "use client" only when needed), Tailwind for styling, typed props, and the existing CartProvider for cart state. Use when creating or modifying pages, layouts, components, or forms in frontend/.
日本語の概要は準備中です。原文の説明を表示しています。
Pre-PR code review checklist for this repo. Plan alignment, security gates, layering, dead code, API-surface completeness. Used by the code-reviewer agent.
日本語の概要は準備中です。原文の説明を表示しています。
Supabase Postgres + Storage + Auth operations from FastAPI — service-role client, RLS-aware queries, additive SQL migrations under backend/db/migrations, the create_order RPC, and the product-images bucket. Use when adding tables/columns, writing queries, calling RPCs, or working with Storage.
日本語の概要は準備中です。原文の説明を表示しています。
Vitest + React Testing Library patterns for the Next.js 15 frontend. Co-located component tests, mocking lib/api.ts, testing Server Components, CartProvider harness. Use when writing or reviewing *.test.ts(x) files in frontend/.
日本語の概要は準備中です。原文の説明を表示しています。
Drive Chrome via the claude-in-chrome MCP to exercise plan.md::Verify steps for a shipped feature. Loads deferred MCP tool schemas, asserts console/network cleanliness, records a GIF for the PR. Used by the browser-verifier agent.
日本語の概要は準備中です。原文の説明を表示しています。