5,071 件 ・ 関連度順
概要と使いどころ
Synthesizes exploration findings into unified analysis with deep investigation capabilities (shell commands, git history, dependency analysis) and completeness evaluation (converted from 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。
Generates MkDocs-flavored or standard GitHub-flavored Markdown documentation from codebase analysis findings including API references, architecture guides, standalone markdown files, and change summaries (converted from 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。
Explores codebases to find relevant files, trace execution paths, and map architecture with team communication capabilities for collaborative analysis (converted from 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。
Reviews code implementations for correctness, security, maintainability with confidence-scored findings (converted from 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。
Reference patterns for graph algorithm problems including BFS, DFS, Dijkstra, topological sort, union-find, MST, Bellman-Ford, and bipartite checking. Provides recognition signals, Python templates, edge cases, and common mistakes for each technique. Use when solving problems involving graphs, trees, shortest paths, connectivity, or dependency ordering.
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 when asking whether something in imp is actually finished - hunting stubs, placeholders, request fields that are parsed and then ignored, code paths or kernels that never run, gate-based features that silently no-op, tests that assert nothing. Triggers on "is this implemented", "unfinished", "stub", "placeholder", "dead path", "does this flag do anything", "accepted but ignored", "kernel never launched", "test asserts nothing", "neutral A/B on a gated feature". Do NOT use for structural debt / god files / duplication (codebase-audit) or for who-calls-what (code-graph).
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。
Structures comprehensive buy-side diligence across financial, legal, commercial, and operational workstreams. Use when coordinating DD processes, building diligence checklists, or synthesizing DD findings.
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。
Ripgrep-powered dead-code crawler that finds stale, broken, or orphaned JavaScript/CJS/MJS modules in the Agent Studio framework. Walks a target directory, checks each file for external references, and surfaces (or deletes) unreferenced files. Prevents registry bloat and silent tool dropouts from modules no one calls.
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。
Protocol for task synchronization, context handoff, and cross-session coordination using Claude Code task tools. Ensures agents properly update tasks with findings and enables seamless work continuation.
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-aware smart revert for tracks, phases, and tasks. Handles rewritten history, finds related commits, and provides safe rollback with multiple confirmation gates.
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。
Automated security scanning pipeline covering SAST (Semgrep), SCA (OWASP dependency-check), SBOM generation (Syft), vulnerability scanning (Grype), and secrets detection (detect-secrets). Invoked by security-architect for pre-commit gates, CI integration, and finding triage.
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。
Manus-style file-based planning for complex tasks. Use task_plan.md, findings.md, and progress.md to maintain persistent context. Use for multi-step tasks, research, or work spanning many tool calls.
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。
AI-first security scanning with Medusa. 3,000+ detection patterns covering AI/ML, agents, MCP, RAG, prompt injection, and traditional SAST vulnerabilities. Wraps Medusa CLI with SARIF/JSON parsing, structured finding output, OWASP mapping, and remediation guidance.
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。
Structural code graph queries via CodeGraphContext MCP (tree-sitter + KuzuDB). Find callers, callees, class hierarchies, dead code, and module dependencies.
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。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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。
Benchmark external agent frameworks, auto-detect source type, scan for prompt injection, and convert findings into a concrete TDD upgrade backlog for agent-studio evolution.
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。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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。
Two-step self-review at every task lifecycle phase. Step 1 (this skill) runs in-context to gather session signals — files read vs grepped, user pushback, build evidence, degradation signs. Step 2 launches a forked subagent for unbiased code/plan review with fresh eyes. Combined report classifies findings as hard blocks or soft warnings.
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。
Validate a bug bounty finding before writing a report — the 7-Question Gate, 4 pre-submission gates, always-rejected list, conditional chain table, CVSS 3.1 quick reference, and severity decision guide. Use BEFORE writing any report to avoid N/A and low quality submissions.
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。
Fuzz directories, files, and parameters using ffuf, feroxbuster, arjun, paramspider, and x8. Use when the user has live hosts and needs to find hidden routes, backup files, exposed configs, and untracked URL parameters.
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。
Mobile (Android) app recon and analysis — decompile APK, extract secrets, find hardcoded endpoints, exposed Firebase / AWS / API keys, deeplink abuse, intent vulnerabilities, exported components, WebView XSS, MobSF static analysis. Use when target's scope includes Android apps (.apk).
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。
Rewrite text so it reads like a real person wrote it, not an AI. Auto-detects Russian vs English and applies the matching rule set. Use when the user asks to humanize text, remove the "AI smell", de-AI, or make AI output sound natural - especially for Russian. Russian requests count too - очеловечь, перепиши как человек, оживи текст, убери запах ИИ, сделай живее, звучит как робот, убери канцелярит. Also use when asked whether a text was written by AI - нейросеть писала?, похоже на ChatGPT?, видно ли, что это ИИ, человек или бот писал, did AI write this? - then check and report findings without rewriting. While installed, also keep your own Russian and English writing free of the quick-list stock phrases, silently; never edit the user's text unless asked.
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。
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".
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。
Expert PE review of a submittal (shop drawings, product data, samples, equipment brochures) against specs, drawings, code questions and trade coordination. Produces findings with both sources, trade routing and a completeness gate. Triggers: 'review submittal', 'review shop drawings'.
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。