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概要と使いどころ
Detect missing glyphs, icons, and HD variants by comparing registries against glyph mapping files, icon directories, and manifests. Reports gaps for skills, agents, and teams across all palettes.
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。
One to three sentences describing what this skill accomplishes, followed by key activation triggers. This field is the primary mechanism agents use to decide whether to activate the skill — it is read during discovery before the full body is loaded. Start with a verb. Include the most important "when to use" conditions inline. Max 1024 characters.
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。
How agents discover and use skills. Use to understand skill invocation protocol.
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。
Creates and registers templates for agents, skills, workflows, hooks, and code patterns. Handles post-creation catalog updates, consuming skill integration, and README registration. Use when creating new template types or standardizing patterns.
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。
Canon TDD for humans and AI agents. Use for production code changes by writing tests first, proving RED, implementing minimal GREEN, and refactoring safely. 2026 edition adds TDP, flakiness gate, ralph-loop integration, memory-search in Step 0, PBT as Step 5.5, mutation testing gate in Step 4, and CJS LSP warning.
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 structured plans from requirements. Generates comprehensive plans with steps, dependencies, risks, and success criteria. Coordinates with specialist agents for planning input and validates plan completeness. Uses template-renderer for formatted output.
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。
Guided project onboarding for new codebases. Helps agents understand project structure, build systems, test commands, and development workflows by creating persistent knowledge memories.
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 JSON Schema validation files for skills, agents, hooks, workflows, and data structures. Ensures type safety and input validation across the framework.
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。
Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running 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。
Auto-evolution skill to initialize a new repository with AGENTS.md localized context.
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。
Documents the hook enforcement system, bypass mechanisms, and common failure patterns to help agents avoid getting stuck on protected path writes
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。
Collaborative document creation via a structured three-stage workflow. Use for writing specs, PRDs, design docs, proposals, RFCs, and any long-form document where quality and clarity matter. Brainstorms 5-20 options per section, builds iteratively, and tests with reader sub-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。
Discover, parse, and call external HTTP APIs at runtime using OpenAPI specs, tool templates, and iterative chaining. Adapted from UTCP (Universal Tool Calling Protocol) patterns for Node.js and Claude Code 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。
Byzantine consensus voting for multi-agent decision making. Implements voting protocols, conflict resolution, and agreement algorithms for reaching consensus among multiple 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。
Decision framework for agents to determine WHEN and WHETHER to use Claude Code's native cron scheduler (CronCreate/CronList/CronDelete) vs OS cron, GitHub Actions, or simple Task() delegation.
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。
Detect when agents are stuck in repetitive action loops using a rolling 20-action window with escalating nudges — replan after 3 similar actions, explore after 5, force-done after 8.
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。
Unified lifecycle management for all framework artifacts (skills, agents, hooks, workflows, templates, schemas)
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 apps with the Claude API or Anthropic SDK. TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`/`claude_agent_sdk`, or user asks to use Claude API, Anthropic SDKs, or Agent SDK. DO NOT TRIGGER when: code imports `openai`/other AI SDK, general programming, or ML/data-science tasks.
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。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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。
The Agent Tool Contract — 5 principles for designing tools agents call reliably: predictable signature, rich errors, token-efficient output, idempotency, graceful degradation. Includes anti-pattern table with 8 common mistakes.
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-native issue tracking (beads), coding guidelines, knowledge injection, and spec-driven planning for AI agents. Drop-in replacement for bd/Beads with simpler architecture. Use for: tracking issues/beads with dependencies, creating bugs/features/tasks, planning specs, implementing features from specs, code reviews, committing code, creating PRs, loading coding guidelines (TypeScript, Python, TDD, golden testing, Convex, monorepo patterns), code cleanup, research briefs, architecture docs, agent handoffs, and checking out third-party library source code. Invoke when user mentions: tbd, beads, bd, shortcuts, issues, bugs, tasks, features, epics, todo, tracking, specs, planning, implementation, validation, guidelines, templates, commit, PR, pull request, code review, testing, TDD, test-driven, golden testing, snapshot testing, TypeScript, Python, Convex, monorepo, cleanup, dead code, refactor, handoff, research, architecture, labels, search, checkout library, source code review, or any workflow shortcut.
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。
Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.
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。