2,316 件 ・ 関連度順
概要と使いどころ
Documentation reconnaissance for takeover — find all docs, assess accuracy, freshness, coverage, and discoverability, and identify critical knowledge gaps. Use when asked "what docs exist", "documentation assessment", or "knowledge gaps".
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 guide for property-based testing (fast-check, Hypothesis) and mutation testing (Stryker, mutmut) — AI-generated test properties, invariant discovery, mutation score optimization, and test suite quality verification / Panduan ahli property-based testing (fast-check, Hypothesis) dan mutation testing (Stryker, mutmut) — properti tes yang dihasilkan AI, penemuan invarian, optimasi skor mutasi, dan verifikasi kualitas suite tes.
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。
Improve a Hinge dating profile through research-backed screenshot audits, photo selection and ordering, conversational discovery, prompt and caption writing, settings review, or post-launch troubleshooting. Use for a full profile rebuild or a focused request about photos, copy, positioning, match quality, or profile performance.
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。
Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.
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 this skill when the user wants to discover, define, or refine their product's aesthetic identity. Triggers on "what should this feel like?", "help me figure out the vibe", "something feels off visually", "I want it to feel like [X]", "define the visual language", or when a project has no established design direction. This is the art director skill - it helps users articulate something they can feel but can't name. Also handles quick cohesion checks when the orchestrator needs one during cycle planning.
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。
Decide whether to adopt, adapt, or justify building a new script, tool, utility, library, module, dependency, or bespoke capability. Use in task planning, worker execution (including tooling discovered after planning), or code review when an implementation choice may duplicate an existing solution.
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。
Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.
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。
GitHub interactome explorer for bmorphism/plurigrid ecosystem. Maps collaborations across AlgebraicJulia, Topos Institute, Anthropic, and MCP servers. Use for discovering cobordisms between research communities.
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。
Interact with Axiom.trade -- a Y Combinator-backed (W25) DEX aggregator and trading platform on Solana with Hyperliquid perpetuals integration. Covers API architecture, multi-wallet management, MEV protection, token discovery, and wallet tracking.
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。
BDD-Driven Mathematical Content Verification Skill Combines Behavior-Driven Development with mathematical formula extraction, verification, and transformation using: - Cucumber/Gherkin for specification - RSpec for implementation verification - mathpix-gem for LaTeX/mathematical content extraction - Pattern matching on syntax trees for formula validation Enables iterative discovery and verification of mathematical properties through executable specifications.
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。
AWS SSO discovery, configuration, and terminal usage
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。
Python fuzzing with Atheris for discovering vulnerabilities in Python 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。
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
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。
Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
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。
Dynamically discover and load ACSet reference skills in dependency-optimal order using GitHub interactome analysis. Use when loading the acsets skill and needing its validation, generation, and coordination partners resolved automatically with GF(3) conservation guarantees.
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 MCP servers into Claude Code plugins — covers .mcp.json configuration, plugin.json mcpServers field, server types (stdio, SSE, HTTP, WebSocket), environment variable expansion, tool naming conventions, OAuth and token authentication, security best practices, and testing workflows. Use when adding an MCP server to a plugin, configuring MCP authentication, debugging MCP tool discovery, setting up Model Context Protocol integration, or choosing between stdio and SSE transport types.
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 writing delegation instructions to subagents, authoring CLAUDE.md files, rules, skills, or agent definitions, or auditing existing AI-facing content for bloat. Activates on "write a rule for", "add to CLAUDE.md", "create an agent", "update memory", or any request to author AI-facing instruction content. Removes discoverable data, explained-away knowledge, invented constraints, and stale cached facts.
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。
Decide which plugin component type to use and how to organize components at scale. Covers the component lifecycle (discovery and activation phases), decision framework for choosing between commands, skills, agents, hooks, and MCP servers, and organization patterns for each component type. Use when asking "which component type should I use", "command vs skill vs agent", "when to use a hook vs MCP server", "component lifecycle", "how to organize plugin components", "plugin structure patterns", or "scale a plugin with many components".
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。
Agent Skills portable format router. Use when creating cross-client skills, validating portable frontmatter, packaging skills for upload or API use, or implementing skill discovery and activation.
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。
Audit skill lifecycle by tracing call chains, detecting circular dependencies, finding instruction contradictions, identifying duplicated datasets, analyzing bidirectional coherence, discovering scriptable sequences, and learning patterns. Use when checking skill coherence, validating skill workflow, finding semantic gaps in plugin structure, or auditing plugin before marketplace submission. Generates audit reports to .plugin-creator/audits/ with findings by dimension.
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。
Autonomous feature research and gap analysis. Use when starting /add-new-feature or analyzing existing architecture documents. Explores codebase patterns, identifies ambiguities, and produces feature-context-{slug}.md for orchestrator RT-ICA phase. Does NOT make technical 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。
Claude Code runtime skills router. Use when configuring SKILL.md frontmatter, discovery, invocation, hooks, context forks, substitutions, loading behavior, or headless skill dispatch.
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。
Executes a groomed milestone with parallel kage-bunshin sessions in isolated worktrees. Use when a milestone has been groomed and /groom-milestone has produced a dispatch plan. Reads the dispatch plan, creates an integration branch, spawns one kage-bunshin (independent claude -p process) per wave item in its own worktree — each session is a full orchestrator with the Agent tool. Sequentially merges worktree branches, relays wave discoveries to subsequent waves, then lands the integration branch to main. Takes a milestone number as argument.
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。
Query and invoke tools on MCP servers using fastmcp list and fastmcp call. Use when you need to discover what tools a server offers, call tools, or integrate MCP servers into workflows.
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。