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概要と使いどころ
Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).
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。
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
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。
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
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。
Persist and retrieve repository-specific knowledge using AGENTS.md files. Use when you want to save important information about a codebase (build commands, code style, workflows) 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。
Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications.
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。
Model ElevenLabs voice synthesis as an ACSet schema with Unison ability handlers for typed voice operations. Use when integrating ElevenLabs TTS into category-theoretic pipelines, building typed voice synthesis workflows, or managing multi-voice narration with GF(3) conservation.
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。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab 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。
Routes Python engineering tasks to specialist skills by matching trigger patterns before any architecture, plan, or code is written. Use when working with Typer CLI frameworks, Rich or Textual terminal UIs, CLI UI/UX design, questionary prompts, FastMCP/MCP servers, ty type checker, uv package manager, Hatchling build backend, TOML editing, pre-commit/prek hooks, async Python, PyPI packaging, complex linting, technical debt modernization, testing workflows, feature development, or stdlib-only scripting.
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。
Configures CI/CD pipelines for automated Python package publishing to PyPI or GitLab Package Registry. Use when creating GitHub Actions or GitLab CI release workflows, setting up trusted publishing or API token-based PyPI authentication, configuring version management with git tags and hatch-vcs, writing pyproject.toml publishing metadata, testing packages against TestPyPI, or documenting the release process for a Python project.
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 working with Python tooling — uv package management, Hatchling build backend, ty or mypy type checker configuration, ruff linting, pre-commit hook setup, TOML read-write with tomlkit or tomllib, or PyPI packaging and release workflows. Routes to standalone specialist skills for deep dives on any single tool.
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 agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .plugin-creator/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute 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。
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 creating a new skill or updating an existing skill that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Activates on "create a new skill", "add a skill to plugin", or "update existing skill".
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。
Orchestrator context window discipline enforcement. Prevents the orchestrator from reading source files it will not edit, running diagnostic commands that waste context, and rationalizing delegation bypasses. Use when setting up orchestrator guardrails, reviewing delegation discipline, or diagnosing context window waste in multi-agent workflows. Activates PreToolUse hooks that surface decision points before source file reads and diagnostic command execution.
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。
Modern Git workflows, best practices, and commands. Use when the user asks about Git branch management (git switch vs checkout), file restoration (git restore), fixup commits, autosquash rebasing, git worktrees, rerere, force-with-lease, repository cleanup (git clean, stale branches, bloat analysis), history navigation (revision syntax, range notation, pickaxe search), recommended global git config, or GitButler and the but CLI.
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 answering business analytics, RevOps, GTM metric, pipeline, revenue, funnel, customer, or warehouse questions with Deepline. Triggers on phrases like 'query Snowflake', 'analyze pipeline', 'total ACV', 'break down by quarter', 'use the semantic layer', 'run a semantic query', or any use of snowflake_get_semantic_layer / snowflake_run_semantic_query. Skip prospecting, enrichment, contact finding, outbound, or personalization workflows; use deepline-gtm for those.
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, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.
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 an existing repository contains agents, prompts, workflows, playbooks, commands, or Skills and you need to identify which capabilities are worth converting into a ChatGPT/Codex Plugin.
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 and format publication-quality figures. Use for chart choice, color, scales, legends, captions, accessibility, and reproducible figure 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。
Email campaign strategy, automation, and optimization. Use when creating email sequences, improving deliverability, designing automation workflows, or optimizing email 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。
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
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-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.
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。