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概要と使いどころ
Rebuild the knowledge base shelf-index with hash-based change detection. Incremental by default — only re-extracts files whose content has changed since the last index. Use after ingesting new sources, after editing library files, or whenever the librarian agent reports a stale index.
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。
Check whether the knowledge base shelf-index is stale relative to the library files. Reports drifted entries without rebuilding. Designed to be invoked from environment validation or pre-push hooks as an opt-in check. Default mode is warning; strict mode fails with non-zero exit when any drift is detected.
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 a new source into the project knowledge base. Wraps the agent-knowledge-updater agent. Source can be a local file path, a URL, or pasted content. The updater reads the source, classifies it, identifies which existing library files it touches, makes surgical updates or creates new files, rebuilds the shelf-index, and appends to log.md.
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 agent-knowledge-updater over a batch of staged files in library/raw/. Tracks progress in .batch-progress.json for resume support. Sequential by default; --parallel <N> opt-in (max 5). Single shelf-index rebuild and one consolidated log.md entry at the end.
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。
Desktop GUI automation via OpenInterpreter — mouse, keyboard, screenshot, and OCR control for native macOS/Linux applications. Three modes: Library (Claude reasons, OI executes), OS subprocess (full autonomous computer use), and Local agent (Ollama, offline). This skill should be used when interacting with desktop apps that have no CLI or API, automating GUI workflows, reading screen content via OCR, or controlling mouse/keyboard.
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。
診斷並清理 macOS 磁碟空間，特別是「系統資料 / System Data」這類看不出內容的用量。涵蓋開發者工具殘骸（Xcode iOS DeviceSupport、Simulator runtime、DerivedData）、套件管理器快取（Homebrew、pnpm、pip、npm、cargo、go、uv）、容器與 VM 磁碟（Docker、podman、lima）、瀏覽器 profile、以及已移除 app 的孤兒 container。使用時機：(1) 使用者說磁碟快滿了、System Data 佔太多、想清空間 (2) 詢問某個資料夾或快取能不能刪 (3) 移除 app 後想清乾淨殘留檔案、完整解除安裝 (4) 想知道 Docker.raw、Group Containers、Library/Caches 這些是什麼、佔了多少。Also triggers in English — disk full, startup disk almost full, free up disk space, clean up Mac storage, what is System Data on my Mac, what is taking up space, safe to delete this cache, uninstall app leftovers, reclaim disk space, Docker.raw huge, Xcode taking too much space
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。
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medchem query language (MATCHRULE, HASALERT, HASPROP, HASGROUP) for filtering a library at scale. Also trigger on medchem, `import medchem as mc`, RuleFilters, NIBRFilters, CommonAlertsFilters, NamedCatalogs, QueryFilter, PAINS filtering, or structural alerts.
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。
Navigate make-on-demand catalogues — ZINC-22 through CartBlanche and Enamine REAL Space — to find compounds that can actually be ordered. Use this skill to look substances up by ZINC identifier or structure, understand tranche partitioning by heavy-atom count and logP, and choose between screening an enumerated subset and searching a combinatorial synthon space with a fragment-growing method such as V-SYNTHES. Also trigger on ZINC22, CartBlanche, Enamine REAL, make-on-demand, tangible library, synthon, tranche, giga-scale enumeration, or ultra-large virtual screening.
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。
Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability 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。
Scaffold project: clone repo, install real library. Only invoke when explicitly requested by the user or by the yoink orchestrator.
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。
Phase 2: Generate and discover tests, validate against real library. Only invoke when explicitly requested by the user or by the yoink orchestrator.
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。
Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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。
Extract the conceptual essence of a repository as skills, agents, and teams — the project's roles, procedures, and coordination patterns expressed as agentskills.io-standard definitions. Reads an arbitrary codebase and produces generalized definitions that capture WHAT the project does and WHO operates it, without replicating HOW it does it. Use when onboarding to a new codebase and wanting to understand its conceptual architecture, when bootstrapping an agentic system from an existing project, when studying a project's organizational DNA for cross-pollination, or when creating a skill/agent/team library inspired by a reference implementation.
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Extract data from web pages using the scrapling Python library — select the appropriate fetcher tier (HTTP, stealth Chromium, or full browser automation) based on target site defenses, configure headless browsing, and extract structured data with CSS selectors. Use when WebFetch is insufficient for JS-rendered pages, anti-bot-protected sites, or structured multi-element extraction requiring DOM traversal.
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。
Evolve an existing team composition by refining its structure in-place or creating a specialized variant. Covers assessing the current team against template and coordination patterns, gathering evolution requirements, choosing scope (adjust members, change coordination pattern, split/merge teams), applying changes to the team file and CONFIG block, updating version metadata, and synchronizing the registry and cross-references. Use when a team's member roster is outdated, coordination pattern no longer fits, user feedback reveals workflow gaps, a specialized variant is needed alongside the original, or agents have been added or removed from the library affecting team composition.
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。
Create a new agent definition file following the agent-almanac agent template and registry conventions. Covers persona design, tool selection, skill assignment, model choice, frontmatter schema, required sections, registry integration, and discovery symlink verification. Use when adding a new specialized agent to the library, defining a persona for a Claude Code subagent, or creating a domain-specific assistant with curated skills and tools.
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。
Create a new SKILL.md file following the Agent Skills open standard (agentskills.io). Covers frontmatter schema, section structure, writing effective procedures with Expected/On failure pairs, validation checklists, cross-referencing, and registry integration. Use when codifying a repeatable procedure for agents, adding a new capability to the skills library, converting a guide or runbook into agent-consumable format, or standardizing a workflow across projects or teams.
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。
Create a GitHub release with proper tagging, release notes, and optional build artifacts. Covers semantic versioning, changelog generation, and GitHub CLI usage. Use when marking a stable version of software for distribution, publishing a new library or application version, creating release notes for stakeholders, or distributing build artifacts (binaries, tarballs).
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。
Create R-based pictogram glyphs for skill, agent, or team icons in the visualization layer. Covers concept sketching, ggplot2 layer composition using the primitives library, color strategy, registration in the appropriate glyph mapping file and manifest, rendering via the build pipeline, and visual verification of the neon-glow output. Use when a new entity has been added and needs a visual icon for the force-graph visualization, an existing glyph needs replacement, or when batch-creating glyphs for a new domain.
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。
Testing React SPAs: React Testing Library + Vitest/Jest for components and hooks, msw for network mocks, Playwright/Cypress for e2e. Query priority, user events, async assertions, mocking patterns. Use this skill to: - Pick the right runner (Vitest vs Jest) and setup. - Write component tests with RTL using accessible queries. - Test custom hooks via renderHook. - Mock network with msw at the boundary. - Set up Playwright or Cypress for end-to-end coverage. Do NOT use this skill for: - General React conventions (see react-conventions). - State management testing patterns specific to a store lib (see react-state-management). - Form-specific testing (apply react-forms patterns inside test).
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。
Testing Angular 18-21: TestBed, component harnesses (@angular/cdk/testing), Karma+Jasmine (default historical) vs Jest (jest-preset-angular, modern), Angular Testing Library (RTL-style). HttpClient mocking via HttpTestingController. NgRx Effects testing. Cypress / Playwright e2e. Use this skill to: - Detect runner (Karma+Jasmine vs Jest) and configure correctly. - Write component tests with TestBed. - Use component harnesses for Material / custom UI components. - Mock HttpClient via provideHttpClientTesting + HttpTestingController. - Test signal-based inputs with componentRef.setInput(). - Test NgRx Effects with provideMockActions. Do NOT use this skill for: - General Angular conventions (see angular-conventions). - Routing patterns broadly (see angular-routing — covers testing routes briefly). - Form patterns broadly (see angular-forms).
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。
Application and library conventions for plain Python projects (no web framework): src/ package layout, CLI tooling (argparse/click/typer), configuration management (pydantic-settings), logging setup, module design, entry points, and packaging conventions. Activated automatically by python-plugin/stack.md as a convention skill for the development phase. Use this skill to: - Organise Python application code in a src/ layout with proper package structure. - Build CLI tools with argparse, click, or typer and register them as console scripts. - Manage configuration from environment variables using pydantic-settings. - Set up structured logging for production-grade applications. - Package a Python project correctly with pyproject.toml. Do NOT use this skill for: - Language idioms (type hints, dataclasses, enums, match/case) — see python-foundation:python-conventions. - Package manager commands (ruff, mypy, pip/poetry/uv) — see python-foundation:python-tooling. - Testing patterns — see python-foundation:pytest-testing. - Web framework patterns (Django/FastAPI/Flask) — see those framework plugin skills.
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。
Testing strategies for Next.js: Server Components, Client Components, Server Actions, Route Handlers, end-to-end with Playwright. Vitest/Jest configuration, React Testing Library patterns, msw for network mocks. Use this skill to: - Pick the right test layer for what you're testing. - Configure Vitest or Jest for Next.js. - Test Server Components without running them in isolation. - Test Server Actions and Route Handlers as pure functions. - Set up Playwright for e2e and integration of RSC. Do NOT use this skill for: - General Next.js conventions (see nextjs-conventions). - RSC vs Client model (see server-component-patterns). - Plain Node.js test patterns (see nodejs-plugin equivalents).
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。