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Rebalances context across Memory, Custom Instructions, knowledge files, and User Preferences in Claude Projects. Audits Memory for redundancy, staleness, and misplacement; prescribes optimization including Codification of stable Memory patterns into explicit User Preferences or Project CI rules. Use when user says "optimize my memory," "what should be in my memory," "trim my knowledge files," "reduce my context usage," "rebalance my project," "what should be in my preferences vs memory," or asks whether something belongs in Memory, a knowledge file, or User Preferences. Also trigger on symptom-phrased: "my project feels bloated," "Claude keeps forgetting things," "my context window keeps hitting limits." Activate whenever Memory-layer balance is the primary concern. Do NOT use for full Project audits (use rootnode-project-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or global-layer audits that don't touch Project Memory (use rootnode-global-audit if available).

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。

Tested reasoning approaches for Claude prompts — 18 variants across 6 categories (Analytical, Strategic, Creative, Technical, Research, Comparative). Use when the user wants a specific reasoning template — retrieving, reviewing, customizing, or combining reasoning instructions for a prompt. Trigger on: "give me the Root Cause Diagnosis approach," "show me the reasoning template for," "I need the Evidence Synthesis method," "show me analytical reasoning options," "combine reasoning approaches for," "show me all reasoning variants." Also use when a prompt produces shallow output and the fix is a reasoning upgrade. Provides 18 tested approaches for analytical, strategic, creative, technical, research, and comparison tasks. If the user is unsure which reasoning method fits, use rootnode-block-selection first if available. Do NOT use for evaluating existing prompts — use rootnode-prompt-validation if available. Do NOT use for project-level audits — use rootnode-project-audit if available.

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。

Cheminformatics toolkit for fine-grained molecular control. Parse and write SMILES, SDF, MOL and InChI; compute descriptors (MW, LogP, TPSA, QED, Bertz); build fingerprints (Morgan/ECFP, RDKit, MACCS, atom pair, torsion) and score Tanimoto, Dice or cosine similarity; run SMARTS substructure search and reaction SMARTS; generate 2D depictions and ETKDG 3D conformers; extract Murcko scaffolds and canonical hashes; control sanitization and stereochemistry directly. Also trigger on rdkit, Chem.MolFromSmiles, rdFingerprintGenerator, SDMolSupplier, SMARTS query, ETKDG, or FilterCatalog. For standard workflows with a simpler interface use the datamol skill, which wraps RDKit; use rdkit for advanced control, custom sanitization, and specialized algorithms.

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。

Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

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 a local coding agent (Codex, Claude Code, Cursor, or similar) needs to operate Multica through the authenticated `multica` CLI: reading or updating issues, comments, metadata, labels, custom properties, subscribers, projects, agents, squads, runtimes, repos, skills, autopilots, attachments, or workspace state; searching issues; inspecting or cancelling agent runs; replying to a Multica issue from an external agent; creating or triaging issues; checking linked pull requests; or safely handling Multica mention/status side effects without relying on the Multica hosted agent runtime.

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 a B2B SaaS founder needs to choose, prioritize, or rebalance customer acquisition channels — deciding outbound vs inbound, paid vs PLG, where to put marketing budget, or sanity-checking CAC, LTV:CAC, and payback. Also for channel-specific playbooks (cold email, LinkedIn, paid social, community, partnerships) and channel-mix audits. Not for closing tactics (use sales-skill) or copy (use copywriter-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。

Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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。

Implement a generative diffusion model (DDPM or score-based) with noise scheduling, U-Net architecture, training loop, and sampling procedures including DDIM acceleration. Use when building a generative model for image, audio, or molecular synthesis; implementing DDPM from a research paper; adding a custom noise schedule or conditioning mechanism; replacing a GAN-based generator with a diffusion alternative; or prototyping before scaling with production frameworks like diffusers.

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。

Establish Service Level Objectives (SLO), Service Level Indicators (SLI), and Service Level Agreements (SLA) with error budget tracking, burn rate alerts, and automated reporting using Prometheus and tools like Sloth or Pyrra. Use when defining reliability targets for customer-facing services, balancing feature velocity against system reliability through error budgets, migrating from arbitrary uptime goals to data-driven metrics, or implementing Site Reliability Engineering practices.

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 Shiny application UIs using bslib for theming, layout_columns for responsive grids, value boxes, cards, and custom CSS/SCSS. Covers page layouts, accessibility, and brand consistency. Use when building a new Shiny app UI from scratch, modernizing an existing app from fluidPage to bslib, applying brand theming, making a Shiny app responsive across screen sizes, or improving accessibility of a Shiny application.

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。

Compose 2D graphics programmatically using SVG generation, diagram layout algorithms, image compositing, and batch processing workflows. Use when generating diagrams, flowcharts, or infographics programmatically, creating reproducible scientific figures, automating production of badges or visual assets, building custom chart types not in standard libraries, or batch generating graphics with parameter variations.

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。

NestJS module structure, dependency injection, lifecycle, configuration, exception handling, and logging conventions. Apply when implementing or modifying NestJS backend code. Use this skill to: - Structure feature modules (one feature = one module). - Wire DI correctly (constructor injection, scopes, custom tokens). - Set up ConfigModule, ValidationPipe, exception filters in main.ts. - Use lifecycle hooks for init and graceful shutdown. - Pick the right exception class for each error case. Do NOT use this skill for: - Decorator-specific patterns (see decorator-patterns). - ORM/data access (see nest-data-layer). - GraphQL/WebSockets/Microservices (see nest-advanced). - Testing (see nest-testing).

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。

FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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 Native: Jest with jest-expo / react-native preset, React Testing Library Native, native module mocking, hook testing. Optional sections for Detox (native automation) and Maestro (declarative YAML e2e). Use this skill to: - Configure Jest preset based on workflow. - Write component tests with @testing-library/react-native. - Mock native modules (react-native-reanimated, expo-*, react-native-mmkv). - Test custom hooks via renderHook. - Set up Detox or Maestro for e2e (optional, requires native toolchain). Do NOT use this skill for: - General RN conventions (see rn-conventions). - Web-React testing (see react-plugin:react-testing — different jsdom setup). - Plain Node testing 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。

Use when implementing pool/slab/arena allocators, tuning jemalloc/mimalloc/tcmalloc, writing a Rust GlobalAlloc, or benchmarking allocator performance and fragmentation.

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 a custom bootloader, jumping to application code, relocating VTOR, or implementing DFU/USB firmware update on Cortex-M. Not for reset-to-main: use baremetal-startup.

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。

Set up agent context for a project. Analyzes tech stack, installs relevant skills from skills.sh, generates custom skills, and configures MCP servers. Use when starting new project, setting up AI context, or asking "set up project", "configure AI", "what skills do I need".

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。

Generate professional Agent Skills for AI agents. Creates complete skill packages with SKILL.md, references, scripts, and templates. Use when creating new skills, generating custom slash commands, or building reusable AI capabilities. Validates against Agent Skills specification.

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。

Run automated QA checks on Grafema VS Code extension via Playwright + MCP cross-validation. Use when: (1) user says "/qa" to start or resume QA session, (2) user wants to validate extension panels against graph data, (3) user wants to re-check previously found bugs after a fix, (4) user wants to run a custom QA task. Requires Docker code-server running.

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 these skills when you need to explore the database structure, discover schema objects like tables and graphs, and execute custom SQL queries to interact with your data.

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。

Write idiomatic application code with the ClickHouse Node.js client (`@clickhouse/client`). Use this skill whenever a user is *building* against the Node.js client - configuring the client, pinging, inserting rows in JSON or raw formats, selecting and parsing results, binding query parameters, managing sessions and temporary tables, working with data types or customizing JSON parsing. Do NOT use for browser/Web client 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。

Generate professional Agent Skills for AI agents. Creates complete skill packages with SKILL.md, references, scripts, and templates. Use when creating new skills, generating custom slash commands, or building reusable AI capabilities. Validates against Agent Skills specification.

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。