1万 件 ・ 関連度順
概要と使いどころ
Add a new learning to the agent learnings file. Use when the user says /add-learning, asks to record a lesson, or when an implementation produced a reusable insight worth preserving for future 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。
Use when a user says "add a ty diagnostic", "write this new ty diagnostic", "change a ty error message", "review ty diagnostics", or asks to add, update, or review ty checks, diagnostic messages, subdiagnostics, or concise output behavior.
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。
Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.
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。
Adds new visual examples to the tdmcp prompt cookbook documentation — both the English and Portuguese versions. Use when asked to: add more cookbook examples, create visual examples for the docs, show more surprising things you can do, add entries to the prompt cookbook, expand the cookbook, or any request to extend the cookbook with new prompts and results. Runs a 3-phase pipeline: curate candidates → write EN → write PT.
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。
Restructure and enrich the tdmcp documentation site for artists — categorized/collapsible sidebar (EN+PT), interactive goal-based guide home, new step-by-step tutorials, copy-prompt/level-badge components. Use whenever the user asks to make the docs more interactive, easier to navigate, better organized, add categories/subcategories, add tutorials, improve docs UX/IA, or for follow-ups: re-run, continue, fix QA findings, add one more tutorial, re-organize a section, or update the docs structure. NOT for syncing docs with a release (tdmcp-docs-roadmap-update) or cookbook entries (tdmcp-cookbook-examples).
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 adding support for a new model architecture to imp, porting a model family, or debugging a model that loads but produces wrong output - "add support for <model>", "new arch", loader detection, chat template, tokenizer parity, RoPE variant, "outputs garbage", "prompt-blind", "digits scrambled", "NaN logits", "describes a different picture", "does it fit in VRAM". Do NOT use for kernel performance (sm120-cuda-expert) or quant-format questions (quant-formats).
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 on any core for first-run planning intake — Phase 0 runs the vendored BMAD-METHOD planning shelf, shared by every core, and Phase 1 (mining `04-prd.md` into intent records plus the Add-ons/sync-and-remote-entities decision) is full-stack-app's and pwa-app's shared procedure — identical mechanics, a different decision at step 5/8. Phase 0 also defines compressed intake, the path a user's explicit "just build it" choice takes on full-stack-app, pwa-app, authored, and copywriting cores: one batched round of questions in place of the shelf, writing a thinned archive at the same paths so Phase 1 (or, on copywriting, that core's own mining step), `ux-planner`, and the Re-entry pass all keep their documented source. Also use for the Re-entry pass, which mines new scope into additional intents without re-running the shelf, on any core with a module axis to add an intent to (full-stack-app, pwa-app, authored) — landing-page has none, so its own new-scope path runs through `hedgehog-landing-loop`'s Correction Protocol instead. Invoked by the `planner` agent, which decides the path; don't run standalone. landing-page runs this skill's Phase 0 on first run, then mines the same archive through `hedgehog-landing-loop`'s own planning-intake section, that core's counterpart to this skill's Phase 1. copywriting runs this skill's Phase 0 the same way (full or compressed), then mines the same archive through `hedgehog-copywriting-loop`'s own planning-intake section into a what/audience/register brief — no module axis, so its own new-scope path runs through that loop's Correction Protocol rather than the Re-entry pass below. An authored core runs this skill's Phase 0, then `hedgehog-core-design`, then this skill's Phase 1 mining against the designed layer sequence. A brownfield adoption (`hedgehog-adopt`) never runs this skill's shelf at all — the drivers BMAD elicits are already settled facts of a repo that already exists.
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 a full RED-GREEN-REFACTOR TDD workflow for a feature. Accepts a feature description, task ID, or spec section as input. Presents a plan for confirmation then runs autonomously through all TDD phases.
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。
Coordinates DDQ completion with standardized responses, data room preparation, and reference call management for prospective LPs. Use when responding to LP DD, completing DDQs, or managing investor onboarding processes.
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。
Evaluates middle-market lending environment with competition analysis, spread trends, and deal structure evolution. Use when analyzing middle-market lending, tracking competitive dynamics, or assessing market conditions.
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。
Manage project settings in sdd/sdd-settings.yaml including component settings that drive scaffolding.
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 adding new wsh commands to Wave Terminal. Use when implementing new CLI commands, adding command-line functionality, or extending the wsh command interface.
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 adding new RPC calls to Wave Terminal. Use when implementing new RPC commands, adding server-client communication methods, or extending the RPC interface with new functionality.
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。
Design combinational logic circuits from a functional specification through gate-level implementation. Covers AND, OR, NOT, XOR, NAND, NOR gates; NAND/NOR universality conversions; and standard building blocks including multiplexers, decoders, half/full adders, and ripple-carry adders. Use when translating a Boolean function or truth table into a hardware-realizable gate network and verifying it by exhaustive simulation.
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 a new puzzle type across all 10+ pipeline integration points in jigsawR. Creates the core puzzle module, wires it into the unified pipeline (generation, positioning, rendering, adjacency), adds ggpuzzle geom/stat layers, updates DESCRIPTION and config.yml, extends the Shiny app, and creates a comprehensive test suite. Use when adding a completely new puzzle type to the package or following the 10-point integration checklist to ensure nothing is missed end-to-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。
Address PR review comments and loop until CI green and all comments resolved. TRIGGER when user asks to address comments, fix PR feedback, respond to reviewers, or babysit/monitor a PR.
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。
RFDB server becomes unresponsive (60s+ timeouts) during enricher addEdges write storms. Use when: (1) RFDB CPU spikes to 60-90% after addEdges calls; (2) subsequent reads time out at 60s; (3) server stays catatonic for 5-15 min after a write storm; (4) Tokio main thread parked (write-lock contention). Root cause: add_edges() calls maybe_auto_flush() which has a memory-pressure path that fires on NODE count — not edge count — and holds the exclusive write lock during disk I/O. Fix: separate maybe_auto_flush_edges() uses byte-limit only.
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 to write a Grafema batch-mode plugin that reads the graph and filesystem, then writes new edges/nodes directly to RFDB. Use when: (1) need to add project-specific edges that generic analyzers can't produce, (2) need to read config files (JSON/YAML) and trace values into code, (3) implementing framework-specific resolvers (Django settings, Express routes, pipeline configs). Covers: grafema.config.yaml plugin config, RFDB field naming (src/dst NOT source/target), addEdges/addNodes API, RFDBServerBackend connection pattern.
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。
Adapt torch_npu.profiler interface for AI frameworks and training/inference code on Ascend NPU. Use this skill whenever users need to integrate profiling, add performance data collection, or instrument PyTorch code with torch_npu.profiler on Huawei Ascend NPU platforms. Triggers on: "torch_npu profiler", "NPU profiling", "Ascend profiler", "profiler adaptation", "profile training", "profile inference", "add profiler to framework", "性能采集", "profiling 数据", "昇腾 profiler", and any request to add performance monitoring to Ascend NPU 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。
Create new marketing skills for the mktg playbook. Use when the agent needs to add a new capability, someone says 'create a skill', 'new skill', 'add a marketing skill', 'extend the playbook', 'I need a skill for X', 'build a skill', 'make a skill for Y', or 'add capability for Z'. Also use when someone wants to capture a marketing workflow they just did into a reusable skill, or when they say 'turn this into a skill'. Reads the skill contract, generates SKILL.md with correct frontmatter and structure, creates the directory, and reminds the agent to register in the manifest.
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。
Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript 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。
Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.
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 for every coding task. Enforce strict TDD workflow: activate Serena, investigate first, clarify+confirm requirements, write per-task REQUIREMENTS.md in .requirements/<datetime>_<feature_name>/, verify APIs via web search, then implement in tiny test-verified steps.
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。