1万 件 ・ 関連度順
概要と使いどころ
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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。
Optimize embedding strategies for AI retrieval. TRIGGERS - Use when user needs help with ai-embedding-optimizer related tasks.
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。
Datadog provider for obz. Supports metrics (Datadog Query Language), logs (Datadog Log Query), and traces. Requires api-key and app-key configured under auth in config.yaml. Does not support metric labels, label-values, or series commands. This skill should be used when the user mentions "Datadog", "DD", "obz -p dd", Datadog Query Language, or needs to configure Datadog provider instances with regional endpoints.
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Makers 开发专家团后端工程师 Skill — Edge Functions (V8)、 Cloud Functions (Node.js/Go/Python)、Middleware、KV/Blob 存储开发。 触发词：后端 API、Edge Function、Cloud Function、Middleware、 KV 存储、Blob 存储、Express、FastAPI、Gin、鉴权、数据库。
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Scaffold Quarkus + LangChain4j projects end-to-end and add agentic components to existing ones — AI services, tools, agents and multi-agent workflows, RAG pipelines, MCP clients and servers (Model Context Protocol), guardrails, and embedding store setups. Use this whenever the user wants a new Quarkus + LangChain4j project or module created, or an agentic component added to an existing project in this stack, including a baseline pom.xml, application.properties, project layout, or starter classes.
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Display the current VCSDD pipeline status for the active (or specified) feature. Shows phase, mode, sprint count, open findings, pending proofs, and the traceability summary.
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Use this skill when writing Phase 1a behavioral specifications in VCSDD. Provides EARS format requirements writing, purity boundary analysis, edge case enumeration, and spec quality validation patterns.
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Run Phase 6 (convergence check) for the active VCSDD feature. Validates four-dimensional convergence: finding diminishment, finding specificity, criteria coverage, and duplicate detection.
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Display the full traceability chain for a VCSDD bead. Traverses the Chainlink graph from the given bead ID and shows all connected artifacts with status.
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 committing VCSDD pipeline artifacts to git. Provides conventional commit message format, phase tag patterns, and atomic staging strategies.
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Use this skill when creating or querying VCSDD Chainlink bead traceability. Provides bead creation patterns, chain traversal, and completeness 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。
Run the strict-mode sprint contract review after Phase 2c. Spawns a fresh vcsdd-adversary instance to review `contracts/sprint-N.md` and writes a PASS/FAIL verdict under `reviews/contracts/sprint-N/output/`.
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Use this skill when evaluating VCSDD grading dimensions during adversarial review. Provides concrete evaluation checklists for each of the 5 dimensions and convergence signal detection.
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Use this skill when running or interpreting VCSDD adversarial reviews. Provides calibration guidance for the Adversary agent, finding severity classification, and anti-leniency enforcement patterns.
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Use this skill when applying VCSDD to Rust projects. Provides Kani proof harness patterns, proptest strategies, cargo-fuzz integration, and cargo-mutants mutation testing guidance.
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。