282 件 ・ 関連度順
概要と使いどころ
Use when extracting specific data points from large agent output transcripts, kaizen analysis reports, or JSONL session files — tool timings, query counts, error summaries, or any structured facts — without loading raw data into orchestrator context. Activates when the orchestrator needs targeted facts from large files and context pollution must be avoided.
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 content generation with OpenAI and Claude, callAIWithPrompt usage, prompt storage in app_settings, structured outputs, response format validation, multi-criteria scoring, rate limiting, JSON schema, and AI API best practices. Use when generating content, creating prompts, scoring articles, or working with OpenAI/Claude APIs.
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。
Security review and implementation guidance for Odoo website, portal, public controllers, JSON routes, QWeb output, and browser-facing assets. Use when exposing Odoo data over HTTP, adding portal/public pages or forms, using sudo, handling tokens, rendering HTML, or reviewing web attack surfaces.
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。
Uses boto3 and the AWS IAM Access Analyzer API to enumerate all roles, policies, and users, then flags permission combinations that could allow privilege escalation to AdministratorAccess. Outputs findings mapped to MITRE ATT&CK TA0004 with remediation steps and least-privilege replacement policy JSON.
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。
audiowaveform is a BBC open-source C++ CLI tool that generates waveform data from MP3, WAV, FLAC, Ogg Vorbis, and Opus audio files. It outputs binary or JSON waveform data and renders PNG waveform images at configurable zoom levels.
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。
Extracts structured text, metadata, and embedded objects from PDFs, Office documents, and 1000+ file formats using the Apache Tika REST API. Outputs clean Markdown or JSON with XMP metadata preservation.
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 benchmarking, profiling, or A/B-testing CUDA kernels or end-to-end perf in the imp inference engine on RTX 5090 (sm_120), including refreshing tests/perf_baseline.json or publishing numbers to docs/BENCHMARKS.md and the README. Triggers on "benchmark kernel", "profile cuda", "ncu", "nsys", "kernel timing", "kernel sum", "occupancy", "bandwidth bound", "compute bound", "roofline", "perf baseline", "is this regression real", "decode dropped", "aggregate throughput", "two-image A/B", "prefill kernel A/B". Do NOT use for writing/optimizing kernel code (sm120-cuda-expert) or output-quality checks (check-degeneration).
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 on or testing imp-server and its HTTP APIs - OpenAI/Anthropic endpoints, /v1/chat/completions, /v1/messages, /v1/responses, SSE streaming, tool calling, json_schema/regex/GBNF constrained decoding, thinking/reasoning_content, cache_control/prefix cache, model loading and swapping, request priority, X-Request-Id, OTLP tracing / traceparent / Jaeger, /metrics, container env (IMP_SET), "server returns 404/garbage", API compliance. Do NOT use for CLI-only inference (imp-cli), kernel work (sm120-cuda-expert), or pure output-quality checks (check-degeneration).
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-first security scanning with Medusa. 3,000+ detection patterns covering AI/ML, agents, MCP, RAG, prompt injection, and traditional SAST vulnerabilities. Wraps Medusa CLI with SARIF/JSON parsing, structured finding output, OWASP mapping, and remediation 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。
Interact with 80+ websites and desktop apps from the CLI via Chrome session reuse — zero LLM cost, deterministic JSON/YAML/CSV output. Covers Twitter/X, Reddit, YouTube, HN, Instagram, Spotify, Amazon, LinkedIn, and more; also automates browser actions and registers local CLI tools. Triggers on 'opencli', 'CLI for website', 'scrape without API', 'browser automation', 'operate command'.
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 CLI command using Commander.js with options, action handler, three output modes (human-readable, quiet, JSON), and optional ceremony variant. Covers command naming, option design, shared context patterns, error handling, and integration testing. Use when adding a command to an existing Commander.js CLI, designing a new CLI tool from scratch, or standardizing command structure across a multi-command 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。
Design a generative recipe DSL so agents emit small validated JSON documents and ONE audited interpreter renders them all — data, not code. Covers the N-artifacts threshold for choosing a DSL over bespoke code, designing roles as style slots so a single recipe renders unchanged in every output style, the validator gate (compile + bounds + real render in all styles, plus a --fast mode for corpus-wide sweeps), hand-authored seed recipes, and the emergent conventions that keep a corpus of hundreds consistent without per-item steering. Use when many similar artifacts (icons, status badges, diagram nodes, test fixtures) must be generated by agents, when per-item bespoke code is multiplying unreviewable code paths, or when a whole corpus must be re-rendered cheaply into new styles.
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。
Statically enforce a privacy/redaction boundary on an artifact tree before publication, the way an AST checker enforces a code contract. Builds a two-tier gate — a shape-based deny-list (regex over identifier shapes) plus a structure-aware tier that parses JSON/HTML/SVG/Markdown/code and asserts sensitive content cannot appear in meaningful positions — with a strict exit-code contract, label-only output that never echoes the patterns themselves, and false-positive narrowing instead of suppressions. Use when gating a public mirror, a disclosure draft, a wire-capture export, or any artifact that must pass a leak check before it ships, and when a plain line-grep is too coarse to trust.
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。
Diagnose and fix RFDB data disappearing after compaction, where rfdb-server reports a tiny node count (e.g. "15 nodes") on startup despite manifest_index.json showing hundreds of thousands of nodes and segment files existing on disk. Use when: (1) rfdb-server logs "Default database: N nodes" with N orders of magnitude smaller than recent analysis output, (2) /api/stats returns only the most recent commit's nodes, (3) the issue appears AFTER a compaction event (manifest with l1_node_segments populated and node_segments: []), (4) subsequent commits dropped L1 references. Root cause: ManifestStore::create_manifest() initializes l1_node_segments/l1_edge_segments to Vec::new() instead of cloning from current manifest, so any commit AFTER compaction silently orphans all L1 data even though segment .seg files still exist on disk. Compaction injects L1 fields explicitly after create_manifest, but regular commits do not.
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 branded 试界TryWorld AI-knowledge videos in the fixed "Paper Algorithm" (纸上算法) style with HyperFrames. Input is a Chinese voiceover script and optional images; output is a 16:9 horizontal video with Azure YunxiNeural (真·云希) voiceover, word-synced captions, ink/paper animations and unified transitions, an always-visible anti-counterfeit red seal, plus horizontal (4:3) and vertical (3:4) cover images and platform-optimized titles for Bilibili/Douyin/Xiaohongshu. Brand values are theme-driven (themes/paper-algorithm.json by default; custom brand themes supported — see references/theme-guide.md). Use when the user provides an AI 科普/教程/内容解读/知识分享 script and wants a TryWorld/试界 branded video, asks for the 纸上算法/Paper Algorithm style, wants a consistent script-to-video pipeline with covers and titles, or wants this pipeline with their own brand theme.
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。
Aggregate DiT inference outputs into per-dimension VBench metrics via AISBench. Run after the quant-tuning-evaluate DiT workflow produces a directory of mp4s and the user supplies a VBench-1.0-mini JSON + pre-populated cache dir. Single mode (Standard).
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。
Analyze Ascend NPU collective communication profiling data with a DB-first workflow. Use when the user provides `cluster_analysis_output/cluster_analysis.db`, rank-level `analysis.db`, rank-level `ascend_pytorch_profiler_{rank_id}.db`, together with `profiler_info.json`, and asks about HCCL or hcom communication cost, collective communication TOP ops, wait time, slow rank/straggler, Notify Wait, bandwidth, retry, relay, SDMA/RDMA/HCCS links, communication matrix, or Ascend communication fault patterns.
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 Upstash CLI (`upstash`) from a terminal, shell script, or CI job when no Upstash MCP tools are in the session. Do not load this skill when Upstash MCP tools are available (the Upstash plugin registers mcp.upstash.com) - they already cover creating, listing, renaming, and deleting Redis databases, running Redis commands, usage stats, backups, Vector and Search indexes, QStash schedules and messages, Blob buckets, and Box, so call them directly. Use this skill for shell work the MCP does not do - a CI step or provisioning script that needs `upstash` commands with JSON output, piping results into other commands, `upstash auth login` and API-key setup, team and member management, changing plans, regions, TLS, eviction, auto-upgrade, or budgets, minting temporary S3 credentials for a Blob bucket, connecting a coding agent to Upstash with `upstash setup` (the Upstash plugin, or the remote MCP server plus skills), or when the user explicitly asks for the CLI or for managing Upstash without the console.
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 an end-to-end GEO visibility measurement through a real AI interface, persist the captures, and return a portable JSON run artifact plus optional PDF/dashboard outputs. Use automatically on an explicit request to measure a brand's AI-search visibility, and as a composable data-collection step inside another agent workflow; the user should not have to launch the pipeline or dashboard manually.
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。
Curate Gaia bot crawler branches into real-skill catalog entries and clean up processed bot/* branches. Use when the user says: "process bot branches", "curate the crawl output", "clean up bot proposals", "run bot curation", "merge the crawler results", "review bot/* branches", "ingest crawler data", or explicitly types /gaia-bot-curate. Also triggers when the user asks to review pending `bot/*` branches, turn crawler JSON proposals into registry nodes, decide which crawled skills to accept vs reject, or batch-add skills from an automated crawl run. This skill handles the full pipeline: fetch remote bot branches, triage proposals, run `gaia dev` mutations, validate, open a PR, and delete consumed bot branches.
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。
Chanjing AI creation Open API client: submit image/video tasks, poll task status, list and get tasks, and optionally download outputs when the user runs download_result.py. Reads and writes local credentials.json; calls open-api.chanjing.cc.
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。
Prepares Go applications to work effectively with AI coding agents. Use when setting up a new Go project or retrofitting an existing one to ensure: structured JSON logging (slog/Zap/ZeroLog/Logrus), machine-readable command output, thorough golangci-lint configuration, non-interactive CLI design with --yes flags, structured error handling with meaningful exit codes, proper context.Context propagation, graceful shutdown, health check endpoints, and a standardized Makefile. Triggers when a user asks to make their Go app "agent-ready," "AI-friendly," wants to improve agent tooling/observability in a Go project, or needs to audit an existing Go project against agent-readiness best 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。
Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".
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。
Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.
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。