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概要と使いどころ
Debug CI failures in unfamiliar repo pipelines. Reads CI configs, fetches failure logs, classifies the failure type, and guides the user through diagnosing and fixing the issue. Use when CI fails on your PR in an OSS repo, when you don't understand a CI error, or when debugging GitHub Actions/CircleCI/other CI systems. Not for setting up CI from scratch — this is for debugging existing pipelines.
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。
Weekly feature launch announcement pipeline. Reads recent MRs, generates Slack announcement (with approval gate), then autonomously produces a blog post, LinkedIn posts (brand + personal), and a LinkedIn carousel. Use when: weekly roundup, feature launch announcement, what's new, launch pipeline, weekly announcement.
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 auditing CRM data quality, finding duplicate records, identifying stale leads, cleaning pipeline data, or improving data hygiene. Triggers: 'clean my CRM', 'audit CRM data', 'find duplicates', 'CRM hygiene', 'data quality check', 'stale leads', 'pipeline cleanup'.
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 scoring leads, qualifying prospects, prioritizing pipeline, deciding which accounts to pursue, or building a lead scoring model. Triggers: 'score this lead', 'qualify this prospect', 'is this a good lead', 'lead scoring', 'prioritize my pipeline', 'qualification framework'.
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 generating weekly GTM performance reports, summarizing pipeline activity, reviewing outreach metrics, or creating executive updates. Triggers: 'weekly report', 'GTM report', 'pipeline review', 'weekly update', 'outreach summary', 'Friday report'.
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 the user wants to smoke-test the evolve pipeline, test tools, or verify the plugin works end-to-end. Also use when the user says 'dry run', 'smoke test', or 'test pipeline'.
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 RocketRide to compose, run, observe, and deploy portable AI pipelines from an IDE or CLI across model providers, vector stores, and agent nodes.
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 structured prompt-injection attack and defense experiments against an LLM-integrated app before production by measuring attack success and testing detection or recovery pipelines.
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。
Orchestrates AWS data pipelines using @aws-sdk/client-s3 and @aws-sdk/client-sqs. Manages S3 object lifecycle with PutObjectCommand/GetObjectCommand, processes SQS message queues via ReceiveMessageCommand with long polling, and configures S3 event notifications to SQS for event-driven processing.
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。
Strategic sales leadership guidance for B2B SaaS and enterprise software companies. Covers sales strategy, team building, pipeline management, enterprise selling, discovery calls, demos, proposals, negotiations, and sales operations. Use when building sales teams, designing sales processes, improving win rates, or scaling revenue operations. Use for "sales strategy", "sales process", "pipeline review", "deal strategy", "sales hiring", "quota planning".
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 the ENTIRE tdmcp roadmap-to-1.0 to completion as a resumable, wave-by-wave campaign — Milestone 4 (generative-AI bridge wave), Milestone 5 (mixer scene arming), and the v1.0 consolidation gates G1–G6 — routing each class of work to the right existing sub-harness. Use whenever the user wants to "implement everything / all the milestones / the whole roadmap / finish the road to 1.0", run the next milestone, close the consolidation gates, or build a long autonomous campaign across M4/M5/G1–G6. ALSO use for every follow-up: continue/resume the roadmap campaign, run the next wave, re-run a failed wave, fold in QA results, check campaign status, re-prioritize, or scope to one milestone/gate. This is the CAMPAIGN layer ABOVE tdmcp-pipeline / tdmcp-feature-lead / tdmcp-backlog-campaign — it sequences across MULTIPLE sub-harnesses (tools, mixer, coverage, docs, bridge, recipes, submission), which the generic backlog-campaign does not. For a SINGLE feature use tdmcp-pipeline; for one tool-shaped backlog file use tdmcp-backlog-campaign. Simple questions can be answered 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。
Orchestrates the tdmcp feature team end-to-end: design/wireframe → build → integrate → QA → release. Use whenever the user wants to build, implement, develop, ship, or add one or more tdmcp features/tools/effects/controls/AI-prompts for TouchDesigner, or to run them through a coordinated design→develop→QA→deploy pipeline. Also use for follow-ups: re-run, continue, update, fix, or improve a feature/batch, ship the next version, or rebuild just one part of a previous run. Single feature or a whole 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。
Drive a whole tdmcp feature BACKLOG to completion as resumable, wave-by-wave releases — the campaign layer ABOVE tdmcp-pipeline/tdmcp-feature-lead. Use whenever the user wants to implement an ENTIRE backlog or discovery file (e.g. _workspace/discovery*/FEATURE_BACKLOG*.md), 'all the features', many features across multiple releases, or a long autonomous build campaign, AND for every follow-up: continue/resume the campaign, run the next wave, re-run a failed wave, fold in QA results, or check campaign status. Backed by a ledger.json so re-running is idempotent (skips shipped work, resumes interrupted work) and resilient (retry-once then quarantine-and-continue). For a SINGLE feature or one small batch, use tdmcp-pipeline instead; this skill is for the whole ledger.
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。
CI/CD pipeline and software-supply-chain exploitation: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, Gitea/Forgejo, self-hosted runners/agents, poisoned pipeline execution, artifact/cache abuse, dependency confusion, third-party Action trust, GitOps/registry poisoning, workload identity/OIDC, and emerging agentic CI/CD. Use this skill when the operator has access to a CI/CD system, source repository, build/deploy configuration, runner/agent, artifact/package/registry path, or software delivery trust chain.
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 scraping JavaScript-heavy pages or SPAs, crawling multiple URLs concurrently, extracting structured data with reusable CSS/JSON schemas, or building automated web data pipelines. Wraps the Crawl4AI library (`crwl` CLI and Python SDK) with schema-generation patterns for LLM-free extraction. Triggers on crawl4ai, crwl, scrape JS-heavy site, scrape SPA, headless browser scrape, schema-based extraction, batch crawl, sitemap crawl, web data pipeline. SKIP when a static HTML page can be read with `defuddle` / `fetch-web` — those are faster cold-start and don't need a browser.
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。
Comprehensive guide for building robust, secure, and efficient CI/CD pipelines using GitHub Actions. Use when the task involves `creating or editing GitHub Actions workflows`, `.github/workflows/*.yml`, `CI/CD pipelines`, `GitHub Actions best practices`, or `workflow optimization`.
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。
6-phase multi-agent pipeline with approval gates. Orchestrates Plan→Design→Implement→Review→Test→Deploy phases with explicit entry/exit criteria, phase-level agent assignment, and human-in-the-loop approval checkpoints.
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。
Automated health checks for framework artifacts modified during a pipeline. Validates hook syntax, security patterns (SE-01/SE-02), skill wiring, agent consistency, and routing correctness. Invoked as the final pipeline step when framework artifacts were created, modified, or deleted.
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。
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".
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。
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper from my experiments", "turn this idea and these results into a paper", "generate a conference submission", "run paper-orchestra on X", or otherwise wants the end-to-end paper-writing pipeline. Coordinates the outline-agent, plotting-agent, literature-review-agent, section-writing-agent, and content-refinement-agent 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。
Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod).
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 game sprites, 8-direction turnarounds, walk-cycle sprite sheets, SVG characters, ASCII art and animated mascots from images or descriptions, with September 2026 providers: Retro Diffusion (pixel art, 8-dir rotation, frame animation, free palette tools), fal genmedia (GPT Image 2.5, Nano Banana 2, Kling 3, Seedance 2.5, Wan 3, Hailuo 03). Deterministic utilities: feet-line frame alignment, palette-preserving Scale2x/3x/4x pixel scaler, atlas manifest v2 with states/fps/loop/anchor, Aseprite JSON, pipeline profiles. Triggers on sprite, pixel art, sprite sheet, spritesheet, turnaround, 8-direction, walk cycle, atlas manifest, frame alignment, pixel scaler, Retro Diffusion, genmedia, SVG character, ASCII art, mascot, game asset, sprite pipeline profile.
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。
Detect missing glyphs, icons, and HD variants by comparing registries against glyph mapping files, icon directories, and manifests. Reports gaps for skills, agents, and teams across all palettes.
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。
应用从 AI for Everyone 蒸馏出的「AI流水线 — 多组件串联的系统设计模式」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to ai-pipeline.
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。