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概要と使いどころ
Use when working with Bitbucket Deep — bitbucket deep platform management covering repository inventory, Pipelines CI/CD monitoring, pull request analysis, branch permission auditing, deployment environment tracking, and workspace member management. Use when auditing repository configurations, investigating pipeline failures, reviewing branch restrictions, or managing workspace access.
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 with Circleci — circleCI pipeline and workflow management. Covers pipeline status, workflow analysis, job logs, credit usage monitoring, orb management, and project configuration. Use when checking CI status, investigating build failures, analyzing credit consumption, or managing CircleCI orbs.
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 with Azure Devops — azure DevOps comprehensive management. Covers pipelines, boards, repositories, artifacts, test plans, work item tracking, and release management. Use when checking pipeline status, managing work items, investigating build failures, or auditing Azure DevOps project configurations.
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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 read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
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。
DevSecOps IA — Expert en intégration sécurité dans les pipelines CI/CD (SAST, DAST, SCA, container scanning, compliance automation)
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。
Expert en CRM avancé (HubSpot, Salesforce, pipeline, automation, reporting, contexte DZ, intégrations, data quality)
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。
CRM Specialist IA — Expert en CRM (HubSpot, Salesforce, Supabase CRM, pipelines, automation).
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。
Data Engineer IA — Expert en pipelines de données (ETL, data lakes, dbt, Airflow, streaming). Construit des pipelines fiables, scalables et auditable.
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。
Expert en CI/CD pipeline v2 (GitHub Actions, GitLab CI, Jenkins, ArgoCD, progressive delivery)
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。
Expert en pipelines CI/CD (GitHub Actions, GitLab CI, Jenkins, ArgoCD, progressive delivery)
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 Workflow Automation IA — Expert en automatisation de workflows IA (n8n AI nodes, LangChain, Dify, Flowise, pipelines custom)
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。
GitHub Actions, GitLab CI, Jenkins, and automated deployment 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。
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
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 and implement a complete ML pipeline for: $ARGUMENTS
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。
backlog（atoms/pipeline/outputs）と skill ヘルス（usage/duration/unused）を統合分析し 1 レポートで提示する。kawai 氏型 analytics ループの起点。「次に何やる？」「atom 整理」「放置案件」「stale」「振り返り」「retro」「skill 使用統計」「unused skill」「atom-suggest」で発動。
Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload 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。
Use this skill when writing aggregation pipelines, grouping, joining collections, or optimizing slow MongoDB queries. Trigger words: 集計, aggregate, パイプライン, $group, $lookup, $match, 結合, グループ化, 遅いクエリ, explain.
Use this skill when setting up or modifying CI pipelines, GitHub Actions workflows, or automated checks (lint, typecheck, test, build) for this project. Trigger words: CI, GitHub Actions, ワークフロー, パイプライン, 自動テスト, lint チェック, typecheck, ビルド検証, .github/workflows.
Set up and run the full RLHF pipeline (SFT, reward model training, RL from reward model) using the Tinker API. Use when the user wants to do RLHF, train a reward model, or run the full preference-based RL 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。
bu skill covers integrating Static Application Security Testing (SAST) tools—CodeQL and Semgrep—into GitHub Actions CI/CD pipelines. It addresses configuring automated code scanning on pull requests and pushes, tuning rules to reduce false positives, uploading SARIF results to GitHub Advanced Security, and establishing quality gates that block merges when high-severity vulnerabilities are Detected.
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。
bu skill covers integrating OWASP ZAP (Zed Attack Proxy) for Dynamic Application Security Testing in CI/CD pipelines. It addresses configuring baseline, full, and API scans against running applications, interpreting ZAP Bul:ings, tuning scan policies, and establishing DAST quality gates in GitHub Actions and GitLab CI.
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。