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概要と使いどころ
Scan Python requirements and environments for known vulnerable or malicious packages before they move further through delivery or promotion workflows.
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 Retentioneering's Python toolkit, MCP server, and agent skills to run reproducible customer-journey and event-log analytics that agents can validate and cross-check.
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 CodeBurn locally or as an MCP server so agents can inspect token usage, cost, model mix, project spend, and budget waste across coding tools.
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。
Integrate Audiobookshelf's self-hosted audiobook and podcast server into AI agent workflows. Agents can manage libraries, track listening progress, search metadata, and automate podcast episode downloads through the comprehensive REST API.
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 GEO Optimizer from the CLI or MCP so agents can audit a site for AI search visibility, generate llms.txt/schema fixes, check bot access, and track citation readiness.
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 Lynis to run an on-host security audit and turn the findings into a prioritized hardening checklist for an agent or operator. Invoke it when a machine is about to become internet-facing, after base image changes, or whenever you need a quick read on hardening drift instead of a generic vulnerability scan.
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 deployment pipelines using the Argo Workflows Engine API and Argo CD ApplicationSet controller. Implements progressive delivery with Argo Rollouts canary and blue-green strategies.
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。
Rewrite recurring code patterns with syntax-aware matching so agents can run migration codemods more safely than plain regex search and replace.
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。
Load maintained action manuals for fragile websites so agents can execute known flows more reliably than generic browser prompting.
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。
Catch insensitive, inconsiderate writing with alex. This CLI tool and Node.js library scans Markdown and plain text for gender-biased, polarizing, or exclusionary language and suggests inclusive alternatives.
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 Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.
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 AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.
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。
Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.
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 Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.
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。
Professional content generation system with a GSD-style phased workflow, integrated SEO optimization (claude-seo), and anti-AI auditing (humanizer). USE THIS SKILL whenever the user asks to write, draft, create, generate, or produce ANY content — including blog posts, articles, LinkedIn posts, Facebook posts, Twitter/X tweets or threads, Instagram captions, email newsletters, email campaigns or sequences, landing pages, product pages, web pages, sales pages, sales funnels, case studies, testimonials, product descriptions, or SEO metadata. Also triggers on: "/writer:discuss", "/writer:plan", "/writer:execute", "/writer:verify", "/writer:ship", "/writer:next", "/writer:profile-create", "/writer:profile-view", "/writer:profile-edit", "/writer:status", "/writer:help", "/writer:update" — or any request to manage a writer profile or create marketing materials. ALWAYS use this skill even for quick, casual content requests like "write me a LinkedIn post about X" or "draft a tweet thread." The profile and phased workflow produce significantly better output than ad-hoc generation.
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。
Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.
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。
Plan and implement native RayTK (t3kt/raytk) raymarching/SDF toolkit integration into tdmcp — beyond the existing package-manager staging, into AI-driven RayTK operator (ROP) node graphs. Use whenever the user wants to build, plan, design, implement, continue, fix, QA, or ship RayTK support: create/wire RayTK ROPs, a RayTK operator knowledge catalog, a RayTK-native raymarch/SDF scene tool, RayTK recipes, or the RayTK setup/doctor flow — including follow-ups (re-run, continue, refresh after a new RayTK release, re-QA, docs). Also triggers on 'raytk', 'raymarching toolkit', 'SDF toolkit', 't3kt', 'montar um build para o raytk', 'integrar o raytk'. This owns RayTK integration specifically; use it before the generic tdmcp-pipeline for this feature. Simple questions about RayTK 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。
Consolidate the td-trend-scout reports under `_workspace/hype-scout/` into one prioritized HYPE_TOOL_BACKLOG.md — dedupe trends across surfaces, vet each candidate against the real tdmcp codebase (layer fit, operator coverage, bridge work), rank by Hype × Build-Ease, and emit a 'Ready for tdmcp-pipeline' top-5. Use when a td-hype-synthesizer agent is consolidating scouts at the end of the tdmcp-hype-scout harness.
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。
Implement ONE tdmcp tool from a spec — the canonical file pattern (Zod schema + …Impl + register… ToolRegistrar), a bridge Python payload via buildPayloadScript, and an offline msw unit test — touching only new files, never shared registries. Use when coding/implementing/writing a tdmcp tool, generator, effect, control, CHOP/TOP/SOP builder, or Python bridge work, especially when several features are built in parallel.
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。
Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.
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。
Scout ONE external surface of the TouchDesigner community for what's HYPED in 2025-2026 that could become a tdmcp tool. Use when a td-trend-scout sub-agent is assigned a surface (community-showcase, tutorials, generative-ai, hardware-interactive, or vfx-aesthetics) during the tdmcp-hype-scout harness — produces `_workspace/hype-scout/01_scout_<surface>.md` with cited trend candidates ranked by hype intensity, recency, and build-ease in tdmcp.
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 only when the user explicitly asks to fix and verify a validated or plausible security vulnerability. Do not use for ordinary bug fixes, correctness or design review findings, general validation, or full PR, commit, branch, patch, or repository scans.
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 supplies or imports existing security findings, vulnerability reports, or security/vulnerability Jira/Linear tickets from scanners, advisories, GitHub, Atlassian Rovo, Linear, or similar backlog sources and wants static repo-impact triage. Do not use for discovery, duplicate-bug triage, validation, or fixes.
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。