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概要と使いどころ
Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning 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。
When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'video pipeline,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative.
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。
Required bmod metadata. Never invoke this skill.
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。
Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.
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。
Required bmod metadata. Never invoke this skill.
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 Agent Architect: Design agent systems: single vs multi-agent, tools and MCP, memory, guardrails, evals.
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。
Session state backend for WDS. Called by wrap, start, and handoff tools — never directly by users. Writes to progress/ in the project repo.
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 Trigger.dev tasks, schedules, and runs for cost optimization opportunities. Use when asked to reduce spend, optimize costs, audit usage, right-size machines, or review task efficiency. Combines static source analysis with live run analysis via the Trigger.dev MCP tools (list_runs, get_run_details, get_current_worker).
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。
Author and run a durable AI chat agent with chat.agent from @trigger.dev/sdk/ai: the per-turn run loop, why you MUST spread ...chat.toStreamTextOptions() first, returning a StreamTextResult vs calling chat.pipe(), the two server actions (chat.createStartSessionAction + auth.createPublicToken), and wiring useChat to useTriggerChatTransport. Load this when building, modifying, or debugging a chat backend (the agent task or its lifecycle hooks) or its React transport, when declaring typed tools or custom data parts, or when migrating a plain AI SDK streamText route to chat.agent.
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。
Environment-aware pre-flight check. Use when starting work in a new environment, switching machines, or when 'works on my machine' bugs surface. Validates OS, runtime versions, installed tools, port availability, env vars, and disk space BEFORE coding starts. Like sentinel but for the environment, not the code.
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 README Writing System — Craft a README that converts visitors to stars in <3 seconds. Proven structure from AFFiNE's 0→60K star journey: tagline engineering, first-screen law, section-by-section copywriting guide, Claude Code integration section, anti-patterns, and a pre-publish checklist. Use when you need to write or rewrite a specific README file. 🇨🇳 GitHub README 写作系统 — 打造 3 秒内把访客转化为 star 的 README。来自 AFFiNE 0→60K star 实战：tagline 工程、首屏法则、逐节文案指南、Claude Code 集成板块、反模式、发布前自检清单。需要写或改一个具体 README 文件时使用。 🇯🇵 GitHub README 作成システム — 3秒以内にビジターをスターに変えるREADMEを作る。AFFiNE 0→60Kスター実績から: タグライン設計、ファーストスクリーン法則、セクション別ライティングガイド、Claude Codeインテグレーション、アンチパターン、公開前チェックリスト。 🇰🇷 GitHub README 작성 시스템 — 3초 안에 방문자를 스타로 전환하는 README 작성법. AFFiNE 0→60K 스타 실전: 태그라인 설계, 첫 화면 법칙, 섹션별 카피라이팅 가이드, Claude Code 통합 섹션, 안티패턴, 게시 전 체크리스트. Triggers: "write README" | "README template" | "GitHub README" | "project description" | "tagline" | "open source README" | "README structure" | "README review" | "fix README" | "rewrite README" | "README写作" | "写README" | "README模板" | "项目介绍" | "开源项目文案" | "改README" | "README检查" | "README 구조" | "README 작성" | "README 검토" | "READMEの書き方" | "READMEレビュー"
Launch your AI product to global attention — the playbook behind Manus, Devin, and AFFiNE's breakout launches. Covers AI-specific GTM strategy, hype cycle management, waitlist tactics, and multi-market rollout for maximum day-one impact.
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。
First AI Product Launch — Zero to Live in 30 Days. Step-by-step guide for shipping your first AI product from idea validation to paying customers. Includes day-by-day timeline and real examples. By @WeiYipei.
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。
Reviews sqlx database code for compile-time query checking, connection pool management, migration patterns, and PostgreSQL-specific usage. Use when reviewing Rust code that uses sqlx, database queries, connection pools, or migrations. Covers offline mode, type mapping, and transaction 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。
Go concurrency patterns for high-throughput web applications including worker pools, rate limiting, race detection, and safe shared state management. Use when implementing background task processing, rate limiters, or concurrent request handling.
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。
Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.
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。
Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.
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。
Test kolu's remote-host features (multi-host / padi-remote) in a real browser against a real remote box — the ONLY full end-to-end harness for this surface (real ssh provision, no mock). Covers the isolated nix-run invocation (or just dev, which bakes the same agent source), ssh- provisioning a real remote box without touching production or another user's box, the chrome-devtools drive sequence for host-switch / split / code-browser / per-host-canvas, PID-exact teardown, and the reproduce- first rule for any bug the feature surfaces. Triggers on "test remote hosts", "test multi-host", "test padi-remote", "verify the host switch", "browser-test a remote host change", or before claiming a remote-host fix works.
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 Kolu client-side performance issues (memory leaks, high GPU, slow interactions) using chrome-devtools MCP + the Debug → Diagnostic info dialog + heap-snapshot analyzers. Triggers on "memory leak", "GPU memory", "tab bloat", "why is kolu slow", "debug performance", "zombie canvas", "WebGL leak", or any
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 one AI agent from another through kolu's terminals: spawn a Claude Code / Codex / opencode session in a PTY, prompt it, watch the screen, read the reply, prompt again. PRIMARY PATH: the kolu MCP tools (`lifecycle_create`/`lifecycle_sendInput`, `wait_outputSettled`/ `wait_agentState`, `screen_text`, the `terminals` resource). FALLBACK: the `kolu` CLI — same verbs, same discipline. Triggers on "drive another agent", "agent drives agent", "make Claude drive Codex", or wiring a loop where one coding agent supervises another.
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。
Produce visual PR evidence — a screenshot or video — whenever a change has on-screen impact. Tests are never a substitute; a change can be backend by cause and visible by effect. Capture via the project's e2e harness on a pu box, or by driving a live kolu with the chrome-devtools MCP; host on a GitHub release and post a `## Evidence` comment. Triggers on "post evidence", "screenshot the change", "record a video of this", "show it working", "prove it", or finishing any change whose effect is visible on screen.
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。
Pre-commit implementation analysis for Retail-Agentic-Commerce. Use before commits, after significant changes, or when validating Apps SDK tools, mock data, MCP communication, widget isolation, or implementation 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。
Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver 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。
Suggest whether Claude Code, Codex CLI, or Grok Build is a useful next collaborator, without launching a CLI or changing controls. Use when comparing the three CLIs, planning a handoff, or deciding whether an independent implementation or review would help.
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。