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Gerencie múltiplos agentes CLI locais via sessões tmux (iniciar/parar/monitorar/atribuir) com agendamento compatível com cron.

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Acelere a inferência de LLMs usando especulative decoding, múltiplas cabeças Medusa e técnicas de lookahead decoding. Use ao otimizar velocidade de inferência (aceleração de 1,5-3,6×), reduzir latência em aplicações em tempo real ou fazer deploy de modelos com recursos computacionais limitados. Cobre modelos draft, atenção em árvore, iteração de Jacobi, geração paralela de tokens e estratégias de deploy em produção.

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Publish a new Canvas Notebook release. Use this skill whenever the user asks to bump a version, prepare release notes, update CHANGELOG.md, create or push a git tag, publish a GitHub Release, deploy, or automate the release workflow. This skill covers the full release sequence for this repository: inspect commits/tags, push completed product-code commits first when authorized, confirm the pushed code has built on GitHub, choose the next calendar version, update package and CLI versions, update the changelog, run the required local build, commit, push the release commit, wait for the remote Build and Push worker on the pushed branch, tag, push the tag, and publish the release.

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[Process] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.

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[Testing] Use when you need to run tests locally and analyze the summary report.

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[Investigation] Use when quickly locating relevant files and affected areas across a large codebase.

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[Code Quality] Use to validate a spec artifact's decisions with the user across three contexts — a freshly-authored Feature Spec (idea-to-spec), an existing canonical spec before PBI decomposition (spec-to-pbi), or a refined idea + §8 test-specs (idea-to-pbi deep mode). Detects the context, walks every applicable validation category, and runs an exhaustive but budget-bounded blocking clarification gate so every non-obvious or conflicting decision is confirmed before the artifact drives downstream work.

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[Utilities] Use when applying structured problem-solving frameworks to complex, blocked, recurring, or uncertain decisions.

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[Utilities] Use when recording an engineering journal entry — capture decisions, blockers, lessons, or progress notes during work.

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[Git] Use when generating developer KPI, performance, contribution value, story point, man-day, or code-quality reports from local git commit history.

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[Document Processing] Use when you need to convert Microsoft Word ( DOCX) files to Markdown with GFM support (tables, images, code blocks).

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Normalizes company names, industries, domains, locations, and descriptions. Use when the user provides company records and wants consistent canonical fields with confidence and unresolved items flagged.

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Create or repair a project-local Inspector profile after INSPECTOR_PROFILE_REQUIRED, INSPECTOR_PROFILE_INCOMPLETE, INSPECTOR_PROFILE_INVALID, or PROFILE_AUTHORING_REQUIRED, using Unity component source, custom Inspector evidence, serialized surfaces, and validation diagnostics.

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Maintains repo-local action contracts and harness repositories where product CLI and MCP adapters stay thin over core libraries. Use when adopting or improving steward.yaml actions, capability-level adoption evidence, cold-start proof loops, probes, benchmarks, CLI/MCP/core parity, adapter refactors, packages/core boundaries, or sibling harness layout; use repo-quality-system-lifecycle first for general app/library/tool stewardship baselines.

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Use when designing or implementing states for AI agent tasks, background jobs, local model runs, queues, and notifications — one shared set of states, only legal transitions, and a recovery path out of every failure.

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Use when designing a user-initiated macOS emergency switch that helps the user save context before stopping, switching tasks, or leaving the computer — capture must be transparent, local-first, and easy to delete.

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Audit or update this Windows computer's fixed third-party Codex helper toolchain when the user requests local helper-tool maintenance. Always show the complete registered list and obtain confirmation for each update group; exclude Codex itself and general runtimes unless explicitly added.

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Project-level workflow for turning GitHub repositories, URLs, local product materials, or markdown drafts into WeChat Official Account articles and draft-box submissions. Use when the user asks to write, adapt, illustrate, verify, or publish a WeChat/公众号 article, especially from a GitHub repo or existing technical/product material.

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Route ordinary public-web information tasks to the correct Tavily CLI operation. Use when the task concerns publicly accessible webpages and needs web discovery, clean extraction, URL mapping, same-site crawling, or public-web-only research, but the exact Tavily operation is not yet clear or several operations must be sequenced. Prefer the specific leaf skill when intent is clear: tavily-search for discovery without a URL, tavily-extract for known URLs, tavily-map for URL/site-structure discovery, tavily-crawl for content from many pages on one site, tavily-research for cited multi-source synthesis, and tavily-dynamic-search for programmatic filtering with context isolation. Do not use for named social/community/media/career/code platforms, login or cookie-dependent content, platform-native discussions, or cross-platform collection; use Agent Reach there. Do not use for local files, Git operations, deployments, or ordinary code editing.

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Audit and safely clean local project repositories or workspaces with excessive generated files, old project residue, dirty Git status, stale worktrees, build caches, duplicate nested projects, confusing roots, or agent context/token waste. Use when the user asks to organize a repo, remove useless files, classify valid project files, clean untracked/deleted files, reduce disk usage, migrate from an old repo layout to a canonical project, or prepare a repository for future Codex/agent work.

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Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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Phase 4.5 hook by Phase-2-Quality-Fix sprint. 封装 mmdc/npx Mermaid CLI subprocess,把 LLM 输出的 `<div class="mermaid">...</div>` block 渲染为 PNG。Use AFTER cite-bound-content-generator (Phase 4 Turn 1) and BEFORE report-bundle-builder (Phase 5). Production smoke must fail closed if rendering fails.

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Use this skill when a user asks to polish, rewrite, humanize, de-AI, localize, or oralize Chinese writing, including short-video scripts, articles, social posts, speeches, newsletters, and personal-IP content. The skill diagnoses “AI味”, preserves the user's meaning, adapts to optional style samples, replaces translation-like phrasing with natural Chinese expression, and outputs a polished version plus concise revision notes.

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Use when audio already placed in a HyperFrames composition needs to be mixed: fade-in/fade-out, crossfade, track gain or volume, volume automation, ducking, a music bed that fights a voiceover (voiceover carve), effects on a track (EQ, compressor, limiter, gate, saturation, delay, reverb, chorus, phaser, bitcrush), automation envelopes drawn on a track's volume or any effect parameter, or one submix bus carrying a chain, a fader and an automation clock for several tracks at once (`<hf-audio-group>`). Don't use for sourcing or generating audio — finding BGM, SFX, or making a voiceover is `/media-use`. Don't use for clip timing or track layout, which is `/hyperframes-core`.

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。