Top Agent Skills

Use when planning an ACM PODC campaign end to end — running the single annual cycle backward from the February deadline through abstract registration, lightweight double-blind review, the late-April notification, the May camera-ready, and the arXiv full version — and coordinating the regular-paper-vs-Brief-Announcement decision along the way.

Use when planning an ACM PODS project timeline across its multiple submission cycles per year, from venue fit through the EasyChair abstract-then-paper deadlines, the 48-hour rebuttal, the accept/reject/revision decision and shepherded revision round, PACMMOD-track publication, the arXiv full version, and presentation — with backward-planning offsets for a theory paper and honest handling of cycle choice.

Use when planning a POPL campaign calendar — backward-scheduling theory and mechanization from the July deadline, riding the October notification into the conditional-acceptance revision and artifact evaluation, landing the January conference, and retargeting across the SIGPLAN/PACMPL deadline wheel if July slips.

Use when planning a SODA (ACM-SIAM Symposium on Discrete Algorithms) campaign end to end — backward-planning to the July AoE deadline, SODA's seat in the theory calendar between STOC and FOCS deadlines, the September rebuttal and October decision, satellite deadlines (ALENEX, SOSA), and the January conference.

Use when planning a SOSP campaign across the annual cycle — back-scheduling from the spring abstract and paper deadlines, running the summer response and July notification, stacking the August camera-ready with artifact evaluation, and retargeting rejections across the SOSP/OSDI/EuroSys circuit.

Use when auditing an IEEE VIS full-paper submission for PCS readiness, covering the abstract-then-paper two-deadline structure under the VGTC society, the IEEE VGTC/TVCG 9+2 page budget, author-optional double-blind anonymization, the supplemental-material one-week window, and desk-reject triage before the AoE cutoff for a paper that will publish in IEEE TVCG.

Use when planning a VLDB project on the PVLDB rolling calendar, covering month-picking strategy, the volume-to-conference mapping and acceptance cutoff, budgeting a possible three-month revision, coordinating author caps across a group, and backward planning from the 25th-abstract / 1st-paper rhythm.

Use when planning a WACV campaign calendar across the two-round model, from track and round choice through the Round 1 submission, rebuttal, and Accept/Revise-and-Resubmit/Reject decision, into the Round 2 revision and decision, camera-ready, and the winter conference — with the branch points that a single-deadline plan misses.

Generate a Conventional Commits message from staged changes and wait for confirmation before committing. Use when the user asks to commit or generate a commit message.

Summarize each day's Git activity into a concise daily work log, for a single date or a range. Uses the current repository, optional configured work projects, or paths named in conversation; configuration is never required.

Query and display the current API provider balance and recent usage.

处理和分析用户上传的文件引用，提取关键信息并提供应用建议。适用于解析@符号引用、自然语言引用、提取短剧策划相关信息

智能截断文本，保持内容的完整性和语义连贯性。适用于长文本预处理、确保文本不超过指定长度限制

创作竖屏短剧剧本，包括宏观建构、剧本创作、精准优化、创意发想。适用于从零开始创作短剧、优化现有剧本、设计故事大纲和悬念钩子

梳理IP网络信息并进行多维度评估打分。适用于评估小说、剧本等IP的改编价值、分析市场潜力和创新属性

协调情节点戏剧功能分析流程，管理文本预处理、并行分析、结果整合。适用于长文本的情节点和戏剧功能分析、需要结构化分析报告的场景

使用联网搜索获取最新市场信息和行业动态。适用于获取竖屏短剧市场趋势、热门题材分析、成功案例研究

分析故事文本，提炼主要情节点并分析戏剧功能。适用于分析小说、剧本大纲、故事梗概等文本，识别关键转折点和情感节点

梳理故事主线，提炼并按发展阶段排列主要情节点。适用于快速掌握故事结构、制作大纲及剧本改编结构梳理

智能截断文本，保持内容完整性。适用于小说文本预处理、确保文本不超过指定长度限制

分析已播剧集，包括信息获取、拉片分析、五元素分析及联网搜索。适用于深度分析剧集创作技法、学习情节设计

专业评估故事大纲，从市场潜力、创新属性、内容亮点维度判断评分。适用于故事大纲质量评估、IP改编潜力判断、项目立项决策

专注于竖屏短剧知识库查询和信息检索，提供专业知识服务。适用于查询剧本桥段、高能情节、创作技巧、商业运营等专业知识

整合智能体输出结果为结构化最终输出，支持多种格式。适用于整合分析报告、生成结构化报告

整合多情点分析结果为综合报告，通过去重、分类、排序、总结生成高质量分析。适用于整合多分析源、生成统一报告

分析故事情节点，识别关键情节与转折点。适用于深度分析情节结构、评估情节发展有效性

基于故事文本分析人物特征，生成详细的人物小传。适用于深度理解故事人物设定、为演员塑造角色提供参考、为剧本创作建立人物档案

AI-era curl for agent web fetching, structure discovery, and deterministic structured extraction with token-budgeted output.

Drive mex (`mex-agent`), persistent project memory and code graphs for AI coding agents. One command scaffolds a living wiki, builds a deterministic code graph, and installs a project anchor file (CLAUDE.md, root AGENTS.md, .cursorrules, .windsurfrules, copilot-instructions.md, or .opencode/opencode.json) that your agent auto-loads as a standing rule document. Use when the user wants to `mex setup` a new project, build a symbol-grounded wiki, keep knowledge connected to implementation, route relevant context to agents, or run drift detection (`mex check`, `mex sync`). Triggers on: "mex setup", "project memory", "code graphs", "codebase documentation", "drift detection", "agent memory", "structured scaffolds", "architectural context", "living wiki", "project anchor file".

Create, validate, and consume Google's Open Knowledge Format (OKF) bundles — YAML-frontmatter Markdown files with type / title / description / resource / tags / timestamp fields for portable, interoperable AI-agent knowledge sharing. OKF formalizes the LLM-Wiki pattern into a vendor-neutral open specification so any producer can write and any agent can consume without translation. Routes: use `llm-wiki` for raw source capture + vault maintenance, `obsidian` for Obsidian-vault workflows, `graphify` for durable committed graphs, `scrapling` for web-content extraction into OKF docs. Triggers on: okf, open knowledge format, knowledge bundle, okf document, llm wiki standard, knowledge atom, agent context format, okf frontmatter, okf bundle, knowledge interoperability.

Packet-first BMAD/BMM front door for idea notes, product briefs, PRDs, architecture drafts, review feedback, existing repo state, and milestone pressure. Use when the user wants to know what BMAD phase or artifact comes next, or needs a portable BMAD entrypoint before routing review, execution slicing, runtime setup, or game-production work outward.

Drive Moli (`moli`), Lexmount's open-source headless browser for AI agents, built around on-demand rendering: real JavaScript, DOM, and CSS by default, with layout and pixels computed only when explicitly requested via `--layout`. Use when the user wants to fetch/extract a live JavaScript-rendered page as Markdown/HTML/JSON/semantic-tree, capture a screenshot or PDF, run a small bounded crawl, start a CDP/WebDriver automation server for Playwright/Puppeteer, replace a Chromium/ChromeDriver dependency, or diagnose readiness/network/frame issues on a rendered page. Triggers on: "moli fetch", "moli serve", "headless browser for agents", "on-demand rendering browser", "CDP server without Chrome", "structure-first web scraping", "Lexmount browser", "moli-webfetch", "moli-cdp-server".

Install, configure, and operate Strix for AI-driven application security testing. Use when you need to run authorized vulnerability scans against local codebases, GitHub repositories, staging URLs, domains, or CI pipelines; configure Docker and LLM providers; choose quick, standard, or deep scan depth; or pass authenticated testing instructions to Strix. Triggers on: strix, ai pentest, vulnerability scan cli, appsec scan, bug bounty automation, strix ci, strix docker, strix scan mode, strix instruction file, headless security scan.

Enterprise AI adoption platform for Claude Code — measures team usage via OpenTelemetry, syncs skills/MCP servers/hooks from a central dashboard, and delivers context-aware skill suggestions at prompt time. Solves the Intention-Action Gap: organizations that deploy Zeude see 3x adoption improvement (6% → 18%). Requires Supabase (config) and ClickHouse (analytics). Triggers on: zeude, ai adoption, claude code adoption, enterprise claude, opentelemetry claude, skill sync, team claude management.

Build fully customizable, agent-ready design systems with Astryx — Meta's production design system now open source. Ships 150+ React components built on StyleX with zero styling lock-in, component swizzling, brand theming, dark mode, and CLI tooling. Use when building component libraries, design systems, UI applications, design tokens, or when teams need consistent accessible components that AI agents can understand and extend. Triggers on: astryx, design system, component library, design tokens, react components, accessible components, astryx design, stylesheets, theme customization, component composition.

Route Firebase AI feature work into either direct app/client Firebase AI Logic SDK integration or a server-owned Genkit workflow. Use when a web, mobile, backend, or full-stack feature needs model calls, typed outputs, reusable flows, tools, retrieval, prompt files, evals, observability, or deployment. Choose client-ai-logic, flow-foundation, tool-and-agent, retrieval-and-prompt, evaluation-and-observability, deployment-runtime, or comparison-or-fallback; route Firebase platform/operator work to `firebase-cli` and broad framework comparisons to `survey`.

Search and download specific files/folders from GitHub repositories directly from terminal using ghgrab, without full clone. Covers install, interactive browsing, release asset download, and automation-safe usage patterns.

Run KADATH (Kernel for Agentic Darwinian Adaptation, Tooling, and Heredity), a Docker-based evolutionary kernel that turns a goal into a locked, Architect-authored benchmark, then evolves a population of smolagents-based coding agents across epochs: each agent runs in an isolated container, gets graded against frozen evidence, and the population is culled, mutated, and reproduced generation over generation until it converges on the best-performing agent framework for that goal. Use when the user wants to propose/approve/run a KADATH evolutionary run, check a run's status or live dashboard, pause/resume/continue a run, export the winning agent population, or understand its Architect/Grader/Tweaker/Birther pipeline, evidence-freezing, or genome lineage/memory model. Triggers on: "kadath", "kadath.sh", "evolve an agent", "Darwinian agent evolution", "agent population fitness benchmark", "smolagents evolutionary run", "kadath dashboard", "genome lineage", "epoch champions".

Run a bounded cross-platform landscape scan before planning or implementation. Use when the real job is researching what exists, how people work around it, which solutions repeat, or how platform/tooling patterns map before deciding what to build. Produce reusable `.survey/{slug}/` artifacts, validate the artifact contract, and route planning or execution outward only after the survey is done.

Generate structured PRD from conversation context without interviewing the user

Set up, run, and contribute to TokHub (github.com/yaojingang/TokHub) — an open-source AI API relay monitoring, recommendation, and OpenAI-compatible gateway system with L1/L2/L3 channel health probing, usage metering, alerts, audit, and Docker self-hosting. Use when the user asks about TokHub, "AI API 中转站监控", cloning/running the Go + React monorepo (TOKHUB_ROLE, sqlc, TimescaleDB, NATS), the L1/L2/L3 probe algorithm, the OpenAI-compatible `/gateway/v1/*` endpoint, or contributing a PR to TokHub. Do not use for connecting a running agent to a live TokHub instance's own API (that is covered by the project's own bundled `agent-skills/tokhub` skill inside the TokHub repo, not this one).

Drive Airship (`@airshiplabs/cli`), a CLI that puts a visual, pannable design canvas in front of a running dev server so an engineer can click an element, describe a change, and have Claude Code, Codex, or OpenCode edit the underlying source file directly — no plugin, no build config, nothing added to the project. Use when the user wants to launch a visual editor over a local dev server (`npx @airshiplabs/cli --target <port>`), pick or compare coding agents (`--agent claude|codex|opencode`) for live UI edits, sandbox agent edits with `--safe`, scaffold an `airship.config.json` (`airship init`), or diagnose a broken setup (`airship doctor`). Triggers on: "airship", "@airshiplabs/cli", "visual editor for my codebase", "click to edit UI", "design canvas over dev server", "airship doctor", "airship init", "pair Claude Code with a visual editor on localhost".

Drive Animato (github.com/otdnnc/Animato) as an API-key agent loop that turns a rigged .fbx/.gltf model plus a plain-text motion request into a baked animation: upload the model, build the bpy prompt, spend one LLM call with your own key, gate the generated script, run it headless, and verify the animated output. Use when the user wants text-to-animation for a 3D character, an unattended animation pipeline driven by a Gemini or OpenAI-compatible API key, or help operating a local Animato server. Triggers on: animato, text to animation, animate a rigged model, bpy animation script, blender headless keyframe, /api/chat animation, character motion from a prompt, GEMINI_API_KEY animation.

Enables persistent ultra-compressed technical communication; use for explicit brevity or token-reduction requests, except where fragments risk safety or clarity.

Lazyweb is the design-evidence skill for AI coding agents. Use it when designing, critiquing, or changing product UI — it provides real app screenshots, competitor references, best practices, quick examples, creative cross-category ideas, paywall optimization guidance, and mobile growth + monetization A/B test context. Routes to the right Lazyweb mode (design workflow, quick search, update, flowchart, or A/B test research) using MCP tools and the Lazyweb server at https://www.lazyweb.com/mcp. Use before designing any screen when you need design evidence instead of training-data vibes.

Work with the OpenCut open-source video editor repo (github.com/OpenCut-app/OpenCut) — clone/setup, run the web/desktop dev servers, understand the Rust/WASM compositor core, and follow current contribution focus areas. Use when the user asks about OpenCut, the "open-source CapCut alternative", opencut-classic, setting up the OpenCut monorepo (moon/proto or Bun/Docker), the OpenCut Rust core, or contributing a PR to OpenCut. Also triggers on "opencut.app", "new.opencut.app", and "opencut-app/opencut-classic".

Self-hosted AI gateway with one OpenAI-compatible endpoint for multi-provider LLM, embedding, image, and audio routing, automatic fallback, load balancing, and cost optimization.

AI-driven Game Development Studio (BMAD-GDS). Routes game projects through Pre-production, Design, Architecture, Production, and Game Testing phases using 6 specialized agents. Supports Unity, Unreal Engine, Godot, and custom engines.

Drive CodeBurn, a free open-source local-first CLI/TUI/web/menubar tool that reads the session files already on disk from 40 AI coding tools (Claude Code, Codex, Cursor, Gemini CLI, Grok, OpenCode, and more) and breaks down token usage and dollar cost by task, model, tool, and project. Use when the user wants to see where their AI coding spend went, find and fix token waste in a Claude Code / agent setup, cap a session's budget before it runs away, compare which model is actually worth its price, check whether AI spend shipped or was reverted, or wire live usage/savings data into an agent over MCP. Triggers on: "codeburn", "npx codeburn", "AI token usage", "AI coding cost", "where did my Claude spend go", "codeburn optimize", "codeburn guard", "codeburn compare models", "codeburn yield", "token waste in CLAUDE.md", "AI spend dashboard".

[devup-ui] Routing-first front door for Devup UI — a zero-runtime CSS-in-JS library whose Rust + WebAssembly preprocessor extracts every style at build time, so no styling JavaScript ships at runtime. Use to adopt/integrate Devup UI, wire the build-time plugin into Next.js/Vite/Rsbuild/Webpack/Bun, write styles with Box/css props or the styled-components-compatible styled() API, set up type-safe theming with devup.json, or migrate off styled-components/Emotion/Tailwind/Panda/vanilla-extract to zero runtime.

Investigates hard, flaky, or performance failures through a six-phase feedback-loop-first debugging process; use when quick targeted debugging is insufficient.

Systematic plan stress-testing through relentless one-question-at-a-time decision-tree interviewing

Build, test, run, and flash NightRun — a bare-metal, no_std Rust UEFI application that boots straight into a local LLM (Llama 3.2, Qwen3, or Granite 4.1) with no operating system underneath. Use when the user wants to build/flash a bootable NightRun USB or Raspberry Pi 5 SD image, convert a GGUF model into the `.nrm` container, run/debug the inference engine on the host or in QEMU/OVMF, or troubleshoot no_std kernel/tokenizer parity issues in the NightRun codebase. Triggers on: "nightrun", "boot into an LLM", "bare-metal LLM runtime", "UEFI LLM appliance", "nrconvert", "nrhost", "cargo xtask", "nrm model file", "flash a bootable LLM USB".

Route web-game auto-playtesting with WAI Play (waiterve/wai-play): decide whether the next move is a testability check, authoring or repairing the `GameFlowAgentAPI` bridge, running a real browser playtest, reading the five-dimension quality report, or unblocking a key node the agent cannot reach. Use when the user wants an AI agent to actually play their HTML5 / canvas / vibe-coded web game and return reproducible evidence, scores, and fix suggestions across the five supported types (survivor-like, arcade shooter, platformer, puzzle/card, visual novel). Triggers on: wai-play, WAI Play, auto-playtest, AI plays my game, web game testing agent, GameFlowAgentAPI, GameFlowIntegration, jumpToScenario, game quality score, playtest evidence. Route Unity/Unreal frame-time work to `game-performance-profiler`, engine build failures to `game-build-log-triage`, human playtest notes to `game-demo-feedback-triage`, and generic browser automation to `browser-harness`.

Get higher-level architectural perspective: maps modules, callers, dependencies using domain vocabulary

Turn raw ideas into one clear pre-planning concept artifact by choosing the right framing mode: problem framing, audience and value framing, concept shaping, game concept framing, or story packaging. Use when the user needs the next artifact before PRDs, sprint plans, launch execution, or game-production routing.

Run a reproduce → isolate → verify debugging workflow for concrete bugs, regressions, flaky failures, and environment-specific behavior. Use when the user already has a failing command, test, request, UI flow, or narrowed symptom and needs root-cause diagnosis or fix verification rather than raw log-line selection, broad test-policy design, PR review, or generic performance tuning.

Cross-runtime 2-stage pipeline for Claude Code, Codex/OMX, and Gemini/Antigravity/OMA: trace causal hypotheses, inject evidence into deep-interview style requirements crystallization, then hand off to the right runtime planner/executor.

Browse, group, relate, and selectively install the jeo-skills catalog through the lightweight `jeo-skill` CLI. Use when the user wants skills organized by web, infrastructure, game, creative media, CLI tools, AI/agents, engineering, research, business, or utilities; needs a frontend/backend/game-audio/game-VFX subcategory; wants overlapping skills connected instead of duplicated; or wants a category, bundle, or named skills installed without copying the full repository.

Route web-scraping work into the lightest workable Scrapling mode instead of defaulting to a browser. Use when the user needs HTML extraction, JS-rendered page retrieval, protected-target escalation, quick CLI scraping, agent-facing MCP access, or a larger crawl with Scrapling spiders. Triggers on: scrapling, scrape website, crawl site, adaptive scraping, selector drift, stealthy fetch, browser scraping, scrape to markdown, scrapling mcp, scrapling spider, research harvesting, literature scraping, paper metadata.