Skills directory · page 70

Build a simulation-style video (agents, particles, 3D machines, fly-throughs) as one HTML page whose render(t) is a pure function of time, then capture it frame by frame to a 60 fps mp4 with sims/capture.mjs. Use when the idea is a simulation or a 3D object rather than motion graphics with text and UI.

Use ONLY when the user explicitly says: 'use the skill web-to-markdown ...' (or 'use a skill web-to-markdown ...'). Converts webpage URLs to clean Markdown by calling the local web2md CLI (Puppeteer + Readability), suitable for JS-rendered pages.

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

Use when planning, promoting or fixing a webinar or virtual event (registrations, show-up, follow-up, conversion). Scores each funnel stage with webinar_funnel_scorer.py to find where it leaks.

See what a website looks like (desktop and mobile screenshots) and break down its background — colors, gradients, images, video, canvas, ::before/::after pseudo-elements, overlays, and the color palette. Use when the user says "look at this website", "check how it looks", "what's the background on this site", "clone/copy this design", "grab the colors/gradient from this site", or gives a URL and asks about its visuals (also in Indonesian, such as "lihat website ini", "cek tampilan", "background website apa", "tiru desain").

Turn raw operational data into a weekly management report that answers exactly three questions - what changed, where is it concentrated, what needs a decision. Use when the user mentions weekly report, haftalık rapor, yönetim raporu, ops review, KPI summary, or asks to summarize operational data for managers. Differentiator - contract-first reporting with plain-language findings and driver decomposition, never a chart dump.

Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says "/cs-weekly-review". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.

Set up WhatsApp and read or send messages in the owner's personal account through wa. Use when asked to link or connect WhatsApp, find a WhatsApp chat or person, read a conversation, search messages, or send a message.

Converts VitePress/GFM wiki markdown to Azure DevOps Wiki-compatible format. Generates a Node.js build script that transforms Mermaid syntax, strips front matter, fixes links, and outputs ADO-compatible copies to dist/ado-wiki/.

Generates AGENTS.md files for repository folders — coding agent context files with build commands, testing instructions, code style, project structure, and boundaries. Only generates where AGENTS.md is missing.

Analyzes code repositories and generates hierarchical documentation structures with onboarding guides. Use when the user wants to create a wiki, generate documentation, map a codebase structure, or understand a project's architecture at a high level.

Analyzes git commit history and generates structured changelogs categorized by change type. Use when the user asks about recent changes, wants a changelog, or needs to understand what changed in the repository.

Use when an operation issue asks to ingest a captured `raw/` source into the LLM Wiki, or the user says "ingest <slug>". Create durable source, entity, concept, synthesis, index, and log pages; use paperclip-distill for Paperclip bundles.

Use when an LLM Wiki operation issue is a lint or health check. Audit for contradictions, orphans, weak provenance, broken links, missing concept pages, and index/log drift; return triage findings without auto-fixing.

Generates llms.txt and llms-full.txt files for LLM-friendly project documentation following the llms.txt specification. Use when the user wants to create LLM-readable summaries, llms.txt files, or make their wiki accessible to language models.

Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager. Use when the user wants onboarding documentation for a codebase.

Generates rich technical documentation pages with dark-mode Mermaid diagrams, source code citations, and first-principles depth. Use when writing documentation, generating wiki pages, creating technical deep-dives, or documenting specific components or systems.

Answers questions about a code repository using source file analysis. Use when the user asks a question about how something works, wants to understand a component, or needs help navigating the codebase.

Use when an LLM Wiki operation issue asks a question. Answer from wiki pages and raw sources with citations, name gaps plainly, and offer to file durable synthesis when the answer should compound.

Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.

Packages generated wiki Markdown into a VitePress static site with dark theme, dark-mode Mermaid diagrams with click-to-zoom, and production build output. Use when the user wants to create a browsable website from generated wiki pages.

E2E testing for Windows native desktop apps (WPF, WinForms, Win32/MFC, Qt) using pywinauto and Windows UI Automation.

E2E testing for Windows native desktop apps (WPF, WinForms, Win32/MFC, Qt) using pywinauto and Windows UI Automation. Use when writing E2E tests for a Windows native desktop app with pywinauto or UI Automation.

Produce low-fidelity black-and-white UI wireframes as SVGs or viewer pages. Use when asked to wireframe, sketch a screen, draft a layout, make a low-fi mockup, or publish wireframes.

This skill should be used when the user asks to "analyze network traffic with Wireshark", "capture packets for troubleshooting", "filter PCAP files", "follow TCP/UDP streams", "detect network anomalies", "investigate suspicious traffic", or "perform protocol analysis". It provides comprehensive techniques for network packet capture, filtering, and analysis using Wireshark.

Read a recorded coding-agent session (Pi, Claude Code, or Codex) from the Wisdom ledger to review what was asked, done, changed and claimed, or to debug tracked agent work. Two moves, glance and open, return recorded evidence in plain text.

Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI secrets, walking an unfamiliar third-party dashboard, or running a one-off migration or cutover. Don't invoke this for steps the agent can perform itself.

Comprehensive technology-agnostic prompt generator for documenting end-to-end application workflows. Automatically detects project architecture patterns, technology stacks, and data flow patterns to generate detailed implementation blueprints covering entry points, service layers, data access, error handling, and testing approaches across multiple technologies including .NET, Java/Spring, React, and microservices architectures.

Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility

Use when writing or running a Claude Code Workflow-tool script (.js in .claude/workflows/) for fan-out, pipeline, loop or judge-panel sub-agents. Gathers intent with workflow_intake.py, scaffolds with scaffold_workflow.py and validates with validate_workflow.py.

The workflow for AI-assisted development: complexity detection (LOW/MEDIUM/HIGH), problem-type routing, branch strategy across repositories, and the create - analyze - implement - archive flow for a spec. Use when: starting work on a spec, deciding how much analysis a task needs, planning a change that spans several projects, checking which branch belongs where. Do NOT use for: the spec files' own layout (use the spec-structure skill), how to run tests (that is the testing rule).

WorkorAI talent marketplace skill: candidate job search and employer hiring with white-box match explanations via the WorkorAI MCP server (https://workorai.com/mcp). Use when the user asks to find a job, apply to jobs, respond to employer invitations, or when an employer wants to post jobs, search and evaluate candidates, invite them, and review applicants.

Use when working in a persistent-slot workspace that uses docko. Covers slot claims, delegated teammate inheritance, and the rule that code edits inside slots/ require an active claim.

Audit active repositories, MCP servers, plugins, connectors, environment surfaces, and tool settings, then recommend the highest-value ECC-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up Claude Code or understanding what is actually available in their environment.

アクティブなリポジトリ、MCPサーバー、プラグイン、コネクター、環境サーフェス、ツールのセットアップを監査し、最も価値の高いECCネイティブスキル、フック、エージェント、オペレーターワークフローを推奨する。ユーザーがClaude Codeのセットアップを支援してほしい場合や、環境で実際に何が使えるかを理解したい場合に使用する。

Audit the active repo, MCP servers, plugins, connectors, env surfaces, and harness setup, then recommend the highest-value ECC-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up Claude Code or understanding what capabilities are actually available in their environment.

Inventory the git worktrees of the current repository, classify each one with evidence (merged, PR state, uncommitted or unpushed work, locked, stale, in use by a running session) and remove them one at a time or by group, always asking the user first and verifying every removal. Use when worktrees have piled up (for example one per Claude Code task or thread), when `git worktree list` is long, or when the user asks to clean up, prune or tidy worktrees.

Interactive guide for parallel development with Ghostty, git worktrees, and Lazygit. Use when setting up multi-task workflows or learning the worktree system.

Wind down a long-running session fast when the user must leave — freeze new work, preserve active subagents, secure finished work, and hand off the remainder via task-handoff.

Use when closing out a launched managed agent ('wrap up', 'what do I own now'). Inventories primitives with primitives_inventory.py, regenerates the overview with overview_page.py and suggests next upgrades with upgrade_suggester.py.

Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.

Write a coding standards document for a project using the coding styles from the file(s) and/or folder(s) passed as arguments in the prompt.

Interview the maintainer and write the product goals document (Goals, Non-goals, Users, How to change this file) that the agent-pr-pipeline skill needs before its first run, then check it. Use when a repo has no goals document, when the pipeline refuses with "goals document missing or incomplete", or when the user asks to write or redo the product goals. It never invents goals; it asks.

Create or revise agent skills. Use when adding a new skill file, renaming a skill, simplifying an existing skill, improving trigger descriptions, or deciding what belongs in a skill versus references, scripts, assets, or ordinary docs. This is the judgment layer — authoring principles and failure modes for manual, principle-level work; prefer skill-creator when the user wants end-to-end interactive creation with generated test harnesses or quantitative description optimization.

Draft and revise publishable prose per channel — technical blog, academic, X/Twitter, Reddit, Xiaohongshu, WeChat MP, tutorial, changelog, a-little-book notes — behind one shared layer of content-first rules, measurable self-checks, and an AI-smell audit. Use when the text has a destination and a reader whose decision it must move. Not for chat replies, commit messages, or any piece where the content is still missing and only the packaging is being polished.

Use when writing prose humans will read—documentation, commit messages, error messages, explanations, reports, or UI text. Applies Strunk's timeless rules for clearer, stronger, more professional writing.

Edit user-provided English text or files with surgical corrections to grammar, spelling, punctuation, and clearly awkward phrasing. Use for proofreading or making existing writing sound natural and casual while preserving the author's meaning and voice. Not for drafting from scratch or broad rewrites.

Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.

Use when you have a spec or requirements for a multi-step task, before touching code

Write an implementation plan that an agent with zero context for the codebase can execute without guessing: exact file paths, complete code, test commands with expected output, and bite-sized tasks of 2–5 minutes each. Use before any multi-step feature, complex requirement breakdown, or hand-off to a task-per-subagent executor. Not for setting outcomes and ambition (that is goal definition) and not for exploring whether the work should happen at all.