managing-claude-code-meta
MUST be loaded when setting up, installing, migrating, reviewing, auditing, or checking CLAUDE.md files in projects. Covers installing the promode CLAUDE.md into new projects, migrating existing CLAUDE.md content to READMEs (progressive disclosure), and auditing projects for conformance. Invoke PROACTIVELY when user mentions CLAUDE.md, project setup, agent configuration, or code meta files.
managing-skills
Install, update, list, and remove Claude Code skills. Supports GitHub repositories (user/repo), GitHub subdirectory URLs (github.com/user/repo/tree/branch/path), and .skill zip files. Use when user wants to install, add, download, update, sync, list, remove, uninstall, or delete skills.
code-to-image
Generate beautiful code snippet images with syntax highlighting. Use when users want to share code as an image, create code screenshots for social media, documentation, or presentations. Produces crisp, syntax-highlighted code with customizable themes, window chrome, and professional styling.
html-to-image
Generate crisp, high-quality images with perfect typography and precise geometric layouts using HTML/CSS. Use when creating social cards, diagrams, certificates, UI mockups, code screenshots, or any image requiring sharp text rendering, exact alignment, or vector-like precision. AI excels at designing symmetric, pixel-perfect layouts as HTML rather than generating raster images directly. Supports Tailwind CSS, Google Fonts, icon libraries, and any web-based design resource.
og-image
Generate Open Graph (OG) images and social media preview cards for websites, blog posts, and products. Use when creating social share images, Twitter/X cards, LinkedIn post images, or any visual preview that appears when sharing links on social platforms. Produces properly sized images (1200x630, 1200x675, etc.) with crisp typography and professional layouts.
website-screenshot
Capture screenshots of live websites and web pages via API. Use when users need to screenshot a URL, capture website previews, archive web pages, generate thumbnails of sites, or document web content. Ideal for monitoring, archiving, sharing website previews, or creating visual references of web pages.
founder-coach
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negotiation
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paul-graham
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strategy
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commit-jira
Create and execute Git commits with a required JIRA-style subject and required body. Use when the user asks to commit changes, write commit messages, or prepare a clean commit and expects a ticket-prefixed subject. Always run a pre-commit safety guard first, abort on log files or untracked high-risk binary extensions, then stage with `git add . -A`.
commit
Create and execute Git commits in Conventional Commits format with a required body. Use when the user asks to commit changes, write commit messages, or prepare a clean commit. Always run a pre-commit safety guard first, abort on log files or untracked high-risk binary extensions, then stage with `git add . -A`.
context-surfing
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intent-framed-agent
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
plan-interview
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self-improvement
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Codex ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Codex realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
simplify-and-harden
Post-completion self-review for coding agents that runs simplify, harden, and micro-documentation passes on non-trivial code changes. Use when: a coding task is complete in a general agent session and you want a bounded quality and security sweep before signaling done. For CI pipeline execution, use simplify-and-harden-ci.
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or the loop cap is reached. Use when: implementing features from a spec or plan, hardening existing code, fixing a batch of issues, or any multi-file task that benefits from a build-verify-fix cycle.
eval-creator-ci
[Beta] CI-only eval regression runner using gh-aw (GitHub Agentic Workflows). Runs all eval cases in .evals/ on a schedule or per-PR, reports pass/fail results, and can block merges on regressions. Also creates new eval cases from promoted patterns flagged by learning-aggregator-ci. Use when: you want automated regression testing of promoted rules in CI/headless pipelines. For interactive eval creation and runs, use eval-creator.
eval-creator
[Beta] Creates permanent eval cases from promoted learnings and runs regression checks against them. Turns failures into test cases that prevent silent regression. This is the outer loop's regress-test step. Use when a learning is promoted and has a clear pass/fail condition, or on cadence to verify promoted rules still hold.
learning-aggregator-ci
[Beta] CI-only learning aggregation workflow using gh-aw (GitHub Agentic Workflows). Scans .learnings/ files on a schedule, groups entries by pattern_key, identifies promotion-ready patterns, and posts a gap report as a PR or issue comment. Use when: you want automated cross-session pattern detection in CI/headless pipelines without interactive prompts. For interactive use, use learning-aggregator.
learning-aggregator
[Beta] Cross-session analysis of accumulated .learnings/ files. Reads all entries, groups by pattern_key, computes recurrence across sessions, and outputs ranked promotion candidates. This is the outer loop's inspect step — it turns raw learning data into actionable gap reports. Use on a regular cadence (weekly, before major tasks, or at session start for critical projects). Can be invoked manually or scheduled.
pre-flight-check
[Beta] Session-start scan that surfaces relevant learnings, recent errors, and eval status before work begins. Bridges the outer loop back into the inner loop by making accumulated knowledge visible at task start. Activated via SessionStart hook or manually before major tasks.
verify-gate
Runs project compile, test, and lint commands between implementation and quality review. Gates simplify-and-harden behind machine verification. If checks fail, routes back to implementation with diagnostics for a fix loop. If checks pass, signals ready for the quality pass. Use after any implementation work completes and before simplify-and-harden. Essential for the inner loop's verify step.
dx-data-navigator
Query Developer Experience (DX) data via the DX Data MCP server PostgreSQL database. Use this skill when analyzing developer productivity metrics, team performance, PR/code review metrics, deployment frequency, incident data, AI tool adoption, survey responses, DORA metrics, or any engineering analytics. Triggers on questions about DX scores, team comparisons, cycle times, code quality, developer sentiment, AI coding assistant adoption, sprint velocity, or engineering KPIs.
self-improvement-ci
CI-only self-improvement workflow using gh-aw (GitHub Agentic Workflows). Captures recurring failure patterns and quality signals from pull request checks, emits structured learning candidates, and proposes durable prevention rules without interactive prompts. Use when: you want automated learning capture in CI/headless pipelines.
simplify-and-harden-ci
CI-only Simplify & Harden workflow for pull requests using gh-aw (GitHub Agentic Workflows). Runs headless scan-and-report checks for simplify/harden/document, posts structured findings, and can block merges on critical or advisory classes. Use when: you want automated quality/security review in CI without interactive approvals.
skill-pipeline
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skill-tester-ci
Validates all CI skills in this repo. Checks Agent Skills spec compliance, gh-aw workflow compilation, permission correctness, and structural conventions. Use when CI skills have been added or modified and you want to verify they compile and conform before committing.
skill-tester
Validates all interactive skills in this repo against the Agent Skills spec, project conventions, and structural requirements. Runs quick_validate.py, checks line limits, verifies cross-references, and tests hook scripts. Use when skills have been added or modified and you want to verify everything passes before committing or submitting.
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
coding-tutor
Personalized coding tutorials that build on your existing knowledge and use your actual codebase for examples. Creates a persistent learning trail that compounds over time using the power of AI, spaced repetition and quizes.
agent-browser
Browser automation using Vercel's agent-browser CLI. Use when you need to interact with web pages, fill forms, take screenshots, or scrape data. Alternative to Playwright MCP - uses Bash commands with ref-based element selection. Triggers on "browse website", "fill form", "click button", "take screenshot", "scrape page", "web automation".
agent-native-architecture
Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
andrew-kane-gem-writer
This skill should be used when writing Ruby gems following Andrew Kane's proven patterns and philosophy. It applies when creating new Ruby gems, refactoring existing gems, designing gem APIs, or when clean, minimal, production-ready Ruby library code is needed. Triggers on requests like "create a gem", "write a Ruby library", "design a gem API", or mentions of Andrew Kane's style.
compound-docs
Capture solved problems as categorized documentation with YAML frontmatter for fast lookup
creating-agent-skills
Expert guidance for creating, writing, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.
dhh-rails-style
This skill should be used when writing Ruby and Rails code in DHH's distinctive 37signals style. It applies when writing Ruby code, Rails applications, creating models, controllers, or any Ruby file. Triggers on Ruby/Rails code generation, refactoring requests, code review, or when the user mentions DHH, 37signals, Basecamp, HEY, or Campfire style. Embodies REST purity, fat models, thin controllers, Current attributes, Hotwire patterns, and the "clarity over cleverness" philosophy.
dspy-ruby
This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications. Use this when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers (OpenAI, Anthropic, Gemini, Ollama), building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
every-style-editor
This skill should be used when reviewing or editing copy to ensure adherence to Every's style guide. It provides a systematic line-by-line review process for grammar, punctuation, mechanics, and style guide compliance.
file-todos
This skill should be used when managing the file-based todo tracking system in the todos/ directory. It provides workflows for creating todos, managing status and dependencies, conducting triage, and integrating with slash commands and code review processes.
frontend-design
This skill should be used when creating distinctive, production-grade frontend interfaces with high design quality. It applies when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
gemini-imagegen
This skill should be used when generating and editing images using the Gemini API (Nano Banana Pro). It applies when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.
git-worktree
This skill manages Git worktrees for isolated parallel development. It handles creating, listing, switching, and cleaning up worktrees with a simple interactive interface, following KISS principles.
rclone
Upload, sync, and manage files across cloud storage providers using rclone. Use when uploading files (images, videos, documents) to S3, Cloudflare R2, Backblaze B2, Google Drive, Dropbox, or any S3-compatible storage. Triggers on "upload to S3", "sync to cloud", "rclone", "backup files", "upload video/image to bucket", or requests to transfer files to remote storage.
hb-build
智能编译 ability_runtime 组件，通过检测修改的文件自动确定编译目标。支持多目标一起编译、自动重试判断、智能错误分析。触发关键词："编译"、"构建"、"compile"、"build"、"make"、"make file"。
ai-generated-business-code-review
Use when reviewing or scoring AI-generated business/application code quality in any language, especially when a numeric score, risk level, or must-fix checklist is requested, or when C++ code must comply with OpenHarmony C++ and security standards
ai-generated-ut-code-review
Use when reviewing or scoring AI-generated unit tests/UT code, especially when coverage, assertion effectiveness, or test quality is in question and a numeric score, risk level, or must-fix checklist is needed
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