Agent skills
Skills you can use with AI coding agents, indexed from public GitHub repositories.
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creating-copilot-packages
Use when creating GitHub Copilot instructions - provides repository-wide and path-specific formats, applyTo patterns, excludeAgent options, and natural language markdown style
pr-pm/prpm 102
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karen-repo-reviewer
Use when the user requests a repository review, code assessment, or honest evaluation of their codebase. Provides brutally honest AI-powered reviews with market-aware Karen Scores (0-100) analyzing over-engineering, completion honesty, and practical value. Available as GitHub Action or IDE tool.
pr-pm/prpm 102
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osgrep
Semantic code search using natural language queries. Use when users ask "where is X implemented", "how does Y work", "find the logic for Z", or need to locate code by concept rather than exact text. Returns file paths with line numbers and code snippets.
pr-pm/prpm 102
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aws-beanstalk-expert
Expert knowledge for deploying, managing, and troubleshooting AWS Elastic Beanstalk applications with production best practices
pr-pm/prpm 102
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github-actions-testing
Expert guidance for testing and validating GitHub Actions workflows before deployment - catches cache errors, path issues, monorepo dependencies, and service container problems that local testing misses
pr-pm/prpm 102
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creating-opencode-plugins
Use when creating OpenCode plugins that hook into command, file, LSP, message, permission, server, session, todo, tool, or TUI events - provides plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript event-driven modules
pr-pm/prpm 102
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human-writing
Write content that sounds natural, conversational, and authentically human - avoiding AI-generated patterns, corporate speak, and generic phrasing
pr-pm/prpm 102
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npm-trusted-publishing
Use when setting up npm publishing with GitHub Actions - provides trusted publishing with OIDC, provenance attestations, and monorepo configuration
pr-pm/prpm 102
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agent-builder
Use when creating, improving, or troubleshooting Claude Code subagents. Expert guidance on agent design, system prompts, tool access, model selection, and best practices for building specialized AI assistants.
pr-pm/prpm 102
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product-discovery
Validate whether a product idea is worth building before committing engineering investment. Use when someone says "should we build this", "validate this idea", "discovery", "run an experiment", "test this hypothesis", "what are the risks", "is this worth building", "feasibility check", "prototype plan", or when a team has a shaped feature or product idea and needs to assess risks and design experiments before building. Sits between product-thinker (should we?) and shaping-work (what exactly?) — this skill answers "will this actually work?" by identifying what you don't know, designing the cheapest way to find out, and defining evidence gates that justify (or kill) the investment. Also trigger when someone has a feature request and you sense high uncertainty — if the team is about to spend weeks building something nobody tested, this skill should intervene.
teambrilliant/dev-skills 3
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implementation-planning
Create technical implementation plans and architecture designs. Use when someone needs a detailed technical approach before coding begins — "create a plan", "plan this ticket", "how should we implement this", "technical design", "architect this", "design the approach", "plan the migration", "refactor plan", "how should we structure this", or when shaped work or a groomed ticket needs a concrete implementation strategy with phases, file changes, and verification steps.
teambrilliant/dev-skills 3
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qa-test
Browser-based QA verification after any implementation. Use when someone says "QA this", "test this in browser", "verify the feature", "qa test", "browser test", or after completing an /implement-change to verify acceptance criteria in a real browser. Opens Chrome via MCP, exercises each acceptance criterion, verifies via DOM snapshots, and reports pass/fail. The "closer" for every implementation — proof it works, not just that tests pass.
teambrilliant/dev-skills 3
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shaping-work
Shape rough ideas into clear, actionable work definitions. Use this skill whenever someone has an unstructured idea that needs to become a concrete work definition — feature requests, bug reports, PRDs, customer feedback, Slack threads, stakeholder asks, or vague "we should do X" statements. Trigger phrases include "shape this", "scope this", "write a PRD", "define this work", "turn this into a ticket", "flesh this out", "spec this out", "what should we build for X", "I have an idea for...", or any rough input that needs structure before implementation can begin.
teambrilliant/dev-skills 3
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implement-change
Execute code changes from an implementation plan. Use when someone says "implement this", "build this", "code this", "start building", "let's implement", "execute the plan", "make the changes", "do the work", or has an approved implementation plan ready for coding. Takes implementation plans and produces working code, phase by phase with verification.
teambrilliant/dev-skills 3
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product-primitives
Break down complex products, features, or systems into fundamental primitives and building blocks from a software creator's perspective. Use when starting a new application, designing a large feature, or needing to understand a complex system's moving parts before building. Trigger phrases: "break down X", "decompose this", "what are the primitives", "building blocks of Z", "map the architecture", "what are the moving parts", "analyze this system", or any situation where you need to identify the atomic, reusable capabilities that compose a system. Complements product-thinker (user perspective) with the builder's perspective (system-level connections).
teambrilliant/dev-skills 3
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loop-check
Assess what's needed to make feedback loops autonomous in a repo. Use when someone says "loop check", "what do I need to work autonomously", "check my feedback loops", "what's manual here", "what should I automate", "can an agent iterate here", or before starting work in an unfamiliar repo to understand what's missing for autonomous iteration. Also use when the user asks "what do you need to make this autonomous?" or describes a workflow they want to close the loop on. NOT for: full repo audits (use tap-audit), coding, test writing, or implementation.
teambrilliant/dev-skills 3
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product-thinker
Use for product decisions, user behavior analysis, and UX evaluation. Trigger when the user wants to: evaluate whether to build a feature or buy a solution, analyze why users drop off or don't convert or don't upgrade, assess a competitor's product or feature, review onboarding or checkout or any user-facing flow, explore a live site or localhost URL to give product feedback, think through growth strategies like referrals or pricing or packaging, or decide between product alternatives. The core signal is the user asking "should we?" or "is it worth?" or "why are users?" or "what do you think about [product/feature/flow]?" or asking you to look at a product and assess it. Also use alongside shaping-work when the user needs product thinking before defining work. NOT for: writing/fixing code, test authoring, PR review, database operations, CI/CD, or decomposing PRDs into tickets.
teambrilliant/dev-skills 3
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devkg-sparql
Query the Dev Knowledge Graph via SPARQL instead of grepping raw session files. Use this when asked about technologies, relationships between tools, session history, or cross-platform knowledge.
robertoshimizu/session-graph 102
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verify-setup
One-time setup for /verify. Auto-detects dev server and indexes the app.
opslane/verify 102
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verify
Verify frontend changes against spec acceptance criteria. Uses Playwright MCP for browser interaction.
opslane/verify 102
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toon
Token-Oriented Object Notation is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for LLM input as a drop-in, lossless representation of your existing JSON.
butttons/dora 103
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dora
Query codebase using `dora` CLI for code intelligence, symbol definitions, dependencies, and architectural analysis
butttons/dora 103
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evaluate
Evaluate the quality of any AI-generated artifact — visualizations, code, documents, conversations, or any skill output. Works in 3 phases: (1) Generate evaluation specs tailored to the artifact type, (2) Run comprehensive evaluation against those specs, (3) Produce a beautiful visual report using the /visualize skill. Use after any skill produces output, or invoke directly with /evaluate <file-or-context>. Supports evaluating: HTML visualizations, code projects, documents, agent conversations, slide decks, dashboards, or any artifact with quality dimensions.
careerhackeralex/visualize 103
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visualize
Create beautiful, self-contained HTML visualizations from any content or idea. Use for: slide decks, presentations, infographics, dashboards, flowcharts, diagrams, timelines, comparison tables, data visualizations, landing pages, one-pagers, org charts, mind maps, process flows, kanban boards, report summaries, or any visual that helps humans digest information faster. Trigger on requests like "visualize this," "make a deck," "create a slide," "build an infographic," "show me a dashboard," "make this visual," or any request to present information in a visual HTML format.
careerhackeralex/visualize 103