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Found 7,014 Skills
Initialize the Platonic Coding system for any project. Scaffolds specs infrastructure (.platonic.yml, RFC templates, impl guide directory) and scans existing codebases to recover missing conceptual and architecture design specs as Draft RFCs. Use when adopting platonic coding for a new or existing project.
Provides structural context for downstream review and refactoring workflows. Use when before architecture reviews to understand file organization, exploring unfamiliar codebases to map structure, estimating scope for refactoring or migration. Do not use when general code exploration - use the Explore agent. DO NOT use when: searching for specific patterns - use Grep directly.
Neon serverless Postgres with autoscaling, instant database branching, and zero-downtime deployments. Use when building serverless applications, implementing database branching for dev/staging, or deploying with Vercel/Netlify.
File and directory operations using Claude Code built-in tools — replaces the Filesystem MCP server. Maps all 11 MCP tools to native equivalents: Read, Write, Edit, Glob, Grep, and Bash. Covers file reading with line ranges, parallel reads, pattern-based file search, regex content search, directory listing, tree traversal, move/copy/rename, and metadata inspection. Trigger phrases: "read this file", "write to file", "create a file", "edit file", "find files matching", "search for text in files", "list directory", "show directory tree", "move file", "rename file", "copy file", "file info", "find all Python files", "search codebase for". Use this skill when performing file operations, navigating codebases, or managing directories.
Use when the user asks about grid trading, ETH/USDC bot, automated trading on Base, grid bot status, trade history, PnL report, or mentions running/stopping/monitoring the grid bot. Covers: grid tick execution, start/stop daemon, status/report/history, market analysis, deposit tracking, retry failed trades. Do NOT use for manual token swaps or DeFi lending — use strategy-auto-rebalance for yield optimization.
Conduct deep codebase research and produce a written report. Use when the user says "Research ...", "start a research for", "deeply investigate", or "fully understand how X works". Do not use for quick questions or simple code lookups.
Generate or regenerate ONBOARDING.md to help new contributors understand a codebase. Use when the user asks to 'create onboarding docs', 'generate ONBOARDING.md', 'document this project for new developers', 'write onboarding documentation', 'vonboard', 'vonboarding', 'prepare this repo for a new contributor', 'refresh the onboarding doc', or 'update ONBOARDING.md'. Also use when someone needs to onboard a new team member and wants a written artifact, or when a codebase lacks onboarding documentation and the user wants to generate one.
AI-Native Issue-Driven development workflow. From GitHub Issue to merged PR: parse issue, explore codebase, design technical plan, execute with agent team, create PR, and cleanup. Use when a user wants to implement a GitHub Issue end-to-end: `/issue-flow #123` or `/issue-flow` to pick from open issues.
Manages persistent Knowledge Graph for specifications. Caches agent discoveries and codebase analysis to remember findings across sessions. Validates task dependencies, stores patterns, components, and APIs to avoid redundant exploration. Use when: you need to cache analysis results, remember findings, reuse previous discoveries, look up what we found, spec-to-tasks needs to persist codebase analysis, task-implementation needs to validate contracts, or any command needs to query existing patterns/components/APIs.
Answer questions against the knowledge base wiki. Use when the user asks a question about their collected knowledge, wants to explore connections between topics, says "what do I know about X", or wants to search their wiki.
Progressively gather requirements through automated codebase discovery and yes/no questions, then generate a comprehensive requirements spec. Use when starting a new feature, planning a build, or when you need structured requirements before implementation.
Systematic evidence-based debugging using runtime logs. Generates hypotheses, instruments code with NDJSON logs, guides reproduction, analyzes log evidence, and iterates until root cause is proven with cited log lines. Use when the user reports a bug, unexpected behavior, or asks to debug an issue.