Development Claude Skills
8 673 development skills from 759 authors. They write and change code. Half of them fit into 1 830 tokens or less — that is what one costs your context window when the agent loads it. 1 213 ship runnable scripts rather than instructions alone. 42 of them cannot work without an MCP server, most often rube. We also found 1 239 copies of these same skills sitting in other people's repositories — counted once here, not 1 239 times.
8 673 unique 759 authors 10 updated this month 1 368 from vendors
Deep architectural analysis of the current workspace using Google Antigravity (`agy`). Trigger when the user needs an architecture overview ("analyze this codebase with agy", "map dependencies with Google Antigravity"), is onboarding to unfamiliar code, exploring legacy systems, or hunting technical debt. Replaces the deprecated gemini-cli `codebase_investigator` workflow.
Deep architectural analysis using Gemini's codebase_investigator tool. Trigger when user needs architecture overview ("analyze this codebase", "map dependencies"), onboarding to unfamiliar code, understanding legacy systems, or identifying technical debt.
Codebase analysis via the Codex MCP server with a read-only sandbox. Trigger when user needs architecture overview ("analyze this codebase with Codex", "have Codex map dependencies"), onboarding to unfamiliar code, understanding legacy systems, or identifying technical debt.
Get Codex's review of an implementation plan before the user starts building — trigger when they want Codex (or a named GPT model like gpt-5.6) to review, critique, or pressure-test a plan. Applies to any plan-shaped artifact — a plan file, plan-mode plan, a migration/rearchitecture/integration/checkout write-up, or a doc describing how they intend to do something (pasted, at a repo path, or just described). Fire on any second-opinion phrasing — "poke holes in it", "sanity-check my plan", "run this past Codex", "get Codex's take", "flag the biggest risks", "look this over before I start", "did I miss edge cases, testing, or a rollback path?". The point is catching gaps, risks, missing steps, and better alternatives ahead of implementation. Do NOT use for reviewing already-written code or diffs (that's diff-review), mapping an existing codebase's architecture (codebase-analysis), or web/research questions.
GitHub issue management using GitHub CLI. Trigger when user wants to list issues ("show open issues"), view issue details ("view issue 456"), create issues ("create an issue", "file a bug"), or edit/close issues ("close issue", "add label to issue").
Stacked pull requests with the GitHub CLI `gh stack` extension. Trigger whenever the user wants to split a large branch or change into a chain of dependent PRs ("stack this into smaller PRs", "split this into a stack", "create a stacked PR", "break this feature into reviewable layers"), or manage an existing stack ("add a layer on top", "rebase my stack", "sync the stack after the bottom PR merged", "merge the stack"). Use this instead of the `pr` skill whenever the work involves more than one dependent PR or any `gh stack` command; use `pr` for single, standalone pull requests.
GitHub Actions workflow management using GitHub CLI. Trigger when user wants to check CI status ("check CI", "is the build passing"), view workflow runs ("show recent runs", "view action logs"), watch running workflows ("watch CI"), or rerun failed workflows ("rerun the build").
Fetch raw file content from GitHub URLs. Trigger when user shares a GitHub file link (https://github.com/user/repo/blob/main/file.go) and wants to view the source code. Avoids HTML/JS clutter from WebFetch.
Repository information using GitHub CLI. Trigger when user wants to view repository details ("show repo info", "what's this repo about"), list repositories ("list my repos", "show repos for user"), or check repository metadata ("stars", "languages", "topics").
Convex backend specialist. Use this agent for any code inside a `convex/` directory — function definitions, schemas, indexes, queries, mutations, actions, HTTP endpoints, cron jobs, file storage, auth wiring, and component installation. Knows the object-form function syntax, validator patterns, resource limits, and component ecosystem that generic Claude routinely gets wrong.
Watch for the next dev/prod error or request in a Convex app and react to it.
Convex code reviewer — security, auth, validators, performance, and pattern checks for code in a convex/ directory. Use to review or audit Convex functions before shipping.
Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.
>- Use this skill whenever a user asks to audit, curate, clean up, deduplicate, rationalize, or assess files used by an AI knowledge source. Require the user to upload copies of the source files, analyze those complete files locally, and use configured knowledge sources only to validate findings.
Pre-simulate how a defined set of role-based personas will react to an internal comms, launch, or enablement artefact before it ships. Use when the user asks to "run the persona panel", "pressure-test this comms against our personas", "QA this launch email/deck before it goes out", "how will each team react to this", or wants persona- and domain-level feedback on a broad internal artefact. Requires a personas file — bring your own (see references/personas.template.md). Do NOT use for 1:1 private comms, HR/performance matters, or legal/contractual language.
Textum PRD→Scaffold→Story workflow for Codex with low-noise outputs and gate checks.
Textum PRD→Scaffold→Story workflow for Codex with low-noise outputs and gate checks.
Guides developers through a human-supervised PDCA framework for AI code generation. Applies analysis, TDD, validation, and retrospection. Activates for sessions requiring systematic quality control.
RouterOS v7 domain knowledge for AI agents. Use when: working with MikroTik RouterOS, writing RouterOS CLI/script commands, calling RouterOS REST API, debugging why a Linux command fails on RouterOS, or when the user mentions MikroTik, RouterOS, CHR, or /ip /system /interface paths. Scope: RouterOS 7.x (long-term and newer) only — v6 is NOT covered and accuracy for v6 problems will be low.
RouterOS command tree introspection via /console/inspect API. Use when: building tools that parse RouterOS commands, generating API schemas from RouterOS, working with /console/inspect, mapping CLI commands to REST verbs, traversing the RouterOS command hierarchy, or when the user mentions inspect, command tree, RAML, or OpenAPI generation for RouterOS.
Ground RouterOS config/scripts/API code against a REAL router using quickchr (@tikoci/quickchr) — a CLI + Bun/TS library that downloads, boots, and manages MikroTik CHR VMs on QEMU. Use when: validating generated RouterOS config or scripts against real RouterOS before trusting them; spinning up a disposable CHR for REST/CLI/API iteration; writing integration/lab tests against CHR; needing host↔guest networking for a CHR (port-forward, L2/MNDP capture, guest→host UDP); driving an external RouterOS tool against a live CHR. For raw QEMU/CHR boot mechanics (VirtIO, UEFI vs SeaBIOS, acceleration) without quickchr, use routeros-qemu-chr instead.
Guides discovery and application of project-specific conventions including code patterns, naming, structure, and team practices. Use when exploring a codebase or implementing features to match existing patterns.
Reference for Claude Code's 6 Task Management tools — TaskCreate, TaskGet, TaskList, TaskUpdate (structured tracking) and TaskOutput, TaskStop (background execution). Covers tool parameters, status lifecycle, completion rules, dependency management, and conventions
Produce a structured codebase analysis report with architecture overview, critical files, patterns, and actionable recommendations. Use when asked to "analyze codebase", "explore codebase", "understand this codebase", "map the codebase", "give me an overview of this project", "what does this codebase do", "codebase report", "project analysis", "audit this codebase", or "how is this project structured".
Provides language-specific patterns for TypeScript, Python, and React including idioms, best practices, and common patterns. Use when implementing features in these languages.
Deep exploration and synthesis workflow using Agent Teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".
Reference for Claude Code Agent Teams — lifecycle, messaging, spawning, orchestration, and hooks
>- Reference for string algorithm patterns — KMP, Z-function, Rabin-Karp, Manacher's, string hashing, suffix arrays, and Aho-Corasick. Covers recognition signals, core ideas, Python templates, edge cases, and common mistakes for each technique.
Reference for dynamic programming patterns — recognition signals, constraint mapping, Python templates, edge cases, and common mistakes for 8 core DP techniques
>- Reference for advanced data structure patterns used in competitive programming and technical interviews. Covers heaps, monotonic stacks, tries, segment trees, Fenwick trees, stack-based parsing, and ordered sets with Python templates, recognition signals, and edge case guidance.
>- Reference patterns for graph algorithm problems including BFS, DFS, Dijkstra, topological sort, union-find, MST, Bellman-Ford, and bipartite checking. Provides recognition signals, Python templates, edge cases, and common mistakes for each technique. Use when solving problems involving graphs, trees, shortest paths, connectivity, or dependency ordering.
Commit staged changes with conventional commit message. Use when user says "commit changes", "commit this", "save my changes", or wants to create a git commit.
Prepare and execute a Python package release with verification steps. Use for releasing Python packages with uv and ruff.
Analyzes the Agent Alchemy plugin ecosystem to detect dependency issues across all plugin groups
Execute pending tasks in dependency order with wave-based concurrent execution via Agent Teams
Execute pending Claude Code Tasks in dependency order with wave-based concurrent execution and adaptive verification. Supports task group filtering and configurable parallelism. Use when user says "execute tasks", "run tasks", "start execution", "work on tasks", or wants to execute generated tasks autonomously.
Generate test files from acceptance criteria or existing code. Supports criteria-driven mode (from specs/tasks) and code-analysis mode (from source files). Auto-detects test framework and spawns test-writer agents for parallel generation.
Produce a structured codebase analysis report with architecture overview, critical files, patterns, and actionable recommendations. Use when asked to "analyze codebase", "explore codebase", "understand this codebase", "map the codebase", "give me an overview of this project", "what does this codebase do", "codebase report", "project analysis", "audit this codebase", or "how is this project structured".
Explores codebases to find relevant files, trace execution paths, and map architecture with team communication capabilities for collaborative analysis (converted from agent)
Provides language-specific patterns for TypeScript, Python, and React including idioms, best practices, and common patterns. Use when implementing features in these languages.
Deep exploration and synthesis workflow with dynamic planning and hub-and-spoke coordination. Use for deep analysis, deep understanding, or codebase investigation.
Guides discovery and application of project-specific conventions including code patterns, naming, structure, and team practices. Use when exploring a codebase or implementing features to match existing patterns.
Executes diagnostic investigation tasks to test debugging hypotheses. Runs tests, traces execution, checks git history, and reports evidence. (converted from agent)
Reviews git history and updates CHANGELOG.md with entries for [Unreleased] section (converted from agent)
Generates MkDocs-flavored or standard GitHub-flavored Markdown documentation from codebase analysis findings including API references, architecture guides, standalone markdown files, and change summaries (converted from agent)
Prepare and execute a Python package release with verification steps. Use for releasing Python packages.
Commit staged changes with conventional commit message. Use when user says "commit changes", "commit this", "save my changes", or wants to create a git commit.
Produce a structured codebase analysis report with architecture overview, critical files, patterns, and actionable recommendations. Use when asked to "analyze codebase", "explore codebase", "understand this codebase", "map the codebase", "give me an overview of this project", "what does this codebase do", "codebase report", "project analysis", "audit this codebase", or "how is this project structured".