Development Claude Skills
8 676 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 1 172 copies of these same skills sitting in other people's repositories — counted once here, not 1 172 times.
8 676 unique 759 authors 5 242 updated this month 1 369 from vendors
| Fidel API integration. Manage Persons, Organizations, Deals, Leads, Projects, Activities and more. Use when the user wants to interact with Fidel API data.
| Firebase Admin SDK integration. Manage data, records, and automate workflows. Use when the user wants to interact with Firebase Admin SDK data.
| Firmalyzer IoTVAS API integration. Manage Organizations. Use when the user wants to interact with Firmalyzer IoTVAS API data.
| Function integration. Manage Deals, Persons, Organizations, Leads, Projects, Pipelines and more. Use when the user wants to interact with Function data.
| Gender API integration. Manage Persons. Use when the user wants to interact with Gender API data.
| GitHub Actions integration. Manage data, records, and automate workflows. Use when the user wants to interact with GitHub Actions data.
| Github integration. Manage project management and ticketing data, records, and workflows. Use when the user wants to interact with Github data.
| Google Cloud API Gateway integration. Manage data, records, and automate workflows. Use when the user wants to interact with Google Cloud API Gateway data.
| Google Cloud Healthcare API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Google Cloud Healthcare API data.
| Human API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Human API data.
| IBM API Connect integration. Manage data, records, and automate workflows. Use when the user wants to interact with IBM API Connect data.
| Implisense API integration. Manage Organizations. Use when the user wants to interact with Implisense API data.
| Intellexer API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Intellexer API data.
| Knot API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Knot API data.
| LastPass Enterprise API integration. Manage data, records, and automate workflows. Use when the user wants to interact with LastPass Enterprise API data.
| Meteomatics Weather API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Meteomatics Weather API data.
| Microsoft Graph API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Microsoft Graph API data.
| Mocean API integration. Manage Organizations, Users, Filters. Use when the user wants to interact with Mocean API data.
| Nordic API Gateway integration. Manage data, records, and automate workflows. Use when the user wants to interact with Nordic API Gateway data.
| Npm integration. Manage data, records, and automate workflows. Use when the user wants to interact with Npm data.
| OpenWeather API integration. Manage Weathers, Locations. Use when the user wants to interact with OpenWeather API data.
| RedCircle API integration. Manage Organizations, Users, Activities, Notes, Files. Use when the user wants to interact with RedCircle API data.
| Search API integration. Manage Deals, Persons, Organizations, Leads, Projects, Pipelines and more. Use when the user wants to interact with Search API data.
| Secure Code Warrior integration. Manage data, records, and automate workflows. Use when the user wants to interact with Secure Code Warrior data.
| Similarweb DigitalRank API integration. Manage Websites. Use when the user wants to interact with Similarweb DigitalRank API data.
| SSLMate — Cert Spotter API integration. Manage Certificates, Domains. Use when the user wants to interact with SSLMate — Cert Spotter API data.
| The Odds API integration. Manage data, records, and automate workflows. Use when the user wants to interact with The Odds API data.
| UK gov Vehicle Enquiry API integration. Manage data, records, and automate workflows. Use when the user wants to interact with UK gov Vehicle Enquiry API data.
| Video sdk integration. Manage data, records, and automate workflows. Use when the user wants to interact with Video sdk data.
| VIES API integration. Manage data, records, and automate workflows. Use when the user wants to interact with VIES API data.
| World News API integration. Manage data, records, and automate workflows. Use when the user wants to interact with World News API data.
| Z-API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Z-API data.
| Zip Archive API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Zip Archive API data.
| Zyte API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Zyte API data.
>- SOP for terminal-based, git-native AI pair programming with Aider (git work-tree + tree-sitter repo-map + edit-format + human-in-loop REPL). Use when editing code in an existing git repo via an LLM, when you need to converge a change to 2-5 files, pick an edit format that fits the model, run architect+editor mode, or wire an auto-test loop.
>- Universal discipline for any LM-driven loop — agent retries, plan-act-observe, multi-agent handoffs, optimiser passes, test-fix cycles. Encodes the one rule every framework reliable terminator. Termination must be provided by an explicit counter + exit predicate + stagnation signal + escalation path that live OUTSIDE the LM's control. This is a tool- level, framework-agnostic skill. It maps onto LangGraph (recursion_limit + state counter + interrupt), CrewAI (max_iter + max_rpm + human_input), Claude / OpenAI SDKs (max_iterations + tool_use_budget), DSPy (declared evaluation budget), Aider (REPL + loop, recursion limit, recursion_limit, GraphRecursionError, max iterations, max_iter, agent stuck, agent won't stop, runaway agent, ReAct loop not terminating, agent repeating itself.
SOP for writing, loading, and evolving a project-level convention file (CONVENTIONS.md / CLAUDE.md / .cursor/rules / .clinerules / AGENTS.md) so that a coder-agent reliably respects your codebase's style choices every session. Tool-agnostic; covers the four load mechanics (read-only attachment, ancestor-walk auto-load, glob-scoped rules, agent backstory) and the conflict resolution between pinned conventions and the existing code.
| RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.
| Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use raw prompting or LangChain templates instead.
| Decision protocol for wrapping a REST / GraphQL / RPC API as a tool an LLM LM-friendly subset of the *API surface* — one tool per user intent, not one per endpoint. Activates when a coder agent must expose an external HTTP API to a model (function calling, tool_use, MCP, LangChain `@tool`, CrewAI `BaseTool`). Encodes the *what to surface, how to name, how to shape, how to fail* — not any single framework's API. ~80% of agent tools in production are HTTP wrappers; this is the SOP for getting them right.
| Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.
| Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE the re-ingest-correctness contract is not surfaced.
| Decision protocol for building, debugging, and operating LangGraph-based agent systems. Activates when a coder agent is asked to design a stateful LLM workflow, add human-in-the-loop, choose a multi-agent pattern (supervisor / swarm / hierarchical), pick a checkpoint backend, or migrate a fragile chain into a durable graph. LangGraph is positioned by its maintainers as a "low-level orchestration framework for building, managing, and deploying long-running, stateful agents" — this skill encodes the *when* and *why*, not the API.
>- ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM ReAct vs CoT vs PoT, which dspy module, predict vs chain of thought.
>- Decomposed, multi-criteria metric design for LLM pipelines. The metric IS the model — change the metric and the optimizer changes behavior. Decompose by default; bool during LLM-as-judge, llm as judge, eval metric, evaluation score, scoring function, rubric, RAGAS, G-Eval, judge bias, verbosity bias, how to evaluate LLM output.
| the framework sent to the model, before changing anything else. Activate when an LM call produced an unexpected output (wrong answer, schema violation, refusal, truncation, cost spike, latency spike, infinite loop, "model got dumber after upgrade"). The skill enforces a 30-second inspect step BEFORE any prompt edit, model swap, retry, or temperature CrewAI `step_callback`, LangChain `set_debug`/`set_verbose`, Aider `/diff`+`--verbose`, raw OpenAI/Anthropic via `OPENAI_LOG=debug`/`ANTHROPIC_LOG=debug` or HTTPX event hooks. Do NOT activate for first-time prompt authoring, exploratory prompt design, or non-LM bugs.
>- A 5-minute gate the coder runs at project kickoff (and again whenever the repo shape changes). Classifies the workspace into Greenfield / Brownfield-large / Mid-size-familiar / Library-SDK, then maps the state to an agent strategy (autonomy, context primitive, tool choice). Use BEFORE picking Cursor vs Claude Code vs Aider, BEFORE turning on repo-map, BEFORE writing the first prompt. Skip only if the same repo was gated within the last day codebase, project setup strategy, legacy codebase agent, where to start a coding agent.
>- Decide where to enforce structured LM output (constrain at decode time with Outlines vs validate-and-retry with Instructor vs grammar with Guidance) and which failure stance to take (Assert/hard-fail vs Suggest/soft-retry). Use when an LM's output is parsed or typed by downstream code and you must pick one enforcement library plus its failure handling, when malformed output is burning tokens on retries, or when choosing between decode-time vs validation-time constraints for local vs API models.