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Found 914 Skills
Use this skill when crafting LLM prompts, implementing chain-of-thought reasoning, designing few-shot examples, building RAG pipelines, or optimizing prompt performance. Triggers on prompt design, system prompts, few-shot learning, chain-of-thought, prompt chaining, RAG, retrieval-augmented generation, prompt templates, structured output, and any task requiring effective LLM interaction patterns.
Generate a production-ready AbsolutelySkilled skill from any source: GitHub repos, documentation URLs, or domain topics (marketing, sales, TypeScript, etc.). Triggers on /skill-forge, "create a skill for X", "generate a skill from these docs", "make a skill for this repo", "build a skill about marketing", or "add X to the registry". For URLs: performs deep doc research (README, llms.txt, API references). For domains: runs a brainstorming discovery session with the user to define scope and content. Outputs a complete skill/ folder with SKILL.md, evals.json, and optionally sources.yaml, ready to PR into the AbsolutelySkilled registry.
Apply when implementing order integration hooks, feeds, or webhook handlers for VTEX marketplace connectors. Covers Feed v3 (pull) vs Hook (push), filter types (FromWorkflow and FromOrders), order status lifecycle, payload validation, and idempotent processing. Use for building order integrations between VTEX marketplaces and external systems such as ERPs, WMS, or fulfillment services.
Use this skill when working with PostHog - product analytics, web analytics, feature flags, A/B testing, experiments, session replay, error tracking, surveys, LLM observability, or data warehouse. Triggers on any PostHog-related task including capturing events, identifying users, evaluating feature flags, creating experiments, setting up surveys, tracking errors, and querying analytics data via the PostHog API or SDKs (posthog-js, posthog-node, posthog-python).
Supanova Landing Page Design Engine. Generates premium, conversion-optimized landing pages using pure HTML + Tailwind CSS (CDN). Overrides default LLM biases toward generic templates. Enforces metric-based design rules, Korean typography standards, and hardware-accelerated motion for standalone HTML output.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Detect users' writing style requirements and load corresponding guidelines. Automatically activate when users mention keywords such as colloquial, life-oriented, authenticity, literariness, serious literature, pure literature, web novel, wish-fulfillment web novel, fast-paced, ancient style, martial arts, ancient charm, minimalism, Hemingway, restraint, etc. Suitable for discussions on novel styles, writing styles, and creative directions.
Build stateless MCP servers with TypeScript on Cloudflare Workers using @modelcontextprotocol/sdk. Provides patterns for tools, resources, prompts, and authentication (API keys, OAuth, Zero Trust). Use when exposing APIs to LLMs, integrating Cloudflare services (D1, KV, R2, Vectorize), or troubleshooting export syntax errors, unclosed transport leaks, or CORS misconfigurations.
Guides users through distributing Tauri applications to the iOS App Store, including Apple Developer enrollment, Xcode configuration, provisioning profiles, code signing, TestFlight beta testing, and App Store submission processes.
Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.
Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.
Methodology for effective AI-assisted software development. Use when helping users build software with AI coding assistants, debugging AI-generated code, planning features for AI implementation, managing version control in AI workflows, or when users mention "vibe coding," Cursor, Windsurf, or similar AI coding tools. Provides strategies for planning, testing, debugging, and iterating on code written with LLM assistance.