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Found 589 Skills
Maintainer workflow for reviewing, triaging, preparing, closing, or landing OpenClaw pull requests and related issues. Use when Codex needs to validate bug-fix claims, search for related issues or PRs, apply or recommend close/reason labels, prepare GitHub comments safely, check review-thread follow-up, or perform maintainer-style PR decision making before merge or closure.
Draft or update requirement documents under `codestable/requirements/` for the project — use **user stories + plain language** to describe a capability's "reason for existence, solution approach, and boundaries", so non-technical readers can quickly understand the highlights of the system. Layered with architecture: requirement is the "problem space" (why this capability is needed), while architecture is the "solution space" (what structure is used to implement it). Two modes: new (draft a new requirement doc from scratch), update (refresh an existing doc based on new materials or implementation changes). Single-target rule — only modify one document at a time. Trigger scenarios: the user says "fill in a requirement doc", "write down the requirements for this capability", "update the requirements directory", or during the feature-design phase, it is found that there is no corresponding requirement for the capability to be implemented this time.
Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
Every Open-Meteo endpoint family in one CLI — forecast, archive, marine, air quality, flood, climate, ensemble, seasonal, geocoding, elevation. Trigger phrases: `what's the weather in`, `forecast for`, `is it going to rain`, `marine forecast`, `air quality in`, `historical weather`, `climate normal`, `use open-meteo`, `run open-meteo`.
Plan an Israeli wedding from engagement to chuppah, covering venue selection (ulmot, ganot aruim), vendor comparison via Israeli platforms (Celebrate, Engaged, Save A Date, Walla Wedding), budget planning (~100-140K NIS average), Rabbinate registration (tik nisuin, teudat ravakut), halachic requirements (mikveh, ketuba), guest management, per-plate cost optimization, seasonal pricing, and timeline creation. Use when user asks about "chatuna b'yisrael", Israeli wedding planning, wedding budget, "ulam aruim", "ulmot", "ganim", wedding vendors, Rabbinate requirements, "tik nisuin", ketuba, or wedding timeline. Prevents common mistakes like missing Rabbinate deadlines, overpaying on Thursday weddings, or forgetting AKUM fees. Do NOT use for destination weddings abroad, non-Jewish religious ceremonies, or divorce proceedings.
High-converting landing pages — campaign pages, collection pages, seasonal promos, A/B testing
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Reviews PR comments from GitHub (Copilot, reviewers), evaluates against actual code, replies with reasoning, and resolves threads. Triggers on "review pr comments", "address pr feedback", "fix pr comments", or "review copilot suggestions".
Complete guide for Google Gemini API using the CORRECT current SDK (@google/genai v1.27+, NOT the deprecated @google/generative-ai). Covers text generation, multimodal inputs (text + images + video + audio + PDFs), function calling, thinking mode, streaming, and system instructions with accurate 2025 model information (Gemini 2.5 Pro/Flash/Flash-Lite with 1M input tokens, NOT 2M). Use when: integrating Gemini API, implementing multimodal AI applications, using thinking mode for complex reasoning, function calling with parallel execution, streaming responses, deploying to Cloudflare Workers, building chat applications, or encountering SDK deprecation warnings, context window errors, model not found errors, function calling failures, or multimodal format errors. Keywords: gemini api, @google/genai, gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite, multimodal gemini, thinking mode, google ai, genai sdk, function calling gemini, streaming gemini, gemini vision, gemini video, gemini audio, gemini pdf, system instructions, multi-turn chat, DEPRECATED @google/generative-ai, gemini context window, gemini models 2025, gemini 1m tokens, gemini tool use, parallel function calling, compositional function calling
Comprehensive DeepResearch methodology for conducting rigorous, traceable research projects with quality gates, structured analysis, and decision-ready deliverables. Use when (1) Conducting deep research projects requiring evidence-based analysis, (2) Managing research progress with quality gates and artifacts, (3) Producing research reports with traceable sources and structured reasoning, (4) Applying OSINT verification techniques, (5) Using structured analytic techniques (ACH, Key Assumptions Check, Red Team), (6) Expressing uncertainty and confidence in research findings, (7) Ensuring research deliverables meet intelligence tradecraft standards (ICD 203/206/208)
ATP and WTA tennis data via ESPN public endpoints — tournament scores, season calendars, player rankings, player profiles, and news. Zero config, no API keys. Use when: user asks about tennis scores, match results, tournament draws, ATP/WTA rankings, tennis player info, or tennis news. Don't use when: user asks about other sports. Don't use for live point-by-point data — scores update after each set/match.
First-principles thinking; identify what everything else builds on. Use when a learner feels like they're memorizing rather than understanding, wants to distinguish foundational from derived concepts, or needs to reconstruct knowledge from basics. This skill guides through dependency testing (elimination, reconstruction, abstraction descent) while enforcing human-only reasoning about what's core vs. derived. Triggers on phrases like "I'm memorizing not understanding", "what's really foundational", "I can use it but don't understand why", "distinguish core from derived", or when a learner wants deeper understanding of first principles.