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Found 1,944 Skills
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
Create and work with Meta SAM 3 (facebookresearch/sam3) for open-vocabulary image and video segmentation with text, point, box, and mask prompts. Use when setting up SAM3 environments, requesting Hugging Face checkpoint access, generating inference scripts, integrating SAM3 into Python apps, fine-tuning with sam3/train configs, running SA-Co or custom evaluations, or debugging CUDA/checkpoint/prompt pipeline issues.
OODA loop decision framework (Observe, Orient, Decide, Act). Use for complex decisions, problem-solving, unclear situations, or when someone is jumping to solutions without analysis.
Debug Node.js/TypeScript/JavaScript applications using the agent-dbg CLI debugger. Use when: (1) investigating runtime bugs by stepping through code, (2) inspecting variable values at specific execution points, (3) setting breakpoints and conditional breakpoints, (4) evaluating expressions in a paused context, (5) hot-patching code without restarting, (6) debugging test failures by attaching to a running process, (7) any task where understanding runtime behavior requires a debugger. Triggers: "debug this", "set a breakpoint", "step through", "inspect variables", "why is this value wrong", "trace execution", "attach debugger", "runtime error".
Use when clarifying fuzzy boundaries, defining quality criteria, teaching by counterexample, preventing common mistakes, setting design guardrails, disambiguating similar concepts, refining requirements through anti-patterns, creating clear decision criteria, or when user mentions near-miss examples, anti-goals, what not to do, negative examples, counterexamples, or boundary clarification.
Golden dataset lifecycle patterns for curation, versioning, quality validation, and CI integration. Use when building evaluation datasets, managing dataset versions, validating quality scores, or integrating golden tests into pipelines.
Agent orchestration patterns for agentic loops, multi-agent coordination, alternative frameworks, and multi-scenario workflows. Use when building autonomous agent loops, coordinating multiple agents, evaluating CrewAI/AutoGen/Swarm, or orchestrating complex multi-step scenarios.
Analyze sports data, identify exercise patterns, evaluate fitness progress, and provide personalized training recommendations. Supports correlation analysis with chronic disease data.
Comprehensive technical research by combining multiple intelligence sources — Grok (X/Twitter developer discussions via Playwright), DeepWiki (AI-powered GitHub repository analysis), and WebSearch. Dispatches parallel subagents for each source and synthesizes findings into a unified report. This skill should be used when evaluating technologies, comparing libraries/frameworks, researching GitHub repos, gauging developer sentiment, or investigating technical architecture decisions. Trigger phrases include "tech research", "research this technology", "技术调研", "调研一下", "compare libraries", "evaluate framework", "investigate repo".
🐟 Rust-native Fish shell-friendly file operations with Steel-backed SCI Clojure evaluation.
Generates business/company names across 10 categories (Descriptive, Metaphoric, Invented, Founder-based, Acronym, Compound, Foreign, Playful, Geographic, Legacy) with USPTO trademark screening, domain availability checking, and 0-100 scoring. Use when users need company/product/brand naming for new business launches, rebranding, trademark strategy, IP protection naming, evaluating current name strength, or any naming/branding tasks requiring systematic analysis with legal clearance.
A cognitive framework based on learning first principles, providing learning method diagnosis, efficiency assessment, and optimization advice. Use when: (1) Diagnosing if current learning methods align with first principles, (2) Evaluating learning plan efficiency and time investment, (3) Analyzing learning behavior problems and providing improvement suggestions, (4) Determining if learning content is worth the time investment. Core principle chain: Self-learning → Induction → Self-output → Expression restructuring → Logical understanding → Practice.