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Found 1,894 Skills
Guide for using MassGen to develop and improve itself. This skill should be used when agents need to run MassGen experiments programmatically (using automation mode) OR analyze terminal UI/UX quality (using visual evaluation tools). These are mutually exclusive workflows for different improvement goals.
A scoring scale for evaluating how well a CLI is designed for AI agents, based on the "Rewrite Your CLI for AI Agents" principles.
A-share Value Investment Analysis Tool, providing stock screening, in-depth individual stock analysis, industry comparison and valuation calculation functions. Based on value investment theory, it uses tushare to obtain public financial data, suitable for ordinary investors with low-frequency trading.
Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator.
Evaluates interfaces, components, screens, and flows against universal UX/UI principles (heuristics, UX laws, Gestalt, cognitive psychology, accessibility) and delivers concrete, prioritized improvements. Use whenever the user shares UI code, screenshots, components, or mockups and wants feedback — even if they don't use the words "critique" or "review". Also trigger when the user asks "what's wrong with this UI", "how can I improve this", "review my component", "does this look right", "give me feedback on this design", or shares any interface and asks for thoughts. Trigger for partial slices too (a single button, form, or card) — not only full screens.
Use when validating subjective quality criteria that cannot be deterministically tested — applies LLM-based evaluation with structured rubrics for tone, aesthetics, UX feel, documentation quality, and code readability. Triggers: documentation quality check, error message tone review, UX copy evaluation, code readability assessment, design aesthetic review.
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.
Conduct structured policy analysis including problem definition, alternative evaluation, and evidence-based recommendation. Use this skill when the user needs to evaluate policy options, compare interventions, assess regulatory impact, or make public sector recommendations — even if they say 'which policy should we adopt', 'what's the best approach to this public problem', or 'evaluate these policy alternatives'.
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Range bar evaluation metrics for quant trading. TRIGGERS - range bar metrics, Sharpe ratio, WFO metrics, PSR DSR MinTRL.
Use when user needs system design validation, architectural pattern assessment, or technology stack evaluation.
Deep multi-framework reasoning using Gemini. Use for complex problem analysis, challenging ideas, and evaluating multiple options with structured thinking.