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Found 1,944 Skills
Scaffolds eval.yaml test files for agent skills in the dotnet/skills repository. Use when creating skill tests, writing evaluation scenarios, defining assertions and rubrics, or setting up test fixture files. Handles eval.yaml generation, fixture organization, and overfitting avoidance. Do not use for running or debugging existing tests nor for skills authoring.
This skill should be used when Claude Code needs to perform basic arithmetic calculations. It provides a Python script that safely evaluates mathematical expressions including addition, subtraction, multiplication, division, exponentiation, and square roots.
Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.
Interpret macroeconomic indicators including GDP, inflation, unemployment, interest rates, and exchange rates to assess economic health and predict trends. Use this skill when the user needs to evaluate a country's economic outlook, understand monetary/fiscal policy impacts, or contextualize business decisions within the macroeconomic environment — even if they say 'is the economy doing well', 'what do rising interest rates mean for us', or 'explain today's economic data'.
Security audit and vulnerability scanning for AI agent skills before installation. Detects prompt injection in SKILL.md files, dangerous code patterns (eval, exec, subprocess), network exfiltration, credential harvesting, dependency supply chain risks, file system boundary violations, and obfuscation. Produces PASS/WARN/FAIL verdicts with remediation guidance. Use when evaluating untrusted skills, pre-install security gates, or auditing skill repositories.
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
Game building mechanics case studies and decision frameworks. Use when designing building systems, evaluating trade-offs, or learning from existing games. Reference-only skill with detailed analysis of Fortnite, Rust, Valheim, Minecraft, No Man's Sky, and Satisfactory building systems.
Coconote platform help — Quizlet's AI note-taker that records lectures and auto-generates study guides, flashcards, quizzes, and AI-narrated podcasts from recordings. Use when setting up Coconote for recording lectures and generating study materials, troubleshooting recordings that crash or fail to save on long lectures, figuring out why Coconote requires payment info before accessing the free plan, choosing between Coconote free and Pro subscription tiers, comparing Coconote to Voicenotes or AudioPen or Cleft Notes for voice capture, evaluating Coconote for student lecture note-taking across iOS and Android, or understanding why Coconote only generates one quiz and one flashcard set per note. Do NOT use for comparing AI meeting note-takers for sales teams (use /sales-note-taker) or reviewing a sales call for coaching (use /sales-call-review).
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).
Cross-symbol comparison (2–5 stocks) via Longbridge — valuation (PE / PB / PS / dividend yield), current price + change, latest financial KPIs (revenue / net income / ROE), market cap. Renders as a single matrix; flags cross-currency or cross-industry caveats. Triggers: "X 和 Y 哪个值得买", "X vs Y", "几只股票对比", "同行业谁最强", "X 跟 Y 谁更便宜", "几只哪个增速快", "科技七姐妹谁最强", "X 跟 Y 對比", "X 跟 Y 哪個便宜", "X vs Y", "compare X and Y", "peer comparison", "which is more expensive", "which has higher growth".
Write, run, and analyze structured test suites for Agentforce agents. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use developing-agentforce); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use observing-agentforce).
Scraper de MarketScreener (S&P Capital IQ): earnings transcripts, cotizaciones, perfiles empresa, financials, valuation, consenso analistas, noticias, insider trading, ratings. Sin API key.