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Found 2,119 Skills
Industry valuation rank time series for a single stock via Longbridge — tracks how a stock's PE / PB / PS / dividend-yield rank within its sector has changed over time (rank N of total M). Answers "is my stock becoming relatively cheaper or more expensive vs peers?" Complements longbridge-valuation (single-stock percentile history) and longbridge-industry-valuation (current sector snapshot). Triggers: "行业排名变化", "估值排名", "PE排名历史", "行业估值位置", "排名走势", "估值相对同业", "行業排名變化", "估值排名", "PE排名歷史", "行業估值位置", "排名走勢", "valuation rank", "industry rank history", "PE rank trend", "relative valuation rank", "sector ranking over time", "how does AAPL rank in industry PE".
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Help users make better decisions between competing options. Use when someone is weighing pros and cons, comparing alternatives, struggling with a difficult choice, deciding between speed and quality, or asking "should we do X or Y?"
Use when "evaluating technology", "choosing frameworks", "stack comparison", "technology decisions", or asking about "React vs Vue", "PostgreSQL vs MySQL", "AWS vs GCP", "build vs buy"
Evaluate agents and skills for quality, completeness, and standards compliance using a 6-step rubric: Identify, Structural, Content, Code, Integration, Report. Use when auditing agents/skills, checking quality after creation or update, or reviewing collection health. Triggers: "evaluate", "audit", "check quality", "review agent", "score skill". Do NOT use for creating or modifying agents/skills — only for read-only assessment and scoring.
Generates a Jupyter notebook that evaluates a fine-tuned SageMaker model using LLM-as-a-Judge. Use when the user says "evaluate my model", "how did my model perform", "compare models", or after a training job completes. Supports built-in and custom evaluation metrics, evaluation dataset setup, and judge model selection.
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
Create AI evaluation plans with benchmarks, rubrics, and error analysis workflows.
Comprehensive security and safety evaluation system for agent skills (.skill files). Use when users provide GitHub URLs, website links, or .skill files for download and request security assessment, safety evaluation, or ask "is this skill safe to use." Evaluates prompt injection risks, malicious code patterns, hidden instructions, data exfiltration attempts, and provides actionable recommendations with risk scoring.
Industry valuation comparison and distribution analysis via Longbridge — cross-peer valuation matrix (PE / PB / PS / dividend yield), industry-percentile ranking, and industry premium / discount for a single stock. Triggers: "行业估值", "行业溢价", "行业折价", "行业对比", "行业百分位", "同行业估值", "板块估值", "行业贵不贵", "行業估值", "行業溢價", "行業折價", "行業對比", "行業百分位", "板塊估值", "industry valuation", "sector valuation", "industry premium", "industry percentile", "peer valuation", "sector PE", "TSLA.US industry valuation", "700.HK sector comparison".
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.