1.3K skills · sorted by relevance
In patients with normal preoperative adrenal function, suggests an individualized clinical approach for postoperative glucocorticoid administration until HPA axis evaluation can be performed. Triggers include managing postoperative patient with normal preoperative adrenal function needing glucocorticoid management.
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Recommends against using a random cortisol level to diagnose adrenal insufficiency. Triggers when considering a random cortisol test for AI evaluation (e.g., "random cortisol measurement being considered for AI diagnosis").
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lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
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Evaluate and install ecosystem tools (Hermes plugins, skills, integrations) — assess complexity vs. benefit before installation, show examples for visual tools, and avoid "cloned but not configured" states.
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Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.
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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
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High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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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
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Expert-level chemical process design covering process flow diagrams, mass and energy balances, equipment sizing, economic evaluation, and sustainable process design.
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Run the LangSmith evaluation suite and display pass/fail results
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Add a new E2E test case to tests/e2e/prompts.yaml for LangSmith evaluation. Use when adding interaction pairs to test, covering new ANSM drug classes, or rebalancing eval coverage.
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Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.
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Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.
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Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.
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Use when building an MCP server in Python (FastMCP) or Node/TypeScript (MCP SDK) — agent-centric tool design, input schemas, error handling, and the 10-question evaluation harness.
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Black-box evaluation of a shipped LLM feature — adversarial probes for hallucination, prompt-injection, and cost-runaway vs stated expectations. Not RAG/embedding. Triggers 'review my chatbot'.
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Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
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Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
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[Code Quality] Use when a workflow step or the user asks for rationale evaluation or critical judgment of a decision, claim or statement: necessity, options, trade-offs. --validate-findings checks findings; --fix-loop fixes. Diffs: changes-review.
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[Content] Use when a workflow step or the user asks for business viability: Business Model Canvas, financials, risks and go-to-market recommendation.
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Audits a live website for AI-engine discoverability (AEO/GEO). Crawls the site, runs 16 deterministic checks plus a 6-dimension content evaluation, and produces a scored report (A-F) with prioritized fixes. Use to get a baseline before improve-aeo-geo, or to measure progress after changes.
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Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.
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