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概要と使いどころ
This skill recommends regularly evaluating for corticotroph tumor progression using pituitary MRIs and ACTH levels in patients with known Cushing's disease who underwent bilateral adrenalectomy and in patients undergoing this procedure for presumed occult ectopic ACTH secretion. Triggers include postoperative follow‑up after bilateral adrenalectomy, rising ACTH levels, or new pituitary lesion on MRI suggestive of tumor progression.
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This skill guides evaluation for malignancy using imaging in patients with a history of adrenal adenoma (post‑adrenalectomy Cushing’s syndrome) when the adenoma demonstrates a CT density of ≥10 Hounsfield units or when pathology is consistent with possible carcinoma. Trigger phrases include “CT density ≥10 HU”, “possible carcinoma on pathology”, and “post‑adrenalectomy CS patient with adrenal adenoma history”.
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This skill recommends that all patients with Cushing’s syndrome receive monitoring and adjunctive treatment for cortisol-dependent comorbidities including psychiatric disorders, diabetes, hypertension, hypokalemia, infections, dyslipidemia, osteoporosis, and poor physical fitness. Apply when managing any CS patient to address associated health issues; triggers include confirmed or suspected hypercortisolism, preoperative evaluation, postoperative follow‑up, or persistent hypercortisolism despite treatment.
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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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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
日本語の概要は準備中です。原文の説明を表示しています。
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
日本語の概要は準備中です。原文の説明を表示しています。
Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.
日本語の概要は準備中です。原文の説明を表示しています。
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
日本語の概要は準備中です。原文の説明を表示しています。
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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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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Run the LangSmith evaluation suite and display pass/fail results
日本語の概要は準備中です。原文の説明を表示しています。
lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
日本語の概要は準備中です。原文の説明を表示しています。
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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Expert-level chemical process design covering process flow diagrams, mass and energy balances, equipment sizing, economic evaluation, and sustainable process design.
日本語の概要は準備中です。原文の説明を表示しています。
Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
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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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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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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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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Wing-4 valuation cognition for a CFO / finance-partner. Use when a deal, internal investment, or board ask names DCF, intrinsic value, WACC, terminal value, or 'what's it worth on a 5-year hold'.
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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'.
日本語の概要は準備中です。原文の説明を表示しています。