データ分析、機械学習、LLMアプリ、プロンプト設計（8,845 件）
概要と使いどころ
Assesses patient’s knowledge of diabetic ketoacidosis prevention, recognition, and treatment. Triggered by clinician prompts such as 'Review sick-day rules and DKA signs' or 'Ensure patient knows when to seek help for hyperglycemia'.
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This skill recommends bilateral adrenalectomy for occult or metastatic ectopic ACTH secretion or as a life-preserving emergency treatment in patients with very severe ACTH‑dependent Cushing’s syndrome who cannot be promptly controlled by medical therapy. It is triggered by life‑threatening complications of uncontrolled hypercortisolism such as infection, pulmonary thromboembolism, cardiovascular catastrophe, or acute psychosis.
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Establishes age- and gender-specific blood pressure goals for pediatric patients with primary aldosteronism (PA) using published normative data to assess treatment adequacy and avoid over-treatment. Use when managing hypertension in children with PA to evaluate if BP is adequately controlled; triggers include pediatric PA hypertension, BP not normalized with glucocorticoid therapy, and need for age‑specific BP targets.
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Adjusts intravenous bisphosphonate dosing and infusion duration for patients with hypercalcemia of malignancy and renal insufficiency (creatinine clearance <60 mL/min). Triggered when a clinician encounters renal impairment and asks how to modify zoledronic acid or pamidronate infusion rates.
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Determines the appropriate IV pamidronate dose (60‑90 mg) and infusion duration (2‑24 h) for adults with hypercalcemia of malignancy based on renal function. Triggered when a clinician orders pamidronate and asks how to adjust for kidney dysfunction (eGFR < 60 mL/min/1.73 m²).
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Reconcile baseline-versioned files that the `create-baseline upgrade` CLI staged for LLM-assisted semantic merge. Use when the CLI prints "Open Claude Code and run /upgrade-project to reconcile". Reads the stage manifest at `.claude/state/upgrade/<ts>/manifest.json`, reasons through each pending file's three-way delta in main context, writes a reconciled LOCAL, then deletes the stage when every file lands. Supports `--dry-run` (preview the reconciled diff without writing) and a structured "needs-user-input" fallback when the conflict cannot be disambiguated automatically.
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Write technical documentation that matches the measured profile of professionally-written docs. Carries the craft rules derived from 114k words of pre-2022 documentation (SQLite, PostgreSQL, Python, Django, Rust, Go, Pro Git, nginx), the per-Diátaxis-type numeric targets, and the copywriting register for page leads. Use when drafting or rewriting any documentation prose, or when a page is factually right but reads as machine-written.
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Use for Fermi estimation, or when a rough number is needed fast: "roughly how many...", "ballpark the cost", "estimate capacity or market size", "is this even feasible?". Decomposes, uses ranges and sanity-checks. Skip when exact data is available or a precise calculation is wanted.
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Use for analytical thinking, or when a question needs breaking down and evidence: "why did metric X drop?", "what drives this?", "break this down", "what data do we need?". Builds a tree, hypotheses and evidence, ends with an answer and key uncertainty. Skip for quick verdicts.
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Use when a request needs external sources, current facts, listings, a supplied URL, papers, or library and API documentation. When using Firecrawl for the request, use firecrawl_search to find sources and firecrawl_scrape to read a supplied or discovered URL. Firecrawl also retrieves documents, collects structured data, and tracks website changes. Respect explicit source and tool preferences.
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An infographic skill for AI agents — works inside Claude Code, Codex, Hermes, OpenClaw, Cursor, and any agent host that loads skills. Create professional infographics as self-contained HTML files with PNG exports. Use when the user asks to create, design, generate, build, or make an infographic, data visualization, visual explainer, chart poster, cheatsheet, or pocket guide. Trigger phrases include "make this visual", "turn this into a graphic", "visualize this data", "infographic for X", "cheatsheet for X", or when the user provides structured data (tokenomics allocations, ecosystem/partner lists, timelines, comparisons, step-by-step processes) AND asks for a visual output. Covers tokenomics, ecosystem overviews, game overviews, cheatsheets, airdrop guides, comparisons, and process flows. Do NOT trigger for plain text answers, markdown tables, bulleted summaries, questions asking for advice or explanations about a topic (even when the topic is tokenomics, ecosystem, vesting, etc.), inline chat diagrams, or data processing with no visual output requested. The signal is "asks for a visual", not "mentions a keyword".
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vLLM: high-throughput LLM serving, OpenAI API, quantization.
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Outlines: structured JSON/regex/Pydantic LLM generation.
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Build a throwaway prototype to flush out a design before committing to it. Routes between two branches — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route. Use when the user wants to prototype, sanity-check a data model or state machine, mock up a UI, explore design options, or says "prototype this", "let me play with it", "try a few designs".
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W&B: log ML experiments, sweeps, model registry, dashboards.
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OBLITERATUS: abliterate LLM refusals (diff-in-means).
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Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).
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TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
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Karpathy's LLM Wiki: build/query interlinked markdown KB.
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Systematic VPS cleanup — analyze files, categorize by importance, safely delete temporary/old data to free disk space
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HuggingFace hf CLI: search/download/upload models, datasets.
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Decomposition playbook + specialist-roster conventions + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
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Iterative Python via live Jupyter kernel (hamelnb).
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Songwriting craft and Suno AI music prompts.
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。