データ分析、機械学習、LLMアプリ、プロンプト設計（9,183 件）
Translate an image brief into provider-specific prompt grammar per model. Use when writing or refining an image-generation prompt for Ideogram, Flux, Gemini, GPT Image 2, or Recraft.
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。
Use when designing production-LLM prompts — few-shot, chain-of-thought, system prompts, templates, self-verification — distinct from prompt-optimizer and refine-prompt.
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。
Before stating any specific fact about an LLM provider's product — models, pricing, limits, context windows, SDK/API — for OpenAI, Gemini, Claude & others, verify against official docs, not memory.
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。
When turning a locked still + blueprint into a provider-tuned motion prompt — camera, primary + secondary motion, physics, native-audio sync. Triggers 'motion prompt for Veo/Kling/Sora'.
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When scoring whether an agent response defended against an injection — treated untrusted content as data, refused role-takeover, ignored fake boundaries. Inverted axis; never auto-gates.
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。
Resolve an icon request to an Iconify name and emit the stack's embedding. Use when adding icons, picking an icon set, or wiring Lucide/Heroicons/Phosphor/Tabler.
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。
Use when classifying data, setting retention, judging cross-border transfer, or shaping DSR workflow. Triggers on 'how long do we keep this', 'can this data go to the US'.
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。
Use BEFORE editing code that touches user data — traces the value from entry → validation → transformation → storage → egress, every hop cited with file:line.
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。
Use when writing Eloquent models, relationships, scopes, or queries via Model:: — 'fetch users with their orders'. NOT for PHPStan output, non-Eloquent services, or raw SQL questions.
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Estimate a Jira/Linear ticket — 'estimate PROJ-123', 'wie groß ist das?', 'should we split this?' — size + risk + split + uncertainty, sibling of /refine-ticket, close-prompt.
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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。
Use when polling external AIs (OpenAI, Anthropic) outside the host session for a neutral second opinion on a roadmap, diff, prompt, or file set — or 'cross-check with another model'.
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。
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
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Determines if current hormone levels are within target range for affirmed gender (e.g., testosterone 400-700 ng/dL for transgender males, estradiol 100-200 pg/mL for transgender females). Triggers include patient on hormone therapy and time for level check.
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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'.
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。
Guide pump setting adjustments for the MiniMed 780G system based on CareLink data, using Medtronic's 3-step follow-up workflow. Use when a clinician has CareLink data for a 780G patient and wants to know what to change — which setting, by how much, and how to program it. Triggers include: "what do I adjust", "how to fix high TIR", "how to reduce hypos", "adjust ICR/ISF/basal", "SmartGuard settings", "780G pump settings review", or any question about optimising 780G therapy after reviewing a download.
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Assesses patient’s understanding of how CGM data (trends, rates of change) differ from discrete SMBG fingerstick values. Triggered when clinicians hear requests to explain lag and trend arrows on CGM or teach the difference between CGM glucose and fingerstick glucose.
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。
Systematically interpret a Medtronic CareLink or MiniMed 780G Daily Review download in a structured 6-step clinical workflow — covering Time in Range, insulin delivery, day/night patterns, hypo and hyperglycemia root causes, and a prioritised action plan. Trigger when a clinician uploads or shares a CareLink report, MiniMed download, 780G Daily Review, pump download, or asks to review a patient's CGM or insulin pump data.
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。
Assesses that CGM low- and high-glucose alarm thresholds are individualized to optimize patient benefit and minimize alarm fatigue. Triggered by clinician prompts to 'Review low-glucose alarm thresholds' or 'Ensure alarms are individualized and not excessive'.
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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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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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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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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).
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。
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.
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。