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概要と使いどころ
Analyze TF motif accessibility variability across samples or single cells using chromVAR. Use when identifying TF motifs whose accessibility correlates with conditions, computing per-sample motif z-scores after matched background correction, comparing to ArchR / Signac equivalents, or distinguishing motif-accessibility signal from per-site footprinting.
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。
Infers genetic super-population ancestry from a 23andMe/AncestryDNA file and computes ancestry-stratified odds ratios with an exploratory Ancestry Elevation Score (AES) showing where ancestry-specific GWAS effect sizes diverge from European reference estimates.
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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Calculate and interpret the PREVENT-ASCVD risk score (10-year and 30-year) for primary prevention of atherosclerotic cardiovascular disease per the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers inputs required, risk-category thresholds (low <3%, borderline 3–<5%, intermediate 5–<10%, high ≥10%), when to use the 30-year calculation (adults 30–59), and how PREVENT differs from the older Pooled Cohort Equations (lower risk estimates by 40–50%, race-agnostic). Use when a clinician asks what the 10-year ASCVD risk is, how to calculate PREVENT, PCE vs PREVENT, risk category for this patient, does my patient need a statin, 30-year risk calculation, primary prevention risk assessment. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.
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。
Set absolute LDL-C, non-HDL-C, and ApoB treatment goals, PLUS percent LDL-C reduction, for a primary-prevention patient on lipid-lowering therapy per the re-introduced treatment goals in the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers goals by PREVENT-ASCVD category (low / borderline / intermediate / high), by CAC score, in severe hypercholesterolaemia / FH, in diabetes with multiple ASCVD risk factors, and the ApoB targets once LDL-C and non-HDL-C goals are met. Use when a clinician asks what LDL goal to aim for, LDL target for intermediate risk, non-HDL goal, ApoB target, dual goals on statin, patient at goal or not, how low should LDL go, statin not enough what next. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.
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。
Comprehensive REST API design review with automated linting, breaking-change detection, and design scorecards. Catches inconsistent conventions, missing versioning, and design smells before APIs ship. Use when reviewing a PR that adds or changes API endpoints, auditing an existing API for v2 migration, or establishing API standards for a team.
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。
The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.
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。
Critically review a resume/CV against ATS (applicant tracking system) parsing rules, recruiter skim patterns, and keyword alignment with a target job description. Use when the user asks to "review my resume/CV", "check my resume against this job ad", "will this pass ATS", or shares a resume file (.docx/.pdf/.md) for feedback. Provides scored, prioritised, critical feedback — not a rewrite.
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。
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
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。
Discover B2B leads from Reddit using AI-powered lead scoring via reddapi.dev Leads API. Finds high-intent signals, scores them 0-100, and classifies by lead type (pain_point, solution_request, complaint, feature_request, comparison). Perfect for competitor poaching, pain point discovery, and sales prospecting.
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 for tradeoff analysis, or when the user must pick between concrete options: "A or B?", "which library should we use?", "build vs buy", "compare these approaches". Scores options on weighted criteria, tests sensitivity and reversibility. Skip for open ideation or single-option reviews.
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Validates a plan before committing to it - checks whether the ranking survives the assumptions, finds the most likely way it fails, and makes the trade-offs explicit via a Pareto frontier instead of an invented single score. Use before a launch, price change, migration, infrastructure change or major commitment, when running a premortem, when two objectives conflict, or when a decision depends on an assumption nobody has tested.
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。
Turns a vague "what should we do about X" into a scored decision - explicit actions, an explicit unknown, an explicit prior, and one utility scale - then picks by maximum expected utility and checks the result for framing effects. Use when facing a choice between options under uncertainty, when a decision has stalled in circular debate, when someone asks "should we do A or B", or before writing any decision record, strategy memo, or roadmap commitment.
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。
Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.
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。
Measure real frontend performance — Core Web Vitals (LCP, CLS, INP), page-load traces, CPU and network throttling, Lighthouse accessibility/SEO scores, and heap growth — using chrome-devtools-mcp against a live browser. Trigger when asked "is the page fast enough", "measure Core Web Vitals", "why is this route slow", "run Lighthouse", "check for a memory leak", before any performance-gated certification (Built for Shopify, Core Web Vitals thresholds, store listing review), and before any launch or traffic-multiplier event. Measurement only — this skill never drives functional journeys.
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。
Optimize prediction-pool tips (kicktipp etc.): rules + multi-book consensus odds → expected-points-max answer for every question, scores AND bonus. Triggers 'optimize my pool tips', 'predict'.
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Use when the user wants a security-maturity scorecard / posture assessment of a module — category ratings with evidence, not a vulnerability hunt. Also on 'wie sicher ist dieses Modul aufgestellt?'
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 committing to a heavy or irreversible plan — imagine it's 6 months later and this failed; enumerate why, score each mode, derive early-warning signals and preventive guardrails.
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Use when adjudicating an operational go/no-go from typed evidence — a readiness enum with an explicit floor where one red is not-ready, unknown is never green, and no score can average a red away.
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 to consolidate multiple already-run judge verdicts into one report — consensus, conflicts, must-fix/should-fix with per-judge provenance. Consume-only, no opaque score, 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。
Use when choosing between alternatives with trade-offs — X-or-Y decisions or a weighted decision matrix / gewichtete Entscheidungsmatrix ('score my options'); ADR via `adr-create`.
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 churn defence — health-score signals, churn-cause split (involuntary / value / relationship / fit), early-warning loop. Triggers on 'why are accounts leaving'.
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。
Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until it reaches the repository's configured Greptile merge threshold with zero unresolved actionable comments, or until the five-review maximum is reached. Triggers Greptile review, fixes all actionable comments, pushes/re-shelves, re-triggers review only for new head SHAs unless a 5/5 score is already published, and repeats. Use when the user wants to optimize a PR/MR/CL against Greptile's code review standards without creating runaway review loops.
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。
Ranks companies or accounts by strategic relevance and opportunity. Use when the user provides accounts and wants a priority score, tier, rationale, risks, and next action.
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。