1.2万 件 ・ 関連度順
概要と使いどころ
Quantitative agent evaluation using 4-metric framework (correctness/step_ratio/tool_call_ratio/latency_ratio) with ideal trajectory annotation and capability-categorical taxonomy. Use when measuring agent efficiency, comparing agent variants, or gating new agents through correctness→efficiency phases. Complements harness-eval (SE benchmarks) and evaluator-optimizer (qualitative rubric).
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Review approved Grafana business-intelligence evidence for revenue, conversion, and operational KPIs. Use for bounded KPI scorecards, financial or operational metric checks, baseline comparisons, and business-unit reviews without changing source systems.
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Monitor reviewed Grafana telemetry and freshness evidence for IoT devices and edge fleets. Use for smart devices, home automation sensors, remote equipment, wind turbines, space hardware, device-fleet freshness, missing telemetry, and bounded edge-health comparisons.
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Use this skill for ANY of the following — do not skip it even if the query seems simple: strategic decisions, business analysis, thesis building, complex problem solving, geopolitical analysis (non-market), philosophical reasoning, academic research, system design, decision under uncertainty, long-form thinking, or any question that benefits from First Principle deconstruction and multi-agent reasoning. ALWAYS use this skill when the user asks about: why something works the way it does, whether a decision is correct, how to think about a complex problem, what could go wrong, analyzing a situation from multiple angles, or any "should I..." question involving real-world trade-offs. This is NOT a market/trading skill — for financial markets, BTC, gold, stocks, forex, macro economics, use money-atlas-intelligence-os instead.
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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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。
Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
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。
Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.
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。
Educates patients with primary adrenal insufficiency on increasing glucocorticoid dosage during intercurrent illness, fever (≥38°C), or physiological stress, including recognition of precipitating symptoms (e.g., fever, vomiting, trauma) and actions to prevent impending adrenal crisis. Trigger phrases: fever, intercurrent illness, stress, vomiting, inability to tolerate oral meds, need for parenteral glucocorticoid.
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。
Suggests adjusting glucocorticoid dose according to severity of illness or magnitude of the stressor in patients with primary adrenal insufficiency. Use when patient with PAI has intercurrent illness, fever, or stress.
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Workflow Phase 6 — TDD coordinator. Decides the scenario recipe and the implementation contract in main context, writes them to a state file, seeds per-worker tasks (scenario, implement, verify-tick, design-ui-tick) into the TaskList, and yields with harness_state continue so the harness invokes each worker as its own tick. No subagent delegation; no nested Skill calls.
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End-to-end workflow orchestrator. Walks the 11-phase pipeline, invoking each phase skill in order inside an internal loop, yielding at consent gates (/approve-direction, /approve-swarm, /grant-commit), and exiting cleanly on yield/failure/done. Decides swarm-vs-solo at Phase 6. Auto-loops /tdd on integrate failures that don't require a spec change. The harness_continuation Stop hook is a safety net that re-fires harness only when the loop exited mid-flow.
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Analyze Amazon product, sales, Amazon Ads, retail-readiness, and Best Seller Rank data to find commercially actionable growth opportunities. Use when Codex needs to identify which ASINs, campaigns, keywords, search terms, product targets, placements, categories, or BSR movements deserve more investment, optimization, harvesting, bid/budget changes, retail-readiness fixes, investigation, protection, or controlled scaling across Sponsored Products, Sponsored Brands, Sponsored Display, search term reports, targeting reports, campaign reports, placement reports, advertised/purchased product reports, Business Reports, organic performance, and BSR history.
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Plan and implement infrastructure, CI/CD, container, deployment, observability, and operational configuration changes with least privilege, staged validation, and rollback awareness. Use for pipelines, infrastructure as code, Kubernetes, containers, release automation, monitoring, and production-readiness work; do not use to perform destructive production actions without explicit authorization.
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Find overly complex code: files over 300 lines, functions over 50 lines, deeply nested conditions (4+ levels), and magic numbers. Use for code quality reviews, pre-release checks, or when asked to assess code maintainability.
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Expert-level mathematical logic knowledge. Use when working with propositional logic, first-order logic, proof theory, model theory, computability theory, Godel incompleteness theorems, set theory, type theory, or formal verification. Also use when the user mentions 'formal proof', 'Godel', 'incompleteness', 'Turing machine', 'decidability', 'Zermelo-Fraenkel', 'axiom of choice', 'model theory', 'completeness theorem', 'computability', 'formal language', or 'type theory'.
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Session-start orientation for this team harness. Load first, before any work. Explains the operating contract, when each skill triggers, and how installed companion plugins supply agent-craft capabilities while this harness supplies the team contract. Trigger at the start of every session and whenever unsure which skill applies.
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Garanta estrutura válida de JSON/XML/código durante a geração, use modelos Pydantic para outputs type-safe, suporte modelos locais (Transformers, vLLM) e maximize velocidade de inferência com Outlines - biblioteca de geração estruturada da dottxt.ai
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[Content] Use when you need to evaluate business idea viability: Business Model Canvas, financial projections, risk matrix, go-to-market, execution plan.
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直近N日（デフォルト1日）のPRレビューで繰り返し指摘されている観点のうち「実際に修正すべき」と判断できるものを集約し、リポジトリルートのCODING_GUIDELINES.mdを生成・更新したうえで、commit-push + create-prでPRを作成する。サイズが膨らみすぎないよう既存ルールとの統合・圧縮も行う。
中文排版规范助手。当用户要"排版""中文网页样式""文章排版""公众号排版""PDF/Word 排版""字体怎么选""中英混排""为什么我的中文网页很丑"时使用。处理中英间距、标点、行高、字体栈、CJK 断行等中文特有问题，输出可直接用的 CSS 或排版建议。
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中式网页主题库。当用户要"中国风网页""国风网站样式""古风博客主题""中式配色 CSS""水墨风页面""新中式网页""给网站换个中国风皮肤"时使用。提供 8 套可直接引入的 CSS 主题，内置中文排版规范。
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中式 DESIGN.md 设计系统。当用户要"中国风网页""国风页面""中式设计系统""给项目定个中式风格""DESIGN.md""让 AI 按设计规范生成界面""中文网页排版规范"时使用。提供 8 套可直接丢进项目根目录的 DESIGN.md，AI 读了就按这套规范生成界面。含西文设计系统普遍缺失的中文排版规则。
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生成中国中小学教案与配套材料。当用户要"备课""写教案""设计一节课""做课件大纲""出分层作业""写说课稿""评课"时使用。覆盖语文/数学/英语/科学等学科，小学到高中；先确认学段、教材版本与适用课程标准，再按学科核心素养、可观察任务和评价证据组织目标。
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。