660 件 ・ 関連度順
概要と使いどころ
Audit a job matching platform for relevance, fairness, and candidate experience. Triggers: building or reviewing job boards, ATS matching engines, internal mobility platforms, or workforce marketplaces.
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。
Analyze franchise expansion plans, model new market entry, score site selection candidates, map territory density, assess cannibalization risk, and build multi-year growth scenarios.
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 a recruiter-hiring manager intake meeting to kick off a search. Use when the user says "I need to kick off hiring", "run the intake call", "prepare for the intake meeting", "align with the hiring manager on this role", "we're opening a req", or wants to define hiring criteria and process before sourcing begins - even if they don't explicitly say "intake meeting". Also use when a recruiter needs to get a hiring manager aligned on what "great" looks like before anyone talks to a single candidate.
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。
Analyze a site's backlink profile, anchors, toxic signals, competitors, gaps, and disavow candidates. Use only when links or referring domains are the requested focus.
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。
Phase 8 of the SRS workflow. Generates an improvement report covering deferred features, technical risks, validation warnings, and next-version candidates. No word limit.
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。
Phase 8 of the SRS workflow. Generates an improvement report covering deferred features, technical risks, validation warnings, and next-version candidates. No word limit.
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 the co-gas no-submit hosted XP loop for a source-backed Coworld policy candidate.
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。
ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction for drug candidates. Use for assessing drug-likeness, PK properties, and safety risks early in drug discovery. Keywords: ADMET, PK, toxicity, drug-likeness, DILI, hERG, bioavailability
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。
Automate recruiting and hiring workflows in Ashby -- manage candidates, jobs, applications, interviews, and notes through natural language commands.
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。
决策调研 / Decision-Driven Research：当用户面对一个具体决策需要找信息时使用——「有没有现成方案」 「怎么接入 X 平台」「这个技术可行吗」「业界怎么做 Y」「选 A 还是 B」「桌面端应该怎么定位」 「高级版和基础版怎么拉开差异」「这个产品方向对不对」。 核心行为：先框定研究层级和问题类型，再锚定决策问题，枚举竞争假设，主动找反对证据， 用排除逻辑给出有立场的结论。支持技术选型、产品策略、商业判断、竞品定位等所有需要做决定的调研。 可用中文唤起：「帮我调研」「有没有现成方案」「这个怎么接」「技术上可行吗」「帮我选一个」 「这个产品方向对不对」「我们应该怎么定位」「行业怎么做」。 与 research-topic-compiler 的边界：需要最终选择、推荐、排除理由、置信度和颠覆条件时用这个； 如果目标是长期学习、候选池沉淀或 Research Project，用 research-topic-compiler，再把 Candidate Backlog 交回本 Skill 做最终决策。 只有发现一个会实质改变结论、可研究且可关闭的证据 gap 时，才向 research-topic-compiler 发出精确 Return Request。 不用于：问题还没定义清楚时（先用 ai-collaboration-calibration L4-fuzzy 脑暴）。
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。
Researches and compares free and open source software projects in a supplied domain, using either user-named candidates or a discovered shortlist. Use when evaluating FOSS alternatives, comparing GitHub popularity, maintenance, licences, domain fit, trade-offs, or producing a linked HTML comparison report.
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。
Capture, gate, and query long-term project knowledge via the gated wiki subsystem: propose candidates, recall, or search; agents propose, humans approve. NOT for short-lived memory (mk: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。
Fetch external sources (web/arXiv/GitHub) into scanner-gated wiki CANDIDATES, never canonical pages: guarded, capped, scanned. NOT for local knowledge (mk:wiki) or markdown fetch (mk:web-to-markdown).
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 surfacing architectural friction and proposing deepening refactors (shallow modules → deep modules) for testability and AI-navigability. Triggers on "improve architecture", "find deepening opportunities", "where is this codebase shallow", "architecture review". Emits structured candidates + precise patches; delegates ALL rendering to mk:preview. NOT for diagram/HTML rendering (see mk:preview); NOT for trade-off deliberation (see mk:party); NOT for plan critique (see mk:plan-ceo-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。
Analyze task trajectories to propose reusable SKILL.md candidates from successful patterns
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。
다중 세션 transcript에서 반복 실패 패턴(instinct)을 자동 채굴하여 신규 skill candidate 생성
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。
Prepare qualified Fusion CAM setups, operations, toolpaths and quarantined NC candidates using explicit manufacturing assets and authority. Never controls physical equipment.
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。
Judge subskill for simmer. Scores a candidate artifact against user-defined criteria on a 1-10 scale and produces ASI (highest-leverage direction) for the next generator round. Supports judge-only, runnable evaluator, and hybrid evaluation modes. Do not invoke directly — dispatched as a subagent by the simmer orchestrator.
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。
Reflect subskill for simmer. Records iteration results in trajectory table, tracks best candidate, handles regression rollback, and passes ASI forward to the next round. Supports both single-file and workspace modes. Do not invoke directly — called by simmer orchestrator after each judge round.
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。
Safely localize unknown Unity games, especially Japanese-to-Chinese projects. Use to discover Unity version/layout/backend, migrate translations across game versions, extract occurrence-mapped text from serialized assets, embedded CSV, bytecode, UI, Localization StringTables, Utage, or middleware, bridge rows through AiNiee-style translation, validate and transactionally write approved candidates, audit residual and runtime-only text or AutoTranslator diffs, diagnose TMP fonts and Addressables/IL2CPP failures, locate saves, or acceptance-test SHA-verified patches.
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。
Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).
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。
Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.
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。
Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.
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。