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概要と使いどころ
Prepares for hiring manager rounds at Staff+ (L6+) level — scope of impact, influence without authority, ambiguity navigation, mentorship, strategic thinking. Use when practicing HM rounds or calibrating story depth for target level. Activate on "hiring manager round", "HM screen", "staff level", "scope of impact". NOT for coding interviews, system design, behavioral/values rounds, or resume writing.
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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。
Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).
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 applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.
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 skill matches by fit, performance history, and contextual relevance. Applies multi-factor scoring including success rate, resource usage, and task alignment. Activate on 'rank skills', 'best skill for', 'skill ranking', 'compare skills', 'optimal skill'. NOT for semantic matching (use dag-semantic-matcher) or skill catalog (use dag-skill-registry).
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。
Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).
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。
Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).
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。
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
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 genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.
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。
Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.
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。
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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。
Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.
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。
Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
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。
Agile product ownership toolkit for Senior Product Owner including INVEST-compliant user story generation, sprint planning, backlog management, and velocity tracking. Use for story writing, sprint planning, stakeholder communication, and agile ceremonies.
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 articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
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 real-time CGM over no CGM for outpatients with type 2 diabetes who use insulin and/or sulfonylureas and are at high risk for hypoglycemia to detect and prevent low glucose events. Consider when initiating CGM in T2D patients on insulin/SU with a history of severe hypoglycemia or impaired awareness of hypoglycemia (IAH).
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。
This skill guides evaluation for malignancy using imaging in patients with a history of adrenal adenoma (post‑adrenalectomy Cushing’s syndrome) when the adenoma demonstrates a CT density of ≥10 Hounsfield units or when pathology is consistent with possible carcinoma. Trigger phrases include “CT density ≥10 HU”, “possible carcinoma on pathology”, and “post‑adrenalectomy CS patient with adrenal adenoma history”.
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。
Calculates the lowest effective glucocorticoid dose (dexamethasone or prednisone) to normalize blood pressure and serum potassium in glucocorticoid-remediable aldosteronism (GRA/FH-I) by titrating to biochemical and clinical targets. Indicated when initiating medical treatment for confirmed GRA, triggered by findings such as early-onset hypertension, family history of stroke before age 40, spontaneous hypokalemia, or suppressed plasma renin activity with elevated aldosterone.
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 genetic counseling for patients with primary adrenal insufficiency (PAI) when a monogenic disorder is suspected. Use when PAI patient presents with early-onset disease, syndromic features, consanguinity, family history, or negative autoimmune antibodies.
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。
Work a primary-prevention patient through the 2026 ACC/AHA multisociety dyslipidaemia guideline CPR framework — Calculate (10-year and 30-year PREVENT-ASCVD), Classify (low / borderline / intermediate / high), Personalize (demographic, clinical, biomarker, and reproductive risk enhancers), and Reclassify (coronary artery calcium scoring where appropriate). Use when a clinician asks how to assess ASCVD risk for primary prevention, who needs a statin, borderline risk patient what to do, intermediate risk should I start a statin, risk enhancers to look for, premature family history of ASCVD how does it change risk, pregnancy-related risk factors, PCOS and cardiovascular risk. 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。
Write a blog post in the preferred style related to amux
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 amux for a new release. Updates version numbers, docs, getting-started, release notes, and blog post. Does NOT run the release script.
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。
Generate user-facing App Store release notes from git history since the last tag.
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。
Agile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use when writing user stories, creating acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing the backlog.
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。