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概要と使いどころ
gnomAD v4 population variant frequencies via GraphQL API. Allele counts and frequencies stratified by ancestry (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID), gene-level constraint (pLI, LOEUF, missense z), and coverage. Identify rare or constrained variants. For clinical pathogenicity use clinvar-database; for GWAS use gwas-database.
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CLI for VCF/BCF: filter, merge, annotate, query, normalize, compute stats. Core post-variant-calling: quality filtering, multi-sample merging, rsID annotation, genotype extraction. Samtools companion in HTSlib. Use GATK for complex indel realignment during calling; use VCFtools for population genetics stats.
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Query NCBI ClinVar via E-utilities for variant clinical significance, pathogenicity, disease associations. Search by gene/rsID/condition/review status; returns ClinSig, submitter data, conditions, HGVS. For GWAS use gwas-database; for variant consequence prediction use Ensembl VEP.
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Query NCBI dbSNP for SNP records by rsID, gene, or region via E-utilities and Variation Services REST API. Retrieve alleles, MAF, variant class (SNV/indel/MNV), clinical links, cross-DB IDs (ClinVar, dbVar, 1000G). Free; 3 req/sec (10 with key). For clinical pathogenicity use clinvar-database; for population frequencies use gnomad-database.
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Multi-Omics Factor Analysis v2 (MOFA+) with mofapy2. Jointly decompose omics layers (scRNA, ATAC, proteomics, methylation) into latent factors capturing major variation. Multi-group designs. AnnData views → MOFA object → train → variance explained → correlate factors with metadata → visualize/cluster → enrich top loadings.
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BigQuery/Snowflake接続、EDA、可視化、Marimoノートブック作成を行うサブエージェント。 データ分析関連の4つのルール（data_analysis, visualization, notebook, marimo_variable_naming）を統合。 「データ分析して」「BigQueryに接続」「EDAを実行」「Marimoで分析」等のリクエストで発動。
Set up environment variables, .env files, and configuration management. Use when configuring environment variables, creating .env files, or managing app configuration.
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Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
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Stack Honey onto Superpowers-style workflows (subagent-driven-development, dispatching-parallel-agents, executing-plans) and any orchestration that dispatches fresh subagents. Dispatched subagents run in isolated context and do NOT inherit the session's Honey hook, so they emit full-fat code and verbose reports. Use when you are about to dispatch implementer/reviewer/fixer/parallel subagents: it gives the exact Honey directive to paste into each dispatch prompt, with a worker variant and a reviewer variant, so the levers apply inside the subagents too — where the multiplied cost actually is.
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Generate high-converting ad copy for Google Ads, Meta (Facebook/Instagram), LinkedIn, and TikTok. Creates multiple variations with A/B testing in mind. Includes headlines, descriptions, CTAs, and platform-specific formats. Use when creating ads, generating ad variations, or A/B testing ad copy.
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OI-3 spike harness — the FORKED (context fork) readability-guidance variant. Same payload as the inline variant but declared to run in a forked context. Use only when a spike consumer skill invokes it.
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OI-3 spike harness — heavy consumer, ADVERSARIAL arm. Worst-case early-exit test: the mid-workflow Skill call has no continuation guardrail and the guidance skill ends with a final-sounding anchor. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.
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Generates 20+ scroll-stopping ad hooks organized by psychological angle (pain, curiosity, social proof, contrarian, urgency) with platform-specific formatting, A/B variations, and psychology breakdowns
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Fix Flutter macOS app crash at launch due to FFmpeg Kit library loading failure. Use when: (1) macOS app builds successfully but crashes immediately on launch, (2) Crash log shows "Library not loaded: libfontconfig.1.dylib" or similar homebrew library, (3) Error mentions "code signature not valid for use in process" with "different Team IDs", (4) Using ffmpeg_kit_flutter_new (Full GPL variant). The Full GPL variant has external dependencies on homebrew libraries that fail macOS code signing. Switch to ffmpeg_kit_flutter_new_min.
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Automatically repair C/C++ code violations detected by TLA+ model checking. Takes a program, TLA+ specification, and TLC counterexample trace as input, then generates minimal code modifications to eliminate the violation. Use when: (1) TLC model checker reports an invariant violation, deadlock, or temporal property failure, (2) You have a counterexample trace and need to fix the corresponding code, (3) You need to understand how a TLA+ violation maps to program-level bugs, (4) You want to validate repairs by re-running TLC. Supports safety properties (invariants), liveness properties (temporal logic), and deadlock detection.
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Automatically generate TLA+ specifications from source code (C/C++, Python) for formal verification of distributed systems. Use when users need to: (1) Generate TLA+ specs from program implementations, (2) Model distributed systems, consensus protocols, or concurrent algorithms, (3) Extract state variables, actions, and invariants from code, (4) Create formal specifications for model checking with TLC, (5) Verify safety and liveness properties of distributed systems. Particularly effective for message-passing systems, replication protocols, consensus algorithms, and distributed transactions.
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Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).
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Generate formal specifications including preconditions, postconditions, invariants, and contracts from code or requirements. Use this skill when documenting APIs, creating formal verification annotations, defining function contracts, specifying class invariants, writing design-by-contract code, or preparing code for formal verification. Supports multiple specification languages including JML, ACSL, Dafny, Eiffel contracts, and documentation annotations.
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Automatically simplify and minimize TLA+ specifications by reducing redundant state variables, merging equivalent actions, and minimizing invariants while preserving specified properties. Use when working with TLA+ specifications that need optimization, simplification, or reduction. Triggers when users ask to minimize, reduce, simplify, or optimize TLA+ specs, or when they want to remove redundancy from formal specifications while maintaining semantic equivalence.
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Generate formal specifications (definitions, predicates, invariants, pre/post-conditions) in Isabelle/HOL or Coq from informal requirements, source code, pseudocode, or mathematical descriptions. Use when users need to: (1) Formalize algorithms or data structures, (2) Create function specifications with contracts, (3) Generate predicates and properties for verification, (4) Translate informal requirements into formal logic, (5) Specify invariants for loops or data structures, or (6) Create formal definitions for mathematical concepts. Supports both Isabelle/HOL and Coq equally.
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Generate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing. Use when creating test cases for robustness testing, generating adversarial inputs, testing error handling, finding edge cases, or security testing. Produces Python test code with fuzzing inputs for strings, numbers, and structured data focusing on edge cases, invalid inputs, and random valid inputs. Triggers when users ask to generate fuzz tests, create randomized test inputs, test edge cases, find bugs through fuzzing, or generate adversarial test cases.
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Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.
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| Production-tested setup for Tailwind CSS v4 with shadcn/ui, Vite, and React. Use when: initializing React projects with Tailwind v4, setting up shadcn/ui, implementing dark mode, debugging CSS variable issues, fixing theme switching, migrating from Tailwind v3, or encountering color/theming problems. Covers: @theme inline pattern, CSS variable architecture, dark mode with ThemeProvider, component composition, vite.config setup, common v4 gotchas, and production-tested patterns.
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Think and work like an expert Genomicist. Use when a task calls for Genomicist judgment. Reasons from reference-relative coordinates, haplotypes, variant classes, and sequencing-as-measurement through GATK/DeepVariant, VEP/ClinVar/gnomAD, GIAB/hap.py benchmarking, and ACMG/AMP-ClinGen frameworks while treating build mismatches, paralog/pseudogene and GC dropout artifacts, contamination and index hopping, batch effects, and annotation drift as first-class failure modes.
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。