新規条項の誕生ゲート(5 宛先の routing 判定 → 常駐集合の予算処理 → 台帳記録)。規範文書に条項を追加・移動・削除するときに実行する
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概要と使いどころ
Create an aggregated Epic PR from a `cc-epic-<Issue number>` branch to the default branch. Takes the Epic Issue number as argument, assumes sub-PRs have already been merged into the epic branch, and automatically generates the PR title and description from the commit log against the base branch (including sub-PR / sub-Issue references). The PR is posted via `gh pr create` with no label and `Closes #<Epic Issue>`.
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。
Create or update the Pencil (`.pen`) design for a UI implementation Issue before any code is written, then open a design-only PR. Takes the Issue number as argument, delegates `.pen` edits to the pencil-design-updater agent, pushes `.pen` and snapshot PNGs on the fixed `cc-ui-design-<Issue number>` branch, and opens a PR referencing the Issue with `Refs #<N>` (never a closing keyword).
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。
Philosophy-first writing partner for Medium with tactical depth. Combines human storytelling values with production-ready technical content tools. Six content modes based on staff pick analysis, mode-specific quality gates, evidence requirements, annotated examples, and comprehensive scripts integration. Designed for sustainable practice, not algorithm gaming.
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Guides agents through scorecards, outcomes, model profiles, and eval suites. Use when running /cc-scorecard or /cc:scorecard, measuring a deliverable, or checking workflow gates with suite-run.
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frontend
無料Work on the dashboard's frontend — the vanilla JS/CSS page with no framework or bundler. Use this whenever a task touches `static/nowplaying.js`, `static/style.css`, the Jinja templates (`templates/nowplaying.html`, `_icons.html`), the accent-color tinting, the karaoke lyrics sync, the now-playing poll, or the empty-state mosaic / recent-plays pile. Covers how the page is wired, how it reads i18n and data from the server, the ESLint gate, and the Android bridge, so changes match the existing patterns.
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Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.
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Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering populations without predefined gates, choosing a clustering algorithm, selecting the number of metaclusters, or annotating clusters into cell types.
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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Resolves rsIDs, navigates RsMergeArch/SNPHistory merge chains, and converts between rsID, SPDI, HGVS, and VCF representations using the dbSNP Build 156 JSON architecture. Use when normalizing variant identifiers, joining variant databases by cluster ID, or tracking deprecated rsIDs through historical merges.
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Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.
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Detect transcription factor binding footprints in ATAC-seq using TOBIAS, HINT-ATAC, Wellington, or scprinter. Use when identifying bound TF sites within accessible regions, correcting Tn5 insertion bias before footprinting, choosing between cleavage-based and aggregate-based footprinters, or comparing differential TF activity between conditions.
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Run the authorized defensive security workflow — domain security-* skills, authorization gate, risk-based routing, hardening, and review.
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Evaluate deliverable quality — scorecard, outcome tracking, regression checklist, and aggregate stats.
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Run OpenSpec backlog delivery — validate BACKLOG.md, plan TaskCards, orchestrate agents by phase, review gate, and update backlog state.
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Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.
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Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.
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Use when the project follows API-first / OpenAPI-first approach: generating controller interfaces, DTOs, and clients from an OpenAPI spec. Use when you see openapi.yaml, openapi-generator-maven-plugin, or ApiDelegate pattern in the project.
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Spec-locked TDD with a mutation-testing gate — refine the intent into an immutable Gherkin contract (SHA-256 frozen), implement under the three laws of TDD, pass a judge audit, and merge only if every mutant dies.
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Use when working with domain models, aggregates, value objects, domain events, or repositories in a DDD-style project. Ensures rich domain model over anemic CRUD.
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事前設計が必要な複数段階のCursor委任作業で、依存関係・担当・検収条件を持つ永続PLANを作成・実行する。
Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.
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Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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。