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Top-level orchestrator for the Gaia Skill Tree evidence verification pipeline. Run this when you want to do a full evidence pass — collecting raw sources, verifying live GitHub star counts, running Phase 2B benchmark-source verification, adversarially auditing for quality/formatting issues, and checking that every URL is reachable — all in one coordinated sequence. Trigger phrases: "run the evidence pipeline", "full evidence pass", "verify evidence", "evidence verification pipeline", "run ev-pipeline", "check all evidence", "audit the data lake", "evidence quality sweep", "refresh evidence", "validate evidence sources". Also aliased as /evidence-verification-pipeline. Use the individual sub-skills (ev-collection, ev-star-verification, ev-adversarial-audit, ev-link-validation) only when you need to re-run one phase in isolation; for end-to-end work, always start here.

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 4 of the feature-pipeline suite. Use this after /fp-review has posted a sandbox review comment — it reads the outstanding checkbox items, applies fixes, then watches CI until green (or exits cleanly so you can retry). Trigger phrases: "address review items", "fix review feedback", "resolve review checklist", "watch CI", "poll CI", "check if CI is green", "iterate on PR", "fp-iterate", "Phase 4", "after review comment", "fix the review issues and watch CI". Always bounded — max 10 poll rounds, never loops forever. Exits with a clear status: green (hands off to /fp-drift), still-pending (re-invoke), or failed (diagnose before touching anything).

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。

Orchestrate a whole-registry sweep of Gaia — fan out 13 parallel audit agents across every skill, run adversarial verification, surface Semantic Fusion candidates, propose new generic skill references, and synthesize a publish-ready HTML report under docs/meta/reports/. Use this skill for broad, systemic analysis across many skills at once: "run a meta sweep", "sweep the meta", "full meta audit", "audit the whole registry against META.md", "widespread nomenclature issues", "find all skills missing a GitHub link", "produce a meta report", "check for evidence type mismatches across the registry", "find skills that document a third-party tool with no attribution", or explicitly types /gaia-meta-sweep. This is the registry-wide macro companion to /gaia-meta-audit (a prioritized queue, single-pass) and /gaia-audit (fixes one skill). Use it when you need the full surface — 13 audit dimensions, adversarial verification, fusion proposals, and a durable findings artifact — not just a queue.

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 provides comprehensive guidance for setting up and configuring test environments for VS Code extension projects. Use when initializing a new test infrastructure, configuring test runners (Vitest), setting up CI/CD test pipelines, integrating coverage tools (v8/c8), or troubleshooting test configuration issues.

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 provides systematic bug detection and discovery capabilities for VS Code extensions. Use when searching for hidden bugs, analyzing code for potential issues, investigating suspicious behavior, performing code audits, or proactively finding bugs before they manifest. Covers static analysis patterns, dynamic analysis techniques, code smell detection, and systematic codebase investigation.

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 provides expert-level guidance for debugging and fixing bugs in VS Code extensions. Use when investigating runtime errors, fixing memory leaks, resolving WebView issues, debugging activation problems, fixing TypeScript type errors, or troubleshooting extension communication failures. Covers systematic debugging workflows, common bug patterns, root cause analysis, and prevention strategies.

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 provides expert-level guidance for implementing VS Code WebView features. Use when creating WebView panels, implementing secure CSP policies, handling Extension-WebView communication, managing WebView state persistence, optimizing WebView performance, or debugging WebView rendering issues. Covers security best practices, message protocols, and VS Code-specific WebView 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。

Product Owner - scans codebase and creates or amends GitHub issues with PRD. Use when: PRD, feature, issue, create issue, amend issue, what should we build, plan feature, design feature, scope, requirements.

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。

Implement, migrate, review, and diagnose iOS Liquid Glass interfaces in SwiftUI and UIKit. Use for system chrome, custom glass effects, transitions, accessibility, and version-specific issues in native iOS apps.

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。

Reviews JUCE audio plugin code for JUCE-specific correctness issues: thread safety, APVTS parameter patterns, MessageManager usage, ValueTree, and MIDI handling. Use when the user asks to review a JUCE plugin, check a processBlock, audit parameter handling, or asks "is this JUCE code safe?". Trigger on phrases like "review my JUCE plugin", "check my AudioProcessor", "is this APVTS usage correct?", or when you see AudioProcessor, AudioProcessorEditor, or AudioProcessorValueTreeState in the code.

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。

Profiling strategy for audio CPU issues — xruns, spikes, and buffer underruns. Use when the user reports CPU overload, audio glitches under load, or performance regressions. Trigger on phrases like "I'm getting xruns", "my plugin causes CPU spikes", "dropouts under high voice count", "audio thread is too slow", "how do I profile my DSP". References audio-numerics-review for denormal slowdown and audio-dsp-review for lock contention.

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。

Reviews audio DSP and audio processing code for realtime safety violations. Use whenever the user asks to review, audit, or check audio processing code — including plugin process callbacks, audio engine render functions, DSP implementations, or any code that runs on the audio thread. Trigger on phrases like "review my processBlock", "check this DSP code", "is this safe for the audio thread?", "review my JUCE plugin", or when you see an audio callback and spot potential realtime violations. Flag issues proactively even when the user hasn't explicitly asked for a 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。

Systematic checklist for diagnosing audio artifacts. Use when the user describes a sound quality problem — clicks, pops, crackling, silence, DC offset, aliasing, phase issues, or latency problems. Trigger on phrases like "why do I hear clicks?", "my output has DC offset", "there's aliasing in my oscillator", "phase is wrong in mono", "dropout when CPU spikes". Pairs with audio-dsp-review (realtime safety) and audio-numerics-review (numeric correctness) — use those to fix once the cause is found.

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。

Reviews YUP audio plugin code for YUP-specific correctness issues: AudioProcessor lifecycle, AudioParameterBuilder and AudioParameterHandle usage, editor gestures, AudioBusLayout handling, CLAP/VST3 wrapper contracts, state recall, and MIDI safety. Use when the user asks to review a YUP plugin, check a processBlock, audit parameter smoothing, or asks "is this YUP code safe?". Trigger when you see yup::AudioProcessor, yup_audio_plugin, AudioParameterBuilder, AudioParameterHandle, AudioProcessorEditor, or createPluginProcessor in the code.

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。

Production-grade TypeScript reference for React & Next.js frontend development. Covers type narrowing, component Props, generic hooks, discriminated unions, as const, satisfies, Zod validation, TanStack Query, server/client boundaries, forms, state management, performance, accessibility, debugging, and code review. Use when the user works with TypeScript in React or Next.js: type errors, Props design, generics, API typing, SSR/CSR boundaries, hydration issues, form validation, state management, performance, or code review. Also use for "how should I type this?", "why does this type error happen?", or any architectural decision involving TypeScript in a frontend context.

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。

Convert current conversation context into a structured Product Requirements Document (PRD) and optionally post it to GitHub Issues. Use before /supergraph:plan when requirements came from a conversation, user story, or feature discussion rather than a formal spec.

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。

Plan-aware graph-enhanced code review before merge. CRITICAL issues block merge. Use after fix/verify.

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。

Review and resolve PR comments from GitHub. Validates each comment, fixes legitimate issues.

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 a day-one patch for a game launch. Scopes, prioritises, implements, and QA-gates a focused patch addressing known issues discovered after gold master but before or immediately after public launch. Treats the patch as a mini-sprint with its own QA gate and rollback plan.

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 reviewing or improving frontend accessibility, semantic structure, keyboard support, focus management, ARIA labels, screen reader behavior, WCAG 2.2 issues, touch accessibility, or assistive-technology regressions; Chinese triggers include accessibility, accessibility, a11y, WCAG, screen reader.

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 a task needs the judgment of a Compensation and Benefits Manager — designing or evaluating a pay structure/leveling system, benchmarking compensation against market, deciding on a benefits package tradeoff, or auditing for pay equity issues. Narrower and more specialized than the broader hr-people-manager role's compensation-adjacent heuristics.

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。

Build comprehensive epigenomic profiles for tissues or cell types using ENCODE data. Use when the user wants to characterize chromatin states, assemble histone modification panels, create epigenomic landscapes, run ChromHMM segmentation, identify super-enhancers or bivalent domains, profile regulatory elements across a biosample, or understand epigenetic regulation in a specific biological context. Covers histone marks, chromatin accessibility, TF binding, transcription, DNA methylation, and 3D genome structure.

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。

Compare ENCODE experiments across different biosamples, tissues, or cell lines to identify tissue-specific regulatory patterns. Use when the user wants cross-tissue comparison, cell-type comparison, tissue-specific elements, differential chromatin, biosample matching, disease vs normal comparison, developmental time course, constitutive vs variable regulation, or multi-tissue data availability mapping. Handles batch effect detection, biosample hierarchy, and comparison 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。

Install bioinformatics tools for ENCODE data analysis. Covers CLI tools (BWA, STAR, samtools, MACS2), R/Bioconductor packages (DESeq2, Seurat, ChIPseeker), Python packages (Scanpy, deeptools), and Nextflow pipeline infrastructure. Generates conda environments, R install scripts, and Python requirements. Use when the user needs to set up a bioinformatics workstation, install tools for a specific assay, create reproducible environments, or troubleshoot dependency issues. Trigger on: install tools, set up environment, conda create, bioinformatics setup, install R packages, install Bioconductor, install pipeline tools.

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。