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概要と使いどころ
Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.
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。
Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas.
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。
Systematically QA test a web application. Use when asked to "qa", "QA", "test this site", "find bugs", "dogfood", or review quality. Four modes: diff-aware (automatic on feature branches — analyzes git diff, identifies affected pages, tests them), full (systematic exploration), quick (30-second smoke test), regression (compare against baseline). Produces structured report with health score, screenshots, and repro steps.
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。
Code review guidelines covering code quality, security, and best practices.
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 changed code for reuse, quality, and efficiency, then fix any issues found. Sizes review effort to the diff — trivial edits get a self-review, substantial edits get parallel agents. Renamed from /simplify to avoid collision with Claude Code's built-in simplify skill.
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。
Tune moflo's memory stack for speed, RAM, and index quality. Covers HNSW parameters (M, efConstruction, ef), vector quantization, batch operations, and common bottlenecks. Use when scaling past ~100k entries or when search latency regresses.
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 creating high-quality scientific or medical infographic images or prompts, including review figures, disease maps, teaching diagrams, mechanism summaries, classification overviews, timelines, matrices, and academic visual explainers. If the user asks to generate the image, use ChatGPT image generation.
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。
Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".
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。
Post-implementation review orchestrator. Launches 5 parallel background sub-agents: Oracle (goal/constraint verification), Oracle (code quality), Oracle (security), unspecified-high (hands-on QA execution), unspecified-high (context mining from GitHub/git/Slack/Notion). All must pass for review to pass. MUST USE after completing any significant implementation work. Triggers: 'review work', 'review my work', 'review changes', 'QA my work', 'verify implementation', 'check my work', 'validate changes', 'post-implementation 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。
Mechanical consistency check for game design — answers "is the design consistent with itself?" — plus changed-scope impact analysis across all three GDD axes: systems, flows, and content types. Offline and deterministic: greps the facts registry against the documents (numbers, terms, IDs, duplicate & dangling references, roster↔disk, map freshness, Depends 3-way, status/version, AC presence, placeholder leaks, flow & content schema), and from a git diff computes which dependent systems, flows, and content a change affects. Scope is inferred: a named document checks that document; an unverified diff triggers a changed-scope pass; otherwise everything. Use when the user wants a consistency or impact check, e.g. "verify the design", "what did this change affect", "find broken references in the design". Conflicts triage into four tracks; the entailed fixes hand off to /unikit-gd-apply in one pass. This is the mechanical pass — for a subjective "is this design good" quality critique use /unikit-gd-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。
Autonomous iterative refactoring workflow. Scans for tactical code quality improvements (DRY, dead code, naming, complexity), implements through SMEs, verifies with QA, commits atomically, and loops until no improvements remain.
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。
Full-lifecycle project workflow. Takes batched tickets, implements via /implement-batch, runs smoke tests, then executes a comprehensive quality pipeline (refactor, review-arch, review-test, tidy-docs, review-release). Maximizes autonomy with andon cord escape.
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。
Multi-ticket batch workflow. Takes a batch of tickets, plans execution order, implements each via /implement in autonomous mode, runs cross-cutting quality passes, and presents results for final 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。
Iterative development workflow that coordinates implementation, refactoring, QA, and documentation agents to complete features systematically. Use when the user wants a full development workflow with quality checks.
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。
Autonomous bug-elimination loop. Iteratively invokes /bug-hunt and /implement-batch until findings converge below an operator-specified severity floor. At termination, runs /review-test scoped to the run's new reproducing tests and fixes quality issues above the floor. Auto-approves /bug-hunt and /review-test ticket proposals; pulls the andon cord on contested findings, repeated implementation failure, hard-cap exhaustion, or breaking-change proposals. Optional /refactor finisher.
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。
Comprehensive test suite review across five phases. Surveys unit coverage, integration coverage, E2E (browser) coverage for webapps, fuzz coverage, and test quality — in that order. Advisory only — produces findings and proposes tickets; does not implement test changes.
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 this skill when creating, scaffolding, reviewing, or maintaining Python Home Assistant custom integrations, including work on custom_components, manifests, config flows, options flows, config entries, platforms/entities, DataUpdateCoordinator, integration automation triggers, diagnostics, repairs, discovery/networking, authentication, HACS-ready repositories, or Home Assistant integration quality scale compliance.
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。
Diagnose and test Claude Code skills against Anthropic's 7 principles. Scans SKILL.md files, checks 8 rules (gotchas, description, allowed-tools, file-size, structure, frontmatter, conflicts, usage-hooks), classifies skill types, generates prescriptions, and runs eval tests. Use when checking skill quality, auditing skills, testing skills, or before publishing skills. Triggers on "스킬 점검", "스킬 진단", "스킬 테스트", "check skills", "audit skills", "test skills", "skill health", "pulser", "pulser eval".
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。
Unified cqs CLI dispatcher — semantic code search, call graph navigation, impact analysis, quality tools, notes, and infrastructure. Use `/cqs <command> [args]` for any cqs operation.
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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
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。
GAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper.
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。
Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.
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。
Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific 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。
Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.
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。