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Use when the user wants their way of working with Claude assessed — "review my session", "how am I using Claude Code", "audit my setup / workflow", "am I following best practices", "coach me", "what should I improve" — or pastes a Claude.ai/Cowork/Tag transcript and asks how they could have worked better. Do NOT use for reviewing code changes (that is code review) or for teaching a product from scratch (use academy-learn); this skill only reviews how the human works with Claude.

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Perform structured code reviews focusing on correctness, readability, security, and maintainability. Use this skill when reviewing pull requests, evaluating code changes, or establishing review standards for a team.

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Create a well-formed git commit from current changes using session history for rationale and summary; use when asked to commit, prepare a commit message, or finalize staged work.

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Push current branch changes to origin and create or update the corresponding pull request; use when asked to push, publish updates, or create pull request.

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-agent management workflow — task delegation, progress monitoring, quality verification with regression testing, feedback delivery, and cross-review orchestration. Use this skill when coordinating multiple agents on a shared task, monitoring delegated work, ensuring quality across agent outputs, or implementing a multi-phase plan (3+ phases or 10+ file 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。

Update the CHANGELOG.md with the description of the changes between the last released version and the new one

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Runs a sample in an instrumented sandbox to observe behavior: process tree, file and registry changes, network activity, and persistence, then summarizes the behavioral report into capabilities and IOCs. Activates for requests to detonate a sample, do dynamic or behavioral analysis, or interpret sandbox output.

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Compares two related malware samples to surface what changed between variants using section-level hashing, import-set differences, and fuzzy similarity scoring. Activates for requests to diff two samples, compare malware variants, or measure similarity between two binaries for variant tracking.

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Analyzes how a ransomware sample encrypts files: identifying the crypto scheme (symmetric/asymmetric/hybrid), key handling, file targeting and extension/marker changes, shadow-copy deletion, and ransom-note artifacts. Activates for requests to analyze ransomware encryption, assess decryptability, or study ransomware behavior.

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Deep code review - Elixir idioms, Phoenix patterns, architecture, safety. Launches 3 parallel reviewers + plan-reviewer gate. Use when: review, review this, code review, check this code, review my changes, is this good, what do you think, code smells.

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Route repository-qualified changes across Pulp and Vellum using Pulp's exact ownership projection. Use when a change touches design import, Chromium authoring, DesignIR, visual harness, screenshot, Skia/Dawn, runtime assets, or another path represented by `.github/vellum-ownership.json`; when deciding whether work originates in Pulp or Vellum; or when validating the cross-repository routing contract.

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Real-DAW (REAPER) functional smoke for reload/editor/format-adapter changes — opt-in, scoped, headless-safe, zero-pollution

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Keep a plugin/demo repo's screenshots in sync with its UX. When editor / UI / design-token code changes in an opted-in repo (one carrying .pulp/screenshots.toml), re-capture and refresh every place each shot is consumed — README images, web-demo Open Graph (og.png) images, and gallery thumbnails — consistently across the WAM and WCLAP demo sites. Two capture routes: native headless (render_to_png / pulp-screenshot, Skia) for README / native-editor shots, and live-web headless Chrome (playwright-core) for WAM/WCLAP demo OG + gallery images. Backfills demos missing OG images. This is a PORTING / consistency tool — it re-shoots a repo's OWN UI, it never clones another product's art.

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Master orchestrator for B2B SaaS content creation and the editorial production system behind it. Use this skill to produce blog articles, whitepapers, case studies, video scripts, newsletters and ghostwritten thought leadership, to develop content strategy, and to run the publishing line itself — content briefs, editorial calendars and production capacity, review and approval workflow, subject-matter-expert access, the style guide, an AI-drafting policy, and a content inventory with owners. Also triggers on: blog, article, case study, whitepaper, newsletter, video script, ghostwriting, content calendar, editorial, thought leadership, content strategy, content brief, content operations, content ops, editorial workflow, managing editor, who reviews this, our content pipeline is stuck, content is stuck in review, content audit, content inventory, content governance, style guide, house style, SME interview, expert interview, freelance writers, AI content policy, how much content can we actually produce. Also owns the customer academy and customer education: online courses, onboarding curriculum, tutorials, certification programs, digital credentials and badges, exam and pass-mark design, course freshness when the product UI changes, and whether the academy should sit behind a login. Triggers on: customer academy, customer education, online course, course library, learning path, curriculum, tutorial, certification program, certified users, digital badge, Open Badges, recertification, exam, pass mark, LMS, Skilljar, our tutorial shows the old UI, our course is out of date, should the academy be gated, do certified customers renew better.

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pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position.

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Diagnose whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).

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Create graph-informed implementation plans before writing code. Use before any non-trivial task. Skip for small changes (≤2 files, <20 lines, no hub/bridge).

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Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Kysely, Django, TypeORM, golang-migrate).

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Strict test-driven development for behavior changes. Requires verified RED before production code, minimal GREEN, and refactor only after passing tests.

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Generates EditMode and PlayMode tests using NUnit, mapped from GDD Gherkin scenarios. Use when writing tests, validating features, ensuring code quality, or verifying behavior after changes. Triggers on requests like "write tests for this", "test the health system", "add unit tests", "verify this works", "add PlayMode tests", "test this feature", "write integration tests", "make sure this doesn't break", "add test coverage", "TDD this feature", or any task involving test creation, test-driven development, or quality assurance. Maps Given/When/Then scenarios directly to test methods using NUnit assertions.

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Pre-commit code quality review for Unity C# code. Checks against Rules, CODING_STANDARDS.md, NAMING_CONVENTIONS.md, and GDD Gherkin test coverage. Use when code has been written and tests are passing but before committing. Runs automatically at Step 8.5 of /uw-cmd-implement-feature. Triggers on "review this code", "check my implementation", "pre-commit review", "code review", "check before commit", "is this ready to commit", "review my changes", or any request to validate code quality before committing.

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Lightweight design spec for small changes — tuning adjustments, minor mechanics, balance tweaks. Skips full GDD authoring when a system GDD already exists or the change is too small to warrant one. Produces a Quick Design Spec that embeds directly into story files.

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Use when running existing project validation commands and fixing failures after code changes, including lint, type-check, unit/integration test, build, CI, or local script failures. Do not use when the task is to design or author new component/E2E tests; Chinese triggers include validation, check failures, fix failures, CI failures.

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Use when designing, implementing, or reviewing responsive frontend layouts, mobile-first breakpoints, container queries, fluid grids, data-dense tables, touch targets, safe areas, orientation changes, viewport overflow, or cross-device UI behavior; Chinese triggers include responsive layouts, mobile-side adaptation, breakpoints, container queries, horizontal and vertical screens, touch targets.

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。