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Facilitate a full design-thinking engagement (Empathize, Define, Ideate, Prototype, Test) grounded in real user evidence. Use whenever the user wants to understand users deeply and design a solution for them: "run design thinking", "understand our users", "design user interviews", "synthesize these interview notes", "build personas", "write How-Might-We questions", "prototype this concept", "design a usability test", "our users are churning and we don't know why", in any language ("tư duy thiết kế", "nghiên cứu người dùng", "phỏng vấn khách hàng", "デザイン思考", "设计思维"), even when they never say "design thinking". Also use when the user drops raw interview notes, transcripts, or survey exports and wants insights, personas, or product decisions. Not for "is there a market for X" desk questions (market-researcher).

Run the Incan review/fix loop end to end: review, fix actionable findings, and review again until clean or blocked. Use when the user wants an autonomous review-and-repair pass or says /review-and-fix.

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Review PyTorch pull requests for code quality, test coverage, security, and backward compatibility. Use when reviewing PRs, when asked to review code changes, or when the user mentions "review PR", "code review", or "check this PR".

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Review other contributors' PRs to learn the codebase and build maintainer trust. Guides structured diff reading, helps formulate review comments, and teaches how to distinguish blocking issues from nits. Use when you want to learn a codebase by reviewing PRs, build trust with maintainers before contributing code, or practice code review on OSS repos. Not for reviewing your own PR — use oss-submit-pr for that.

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Use when reviewing, debugging, or giving feedback on Go code in this repo (exercises/part2, website/tools, or any *.go file) or when asked for a "code review", "review this Go", "revisa este código Go", or "crítica de código". Reviews for correctness, idiomatic Go, error handling, resource cleanup, concurrency, performance, and security, and always ends with a test-thinking pass (what a test would catch). For exercises/part2, prefer the go-exercise-reviewer skill which knows the module-mode quirk; this skill covers general Go anywhere in the repo.

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Use when reviewing, debugging, or improving frontend code in this repo (website/ HTML, CSS, JS, service workers, PWA assets) or when asked to "review frontend", "revisa el html/css", "review JS", or "audita el frontend". Reviews for correctness, browser compatibility, accessibility, performance, security (XSS), responsive behavior, and PWA/service-worker correctness, and ends with a "what a user/reviewer would catch" pass.

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Use when the user asks for a code review by a fleet of specialized reviewer agents, wants multiple independent reviewer perspectives, or asks to run reviewers in single-pass or iterative fix-until-clean mode. Launches focused subagents for correctness, security, architecture, conventions, simplicity, UX, reliability, telemetry, testing, compatibility, and documentation review.

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Run a Duck Critic producer-critic loop: you (main) keep producing the plan/code/tests and gate your own work at checkpoints with a different-model critic, revising until it passes. Use when asked for rubber duck, ラバーダック, 別モデルレビュー, second opinion, critic, or a different-model code review, design review, plan critique, or review by another model/agent harness. Not for readability refactoring or code cleanup alone.

Create or review a reusable skill (SKILL.md) that packages a workflow, and decide whether the request should be a skill instead of a prompt, instruction, agent, or hook. Use when creating a new skill, extracting a workflow from a conversation, updating an existing skill, reviewing SKILL.md quality, or fixing weak skill triggering. Triggers on "create skill", "/create-skill", "new skill", "review skill", "fix skill trigger", "SKILL.md", "スキル作成".

Evaluate and review GO-CAM (Gene Ontology Causal Activity Model) activities / annotons — molecular-function typing, has-input, causal relations, complexes, evidence — and check their consistency with gene annotation reviews. Use when reviewing cached GO-CAMs under gocams/, filling a GoCamReview YAML (gocams/<id>/<id>-review.yaml), or grounding a module in GO-CAM models.

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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.

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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.

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-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.

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Security-focused code review identifying high-confidence exploitable vulnerabilities with two-axis severity/confidence scoring, OWASP 2025 alignment, per-finding Haiku verification, and false positive filtering. Optional GitHub PR posting. Use when user runs /security-review, /review:security-review, requests a "security review", "security audit", "vulnerability scan", or mentions "find vulnerabilities", "check for exploits".

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Write a literature review. Use when the user says "literature review", "lit review", "review the literature", "summarize prior research", "what does the research say about", "synthesize research on", "background section", "related work section", "what has been studied on this topic", or needs to systematically identify, synthesize, and critically evaluate existing research on a topic - even if they don't explicitly say "literature review".

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Review code for quality, bugs, and design issues. Use when the user says "review this code", "do a code review", "check this PR", "review this diff", "is this code good", "what's wrong with this code", "code quality check", or pastes code and wants feedback - even if they don't explicitly say "code review".

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Always use this skill before claiming any work is done, fixed, passing, or complete — even if it seems obvious. Also use when receiving code review feedback from any source (human or automated), when finishing a task or major feature, before committing or creating a PR, or when about to express satisfaction with work. Covers three practices: receiving feedback with technical rigor (no performative agreement), triggering code review via /code-review command or cook/fix pipelines, and verification gates (run the command, read the output, then make the claim). Never skip — the cost of false completion claims is higher than the cost of verifying. References: code-review-reception.md, requesting-code-review.md, verification-before-completion.md.

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 preparing for a Salesforce seasonal release — triaging release notes, reviewing Release Updates, opting into Sandbox Preview, and communicating change impact to stakeholders. Triggers: 'upcoming Salesforce release', 'release notes triage', 'Release Updates', 'sandbox preview opt-in', 'release readiness checklist', 'production upgrade date', 'feature impact', 'critical update', 'roll out the Spring release safely', 'is our org ready for the Spring release', 'release update settings metadata', 'FlowSettings release update field', 'release run sheet', 'preview sandbox refresh freeze', 'settings deploy flipped a release update'. Also covers: which Release Updates are deployable as Settings metadata, the release run sheet artefact, and the preview opt-in refresh-date rule. NOT for planning your own release train — use devops/release-management. NOT for user training and go-live comms — use admin/change-management-and-training.

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。

Configuring and running Pipeline Inspection in Sales Cloud: enabling the feature, mapping forecast categories into the inspection view, Days in Stage and other deal-change metrics, and pipeline review cadence for sales managers. Use when designing or improving how a team monitors deal health and pipeline movement. NOT for the Revenue Intelligence app, Einstein deal insights, or forecast-accuracy dashboards — use admin/revenue-intelligence-setup. NOT for forecast types, quotas, or forecast hierarchy setup — use admin/collaborative-forecasts. Keywords: pipeline review, coverage ratio, stage conversion, slippage, stale deals, days in stage, LastStageChangeInDays, LastStageChangeDate, AgeInDays, PushCount, ForecastCategoryName, enablePipelineInspection, PipelineInspMetricConfig, OpportunitySettings, review cadence, commit call.

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Qualitative quality review of game design documents — answers "is this design good, and does it follow best practices?" — across all three GDD axes: systems, flows, and content types. Fans out adversarial lenses (completeness, clarity, pillar alignment, systems-math, fantasy-delivery, feasibility, scope) plus domain lenses that check it against the game-design rules, each prompted to find problems rather than validate; produces a severity-graded verdict and a two-bucket report (apply-ready fixes vs research), handed to /unikit-gd-apply or /unikit-gd-explore in one move. Scope is inferred: a named system, flow, or content type reviews that document; "all" runs a cross-document review. Use when the user wants a critique, quality judgment, or rules check of the design, e.g. "review the combat GDD", "review the onboarding flow". This is the subjective quality pass — for a mechanical consistency check (numbers, terms, IDs, references matching the registry) use /unikit-gd-verify.

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Use when adversarially reviewing a document that makes formal or technical claims — math derivations, physics papers, statistical analyses, benchmark reports, whitepapers. Inventories every equation and quantitative claim, verifies each AS NAMED in the text (never a paraphrase or a neighboring statement), and classifies VERIFIED / REFUTED / UNVERIFIABLE / VACUOUS. Triggers on "check this paper", "verify these claims", "is this derivation right", "review this proof", "audit this benchmark", "does the math hold up". For source-code review see code-review-and-quality; for skill/agent library audits see skill-library-review; for content quality scoring see content-ops.

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。

SOLID design review rules (SRP, OCP, LSP, ISP, DIP) for code reviews: what counts as a real design finding, what is only taste, how to cite it and how severe it is. Use when reviewing a diff, a pull request or recent changes for design problems, when asked for a "SOLID review", "design review", "is this class doing too much", or when a review checklist asks for SOLID. Pair it with the dry skill for duplication and with owasp-asvs for security.

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 plan directory (plan.md + phase-*.md) needs a visual review. By DEFAULT this drives the STRUCTURED visual-plan artifact (visual-plan/plan.json) via the mewkit visual-plan CLI — generate if absent, validate, open the local studio (view/edit), and export plan.html from the approved artifact. Pass --static for the legacy prompt-only single-file plan.html template render. Triggers on "render this plan", "visualize the plan", "open the plan studio", "make a shareable plan page", "plan.html". This is the canonical owner of plan-as-visual-review. NOT for plan critique or scope review (see mk:plan-ceo-review / mk:validate-plan), NOT for generic code/diagram/diff visuals (see mk:preview), NOT for live media generation (see mk:multimodal).

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Triage reviewer comments on a GitHub PR with receiving-code-review discipline — verify each against the codebase, then accept/push-back/clarify, and optionally reply in-thread. Use when responding to received PR feedback. NOT for giving a review (see mk:review-pr); NOT for implementing the fixes (hand off to mk:fix).

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。