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Found 10,841 Skills
Use when the user is investigating QML / Qt Quick performance — both vague complaints ("the UI feels laggy", "this is slow", "frames are dropping", "the app stutters") and explicit asks to profile, find hotspots, or optimize bindings, signals, or rendering. Runs qmlprofiler on a 2D QML application, parses the .qtd trace, and analyzes hotspots against the source with frame-time, memory, and pixmap-cache summaries. Does NOT cover Qt Quick 3D.
This skill should be used when the user asks to "migrate to Buildkite", "convert pipelines from Jenkins", "convert GitHub Actions workflows", "convert CircleCI config", "convert Bitbucket Pipelines", "convert GitLab CI", "migrate CI/CD to Buildkite", "switch from Jenkins to Buildkite", "move from GitHub Actions", "plan a CI migration", "convert my CI config", "bk pipeline convert", or "what's the Buildkite equivalent of". Also use when the user mentions migration planning, CI conversion, pipeline conversion, converting workflows, or asks about translating CI/CD configuration from another provider to Buildkite.
Use this skill when surfacing cancellation, pause, and winback state on Android with RevenueCat. Covers reading unsubscribeDetectedAt, billingIssuesDetectedAt, pause state via periodType, managementURL for deep link, and pause resume date lookup via the REST API.
Local vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel box), detect (element inventory), trace (image to SVG geometry), crop (cut a pixel box to a file), and scripts/html_shot.py (HTML file to image). Use for any task involving an image — questions, text, splitting and transcribing long screenshots or chat histories, locating elements, comparing, rebuilding as HTML/SVG, digitizing a sketch or diagram, reading values off a chart, operating a GUI from screenshots — and to re-check an image yourself when a description you were given lacks a detail.
Audit Snapchat Ads measurement, Snap Pixel and Conversions API, mobile and app campaigns, creative, AR and catalog formats, audiences, budgets, brand safety, and reporting. Use for Snapchat Ads, Snap Ads, Snap Pixel, Snapchat Conversions API, AR Lens ads, app-install campaigns, or Snapchat dynamic product ads.
Convert per-page styled HTML prototypes (stardust under stardust/prototypes/**, or claude-design / Mobirise / Relume / Lovable / v0 / Figma-derived pages, or JSX prototypes pre-rendered to HTML, often under samples/) into Edge Delivery Services (EDS / AEM) blocks and content pages, then deploy via DA. Each prototype section becomes one EDS block; the prototype's per-section CSS becomes that block's CSS scoped under the block class. Use when the user wants to lift styled per-page HTML prototypes into a working EDS site under blocks/ and content/.
Use biological evolution logic (genetic variation + natural selection) to address uncertainty, and iterate products or teams through the cycle of MVP launch → market feedback screening → variation and innovation → accumulating advantages. It applies to scenarios with unclear goals, rapidly changing environments, and inability to plan in advance. It is not suitable for deterministic projects with clear goals and clear paths (planning is more efficient). Planning and evolution are complementary like Yin and Yang: Planning = Yang (efficiency), Evolution = Yin (addressing uncertainty). Key trigger signals: "Unsure what users want", "The market changes too fast", "Plans can't keep up with changes", "Let's get started first"
Turn a chosen segment + Core Jobs into a build-ready PRD (full functionality + edge cases) using Ivan Zamesin's AJTBD / Next Move Theory methodology. It consumes upstream work — segments from /nmt-market-research, value from /nmt-craft-value-proposition — and never re-derives them; with no research done it routes you upstream first, or takes a manually described segment + value for a fast run. Before writing requirements it runs a "challenge the build" gate that looks for a more effective way to hit the same business goal; if a better way wins, the PRD is written for that. Output — a single PRD — functionality mapped Core Job → Big Job → value mechanic → success criteria → Aha Moment on the Critical Chain of Jobs, plus edge cases covering ~90% of use cases. Use when the user says "write the PRD / product requirements" or wants to turn a segment+value or a feature idea into a build spec. Two modes — Quick (default, no internet) and Deep (subagents + web parity check). Plain language; defaults to English.
Convene a structured LLM Council — five thinking-lens advisors (Red Team, First Principles, Expansionist, Outsider, Executor) plus anonymised peer review, forced debate on consensus, dual-chairman synthesis with dissent preservation, and optional Codex-powered Decision Science pass — to pressure-test high-stakes decisions. Adaptive modes (Quick/Standard/Deep) keep cost bounded; a persistent journal enables learning across runs. Mandatory triggers: /claude-council, "convene the council", "run this by the council", "I need the council", "council this", "pressure-test this", "stress-test this", "war room this", "debate this". Strong triggers: "I'm torn between X and Y", "this is a big decision", "help me think this through from multiple angles", "I need outside perspectives", "should I X or Y" (with real stakes — if binary with obvious answer, triage rejects per Step 1 rule 4). Do NOT invoke for factual questions, coding help, debugging, quick yes/no decisions, emotional support, or questions with one right answer — answer those directly. Optional suffixes: "with codex" enables Decision Science pass; "deep" forces Deep mode; "quick" forces Quick mode. Secondary invocation: /claude-council outcome <sha1> <note> records decision outcome. /claude-council meta runs journal meta-analysis.
Escribe o cambia el playbook de objeciones y el tono de cierre, sin tocar ninguna otra parte del kit.
Audit an ENTIRE codebase with multiple agents in parallel — map the repo, partition it into review slices, fan out one (or more, multi-lens) reviewer subagent per slice, reduce with a cross-cutting/architecture critic + a completeness check, then triage, fix, and report with an honest coverage ledger. Use when asked to 'audit the whole codebase', 'full security/quality review of the repo', 'review the entire project', 'do a deep/comprehensive code audit', 'scan everything for bugs or vulnerabilities', when onboarding/inheriting an unfamiliar repo, or for a periodic deep sweep. Whole-repo scoped and scales agent count to repo size — NOT a diff review (for changed lines use dual-agent-review).
Guides the agent through adding, configuring, and using the Capawesome desktop platforms for Capacitor — @capawesome/capacitor-electron and @capawesome/capacitor-tauri. Covers choosing between Electron and Tauri, installation and scaffolding, sync/run workflows, live reload, deep links, plugin compatibility (Electron plugin implementations and web fallback; Tauri plugin tiers), packaging, and app updates. Do not use for the Android or iOS platforms, installing individual Capacitor plugins, migrating Capacitor apps or plugins to newer versions, or non-Capacitor desktop frameworks.