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Found 3,726 Skills
Write tests before implementation code. Use when starting new features or fixing bugs. Covers Red-Green-Refactor cycle and TDD best practices.
Design push notification and messaging strategies including channel selection, timing optimization, personalization, and fatigue management. Use this skill when the user needs to improve notification engagement, reduce opt-out rates, plan multi-channel messaging, or A/B test notification content — even if they say 'our push open rates are low', 'users are unsubscribing', 'when should we send notifications', or 'which channel to use for alerts'.
Rust no_std skill for embedded and bare-metal development. Use when writing
Points to Michał Zalewski’s (lcamtuf) canonical American Fuzzy Lop (AFL) documentation at lcamtuf.coredump.cx/afl—coverage-guided fuzzing concepts, afl-fuzz usage, and historical technical notes for C/C++ targets. Use when the user cites AFL classic, lcamtuf’s AFL page, or needs the original upstream reference—not as a substitute for current AFL++ docs or authorized fuzzing policy.
Analyze test coverage gaps and generate tests to improve coverage. Use when improving test coverage, finding untested code, or writing missing tests.
Loads orchestrate mode — a disciplined delivery loop that enforces BDD specs in specs/, real integration tests (no mocks), PR CI and CodeRabbit babysitting, and mandatory end-user QA via computer-use or CLI dogfooding before anything is considered done. Use when starting any non-trivial implementation task, feature build, or delivery where you want the work driven from spec to proven-shipped state rather than stopping at "tests pass".
Interview the user relentlessly about whatever they want to work on — a plan, task, design, idea, feature, architecture decision, or anything else — until reaching shared understanding. Walk the decision tree one branch at a time, resolving dependencies between decisions. Use when the user says "grill me", wants to stress-test a plan or idea, wants to be interviewed about a design, or wants to flesh out an under-specified task.
UI and design review: evaluate visual quality, responsive behavior, accessibility, color/contrast, typography, layout consistency, and i18n readiness using browser-based validation against industrial standards.
Investigate OpenClaw pnpm test memory growth, Vitest OOMs, RSS spikes, and heap snapshot deltas.
Lightning Web Components with PICKLES methodology and 165-point scoring. Use this skill when the user creates or edits LWC components, builds wire service patterns, or writes Jest tests for LWC. TRIGGER when: user creates/edits LWC components, touches lwc/**/*.js, .html, .css, .js-meta.xml files, or asks about wire service, SLDS, or Jest LWC tests. DO NOT TRIGGER when: Apex classes (use generating-apex), Aura components, or Visualforce.
Readability and rendering audit for figures and tables in academic manuscripts. Computes effective font/marker sizes at display scale from generation scripts, checks label collisions, color/hatch accessibility, axis-range efficiency, table formatting, and cross-figure consistency. Triggers on: "check figure quality", "audit plots", "readability check", "figure rendering", "are my figures readable", "table formatting check". Companion to figure-rhetoric (visual argument) and manuscript-typography (typesetting).
Guides quantitative research for markets and finance—research question framing, data sourcing and quality checks, descriptive and inferential statistics, time series and panel methods (high level), factor and signal research, backtest design and pitfalls (lookahead, survivorship), risk metrics (volatility, drawdown, Sharpe limitations), regime and stress analysis, and reproducible notebooks or reports with explicit limitations and uncertainty communication. Use when the user mentions "quantitative research", "quant researcher", "factor research", "signal backtest", "time series analysis", "panel regression", "alpha research", "Sharpe ratio analysis", "survivorship bias", "lookahead bias", "econometric analysis", or "risk factor model". Not for production ML pipelines (data-scientist, ml-research-engineer), equity narrative reports (equity-research skills), SOX accounting (financial-statements), legal investment advice, or trading execution systems (senior-software-engineer).