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Found 1,932 Skills
Use when "Dask", "parallel computing", "distributed computing", "larger than memory", or asking about "parallel pandas", "parallel numpy", "out-of-core", "multi-file processing", "cluster computing", "lazy evaluation dataframe"
Apply consulting methodologies from McKinsey, BCG, Bain, and Accenture for structured problem-solving and strategic analysis. Use when analyzing business problems, developing strategy, structuring presentations, evaluating M&A, sizing markets, improving profitability, managing projects, or driving organizational change.
Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.
Analyze TCM constitution data, identify constitution types, evaluate constitution characteristics, and provide personalized health preservation suggestions. Support correlation analysis with health data such as nutrition, exercise, and sleep.
Design intuitive, meaningful interactions grounded in user goals and cognitive principles. Use when designing component behaviors, user flows, feedback systems, error handling, loading states, transitions, accessibility, keyboard navigation, touch/gesture interactions, or when evaluating interaction quality. Also use for modal vs modeless decisions, direct manipulation patterns, input device considerations, emotional/dramatic aspects of UX, or when asked about making interfaces feel responsive, humane, and goal-directed.
A pattern for generating higher-quality output by iterating against explicit scoring criteria. Use for headlines, CTAs, landing page copy, social content, ad copy — anything where quality matters. Generate → Evaluate → Diagnose → Improve → Repeat.
MixSeekの設定ファイル（team.toml、orchestrator.toml、evaluator.toml、judgment.toml）を検証します。「設定を検証」「TOMLをチェック」「設定ファイルの確認」「バリデーション」「ワークスペースの検証」といった依頼で使用してください。TOML構文とMixSeekスキーマへの準拠を確認します。
Post-pipeline retrospective — parse logs, score process quality, find waste patterns, suggest skill/script patches. Use after pipeline completes or when user says "retro", "evaluate pipeline", "what went wrong", "pipeline review", "check pipeline logs".
Audit and assess a codebase for programmatic SEO readiness at 1000+ page scale. Use when starting a pSEO project, evaluating an existing codebase for pSEO gaps, or when the user asks to audit, assess, or review their site for programmatic SEO scalability.
ioredis v5 reference for Node.js Redis client — connection setup, RedisOptions, pipelines, transactions, Pub/Sub, Lua scripting, Cluster, and Sentinel. Use when: (1) creating or configuring Redis connections (standalone, cluster, sentinel), (2) writing Redis commands with ioredis (get/set, pipelines, multi/exec), (3) setting up Pub/Sub or Streams, (4) configuring retryStrategy, TLS, or auto-pipelining, (5) working with Redis Cluster options (scaleReads, NAT mapping), or (6) debugging ioredis connection issues. Important: use named import `import { Redis } from 'ioredis'` for correct TypeScript types with NodeNext.
Score each creator on a completed campaign across consistency, content quality, engagement rate, and brand alignment, then produce a ranked retention list for future campaigns. This skill should be used when grading creators after a campaign ends, evaluating influencer performance post-campaign, ranking creators by campaign performance, building a retention list of top creators, deciding which creators to rebook for the next campaign, scoring influencer deliverables after a launch, comparing creator performance across a campaign roster, auditing which creators delivered the most value, or tiering creators into re-engage versus one-and-done lists. For calculating engagement rates and benchmarking them by tier, see engagement-rate-calculator-benchmarker. For scoring niche fit before a campaign, see niche-fit-scorer. For building the full campaign report with ROI narrative, see campaign-roi-calculator-narrative-builder.
This skill should be used when auditing a codebase for AI agent readiness, or when guiding improvements to make a codebase work well with agentic coding tools. It applies when users ask to evaluate test coverage, file structure, type system usage, dev environment speed, or automated enforcement -- the five pillars that determine how effectively coding agents can operate in a project. Triggers on "audit my codebase", "make this agent-ready", "improve for AI agents", "agent-friendly", or questions about why agents struggle with a codebase.