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Found 5,954 Skills
Apply Bloom's revised taxonomy to classify learning objectives and design assessments across six cognitive levels. Use this skill when the user needs to write learning objectives at specific cognitive levels, align assessment with instructional goals, or evaluate curriculum for cognitive complexity distribution — even if they say 'how to write learning objectives', 'what level of thinking does this require', or 'higher-order thinking skills'.
Review business contracts for risk identification including liability clauses, IP ownership, termination terms, and payment conditions. Use this skill when the user needs a practical contract risk assessment for vendor agreements, partnership contracts, or service agreements — even if they say 'review this contract', 'what should I watch out for', 'is this agreement fair', or 'negotiate better terms'.
Build and manage content calendars for multi-platform content marketing including editorial planning, content type allocation, and team workflow. Use this skill when the user needs to plan content across channels, coordinate a content team, maintain publishing consistency, or align content with business goals — even if they say 'we post randomly', 'plan our content for next month', 'content strategy', or 'what should we post this week'.
Conduct scenario planning to prepare for multiple plausible futures using driving forces, uncertainty axes, and the 2x2 scenario matrix. Use this skill when the user faces high uncertainty, needs to stress-test a strategy against different futures, or prepare contingency plans — even if they say 'what if things go wrong', 'what could the future look like', 'how do we prepare for uncertainty', or 'stress-test our strategy'.
Coverage-guided fuzzing workflow for C/C++, Rust, and Go targets. Runs audit-context-building to find suspicious code, writes a targeted harness, builds with sanitizers, runs the fuzzer, and reports crashes.
Help non-technical stakeholders write clear, scoped requirements documents. Translates business needs into structured specs including user goals, workflows, and success criteria — but first guides the requester through prioritization, scope limits, and MVP phasing to prevent wishlist bloat. Output in Taiwan Traditional Chinese. Use this skill when your boss, PM, or stakeholder wants to define what a feature should do, write a PRD, plan a product, or describe requirements. It is also suitable when someone presents a large feature wishlist and needs help structuring and narrowing down its scope.
Set up `release-please` for automated releases in a repository. Use this skill when the user mentions release-please, `googleapis/release-please-action`, release PRs, conventional commits, `release-please-config.json`, `.release-please-manifest.json`, GitHub Actions release automation, or wants to bootstrap or debug release-please in a new or existing repo.
Build a production-ready multilabel classifier on tabular data using XGBoost wrapped in MultiOutputClassifier. Use when each row can have multiple labels simultaneously (tags, attributes, gene functions, content moderation categories, multi-disease detection). Covers hamming loss, per-label metrics, label co-occurrence, MultiOutputClassifier vs ClassifierChain, and per-label SHAP. Default to this for any tabular multilabel problem.
Get AI-powered match predictions for Premier League and Champions League including scores, next goal, and corners.
Use when context is growing large (50k+ tokens), performance is degrading, instructions are being ignored mid-conversation, or planning multi-agent workflows. Triggers on "lost context", forgotten instructions, or sessions exceeding 30 minutes.
Document the pitfalls encountered or good practices discovered during this work into searchable learning documents, so that both AI and humans can look them up when similar tasks arise in the future. Two tracks: The pitfall track records experiences where "things should have worked but didn't" — bugs, configuration traps, environment issues, integration failures; The knowledge track records findings that "should be the default approach going forward" — best practices, workflow improvements, reusable patterns. Trigger scenarios: Proactively prompt for input when wrapping up feature-acceptance or issue-fix, or when the user says phrases like "document knowledge", "learning", "document learnings", "record this experience". Spec documents record what was done and how it was done, while learning documents record what pitfalls were encountered / what was learned — the two complement each other and are not interchangeable.
imagine is a multi-provider command-line tool for generating and editing images via Google Gemini, Google Vertex AI, and OpenAI (gpt-image-2).