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Found 1,893 Skills
Use when a workflow, recipe, or capability needs to be packaged as an agent skill, or when an existing skill needs scaffolding, validation, linting, or evals. Covers requests to build, create, generate, scaffold, check, or evaluate a skill: a folder holding SKILL.md, scripts, tests, a task graph, CI, and eval cases. Applies even when the request says playbook, runbook, or reusable workflow instead of skill.
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Verified corrections for IAM behaviors that AI agents frequently get wrong — policy evaluation edge cases, trust policy gotchas, STS session limits, Organizations quirks, and SAML/MFA specifics. Use alongside documentation when working with IAM roles, policies, STS, or Organizations. Do NOT use for non-IAM authorization like Cognito user-pool policies or app-level RBAC.
Analyze multi-round evaluation score data, count various indicators, and calculate rating levels. Suitable for analyzing score trends and calculating S/A/B ratings
Guides advanced long-term actuarial mathematics (SOA ALTAM)—survival models, life insurance and annuity APVs, premiums and reserves (equivalence principle, Thiele), multiple decrement and Markov states, yield-curve discounting, mortality improvement, longevity risk, profit testing, and mortality graduation. Tool-agnostic, concept-first. Use when the user mentions advanced long-term actuarial mathematics, ALTAM, survival model, life insurance reserve, annuity valuation, equivalence principle, Thiele equation, multiple decrement, force of mortality, longevity risk, mortality improvement, actuarial present value, or net premium reserve—not ASTAM/P&C (advanced-short-term-actuarial-mathematics), workpapers only (actuarial-analyst), appointed actuary (appointed-chief-actuary), assumption governance (assumption-setting), ALM detail (asset-liability-management), or exam-only deliverables.
A systematic stock analysis framework based on Warren Buffett's value investing philosophy. It provides a complete investment analysis process including economic moat analysis, financial evaluation, management assessment, valuation methods and risk control. Suitable for evaluating specific stocks, screening high-quality targets, analyzing competitive advantages, and building investment portfolios. Activate when users mention keywords such as "Buffett", "value investing", "economic moat", "ROE", "pricing power", "long-term holding", "margin of safety", "circle of competence", "white horse stock", "blue chip stock", or when stock investment analysis is required.
Reviews pitch decks and investor presentations. Reads slide content, evaluates narrative flow, problem/solution clarity, market sizing, competitive positioning, financial projections, team credibility, and ask clarity. Generates a scored pitch-review.md with slide-by-slide feedback, overall score, top improvements, investor objection predictions, and comparisons to successful decks. Use when reviewing fundraising materials, investor decks, or pitch presentations.
AI job search command center -- evaluate offers, generate CVs, scan portals, track applications
Evaluate a skill against the Legal Skill Design Framework — thirteen design parameters (including trust-surface, freshness, schema validation, and conflict detection), three legal failure modes, and a three-band verdict (Ready / Some Concern / Material Concerns). Use when deciding whether to trust a community skill before installing it, before deploying a first-party skill to your team, or whenever the user asks "should I trust this?" or "is this skill well-designed?". Runs automatically as part of /legal-builder-hub:skill-installer.