Loading...
Loading...
Found 1,934 Skills
Calculate Altman Z-Score to predict corporate bankruptcy probability from financial ratios. Use this skill when the user needs to assess a company's financial distress risk, screen for bankruptcy-prone firms, or evaluate credit worthiness — even if they say 'bankruptcy prediction', 'financial distress score', or 'Z-score analysis'.
Analyze e-commerce performance using GA4 metrics, conversion funnel analysis, and key e-commerce KPIs. Use this skill when the user needs to evaluate online store performance, diagnose conversion drop-offs, set up e-commerce tracking, or create performance dashboards — even if they say 'why are sales down', 'optimize our online store', 'set up GA4 for e-commerce', or 'what metrics should we track'.
Apply contract theory to design incentive-compatible agreements under moral hazard and adverse selection. Use this skill when the user needs to structure principal-agent contracts, evaluate compensation schemes, or analyze incomplete contract problems where parties cannot specify all contingencies ex ante.
Apply Porter's Five Forces framework to assess industry competitive dynamics and attractiveness. Use this skill when the user needs to analyze an industry's profitability structure, evaluate market entry barriers, assess supplier or buyer bargaining power, or understand competitive intensity — even if they say 'industry analysis' or 'is this market worth entering' without naming Porter's explicitly.
Apply pragmatist philosophy (Peirce, James, Dewey) to frame knowledge as instrumental for action, evaluate ideas by their practical consequences, and conduct inquiry as problem-solving. Use this skill when the user needs to bridge theory and practice, evaluate competing theories by their usefulness, employ abductive reasoning to generate hypotheses, or when they ask 'which theory is more useful here', 'how do I move from abstract ideas to actionable knowledge', or 'what practical difference does this distinction make'.
Build CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
Discussion entry when ideas are still vague — first conduct triage through 1-2 rounds of dialogue to determine which downstream process this discussion should eventually go to: if the idea is clear enough, proceed directly to feature-design; if the direction of a small requirement is set, continue the discussion within the feature and document it in `{slug}-brainstorm.md`; if a large requirement cannot fit into a single feature, hand it over to roadmap for decomposition. The role of AI is a thinking partner, not a recorder — dig out the real problem the user wants to solve, proactively evaluate when the user brings a solution, and propose alternative directions when necessary. Trigger scenarios: when the user says "I have an idea that's not clear yet", "Let's brainstorm first", "I want to do something but it's still vague", "Let's talk about this area", "The function direction is still undecided", or when the user comes with a specific solution but wants to hear other ideas first. Bugs (go to issue) and refactoring (go to refactor) are not handled here.
Genesys Cloud CX platform help — enterprise CCaaS with AI-powered experience orchestration, omnichannel ACD routing (voice + digital), Architect IVR/flow builder, workforce management (WFM forecasting/scheduling/adherence), quality management (evaluations/scoring), predictive routing, agent assist, virtual agents, outbound dialer, Interaction Analytics, AppFoundry marketplace (450+ apps), REST Platform API with OAuth 2.0 and 15 regional endpoints, deep Salesforce integration (CX Cloud joint product + Service Cloud Voice BYOT), 4 tiers CX1 $75/CX2 $115/CX3 $155/CX4 $240 per user/mo + telephony minutes. Use when setting up Genesys Cloud routing or Architect flows, WFM forecasting not matching actual volume, quality management evaluations not triggering coaching, dropped calls or audio quality issues, comparing Genesys pricing tiers, integrating Genesys with Salesforce or ServiceNow, Genesys reporting hard to navigate, MFA management confusing, Genesys API integration, or evaluating enterprise CCaaS platforms. Do NOT use for building a general coaching program (use /sales-coaching) or comparing CCaaS platforms (use /sales-ccaas-selection).
MSW search integration — (1) vector search for API docs and implementation guides (msw-guide-mcp or curl against mlua_Document_Retriever / mlua_API_Retriever), (2) REST API search for resources (sprite / animation / sound / resource pack / avatar). Use for 'find details, examples, or related APIs not in .d.mlua', 'need a SpriteRUID', 'monster sprite', 'background image', 'find a sound', 'avatar rendering', etc. Keywords: document search, API details, examples, guide, retriever, resource, sprite, animation, sound, RUID, resource pack, avatar.
Quality review of test files and manual evidence documents. Goes beyond existence checks — evaluates assertion coverage, edge case handling, naming conventions, and evidence completeness. Produces ADEQUATE/INCOMPLETE/MISSING verdict per story. Run before QA sign-off or on demand.
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval / pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.