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Found 2,052 Skills
Diagnose competitive product analysis state and guide through systematic market evaluation. Use when analyzing a product category, building feature comparisons, understanding competitive landscape, building personas, or deciding build vs. buy. Routes to 6 interconnected frameworks based on current analysis state.
Source and evaluate candidates from LinkedIn using the linkedin_scraper Python library. Use when the user wants to (1) scrape LinkedIn profiles for candidate data, (2) evaluate candidates against a job description, (3) generate boolean search strings for sourcing, (4) produce candidate scorecards, summaries, or comparison tables, or (5) any recruiting/talent-sourcing task involving LinkedIn data.
Build discounted cash flow (DCF) valuation models in Excel. Use when creating DCF models, calculating enterprise value, or valuing companies. Trigger with phrases like 'excel dcf', 'build dcf model', 'calculate enterprise value'.
Score, grade, or evaluate things using AI against a rubric. Use when grading essays, scoring code reviews, rating candidate responses, auditing support quality, evaluating compliance, building a quality rubric, running QA checks against criteria, assessing performance, rating content quality, or any task where you need numeric scores with justifications — not just categories.
Technical spike and research investigation specialist. Use when exploring options for a technical decision, conducting timeboxed investigations, or evaluating technology choices.
Design, evaluate, and document software architecture patterns
Master Alex Hormozi's offer creation framework from "$100M Offers" (2021). Build irresistible offers using the Value Equation, stacking, guarantees, and scarcity. Use when: Creating new product or service offers; Restructuring existing offers for higher conversions; Pricing premium products and services; Building offer stacks with bonuses and guarantees; Choosing target markets for maximum leverage
Assess research idea novelty through systematic literature search. Multi-round search-evaluate loops with harsh critic persona. Binary novel/not-novel decision with justification. Use before committing to a research direction.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).
INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.
Strategic AI thinking frameworks and mental models from Satya Nadella's perspective on platform shifts, AI deployment, and building successful AI products. Use when evaluating AI strategy decisions, assessing platform opportunities, thinking through AI product positioning, considering enterprise AI deployment challenges, evaluating talent and team capabilities, or needing frameworks for justifying AI investments in terms of economic surplus. Triggers on questions about AI platform strategy, change management for AI adoption, building AI scaffolding layers, evaluating AI opportunities, or thinking through AI's societal implications.