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Found 1,942 Skills
Analyze dividend investment opportunities, evaluate dividend safety, growth potential and yield rate. Use this when users inquire about dividends, dividend investment or dividend yield. Supports quick screening, in-depth analysis and portfolio optimization.
Validate built features through conversational testing, running UAT, user acceptance testing, checking if features work, or verifying implementation. Triggers include "verify work", "test features", "UAT", "user testing", "check if it works", and "validate features".
Retrieve market capitalization data for multiple companies at once using Octagon MCP. Use when comparing valuations across peers, screening by market cap, or analyzing a portfolio's composition by company size.
Expert in Jungian analytical psychology, depth psychology, shadow work, archetypal analysis, dream interpretation, active imagination, addiction/recovery through Jungian lens, and the individuation process - grounded in primary sources and clinical frameworks. Activate on 'Jung', 'Jungian', 'shadow work', 'archetypes', 'dream interpretation', 'active imagination', 'individuation', 'anima', 'animus', 'collective unconscious', 'addiction', 'recovery', 'spiritus contra spiritum'. NOT for therapy or diagnosis (only licensed analysts diagnose), active psychosis, severe dissociation, or replacing the relational container of actual Jungian analysis.
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.
Use only when the user explicitly requests brainstorming, evaluating architecture choices, or comparing options where no single concern dominates
Property valuation, market analysis, investment ROI calculations, comparable analysis, and rental yield assessment. Use when evaluating real estate investments, analyzing property markets, or calculating returns.
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
Evaluate and rank agent results by metric or LLM judge for an AgentHub session.
Conduct structured policy analysis including problem definition, alternative evaluation, and evidence-based recommendation. Use this skill when the user needs to evaluate policy options, compare interventions, assess regulatory impact, or make public sector recommendations — even if they say 'which policy should we adopt', 'what's the best approach to this public problem', or 'evaluate these policy alternatives'.
Create a boolean first flag, add evaluation, toggle on/off for end-to-end proof. Parent onboarding Step 6; uses MCP, API, or ldcli; optional flag-create skill.
Measure and improve the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For deployment, use agent-platform-deploy.