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Found 1,943 Skills
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.
Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models
MUST READ before running any ADK evaluation. ADK evaluation methodology — eval metrics, evalset schema, LLM-as-judge, tool trajectory scoring, and common failure causes. Use when evaluating agent quality, running adk eval, or debugging eval results. Do NOT use for API code patterns (use adk-cheatsheet), deployment (use adk-deploy-guide), or project scaffolding (use adk-scaffold).
A-share Value Investment Analysis Tool, providing stock screening, in-depth individual stock analysis, industry comparison and valuation calculation functions. Based on value investment theory, it uses tushare to obtain public financial data, suitable for ordinary investors with low-frequency trading.
Run a single experiment iteration. Edit the target file, evaluate, keep or discard.
A decision-support framework that evaluates systems, architectures, and strategies through the entropy (decay) vs negentropy (growth) lens, while surfacing tacit knowledge gaps. Use this skill whenever the user is making architecture decisions, evaluating system designs, reviewing technical approaches, choosing between options, auditing existing systems, or planning strategies. Also trigger when the user explicitly asks to "apply the negentropy lens", mentions "entropy", "negentropy", "tacit knowledge", "knowledge engine", or "flip the switch". Nudge activation when you detect the user is at a decision point — even if they haven't asked for this lens — by briefly noting the entropic/negentropic dimension before proceeding.
This skill should be used when the user asks to "build an agent with Google ADK", "use the Agent Development Kit", "create a Google ADK agent", "set up ADK tools", or needs guidance on Google's Agent Development Kit best practices, multi-agent systems, or agent evaluation.
Evaluate business decisions through the lens of sustainable, profitable growth. Use when someone is making decisions about spending, hiring, fundraising, or scaling their business.
This skill provides the 12 critical story questions for screenplay evaluation. Covers concept, theme, audience reaction, beginning, ending, rising tensions, characters, protagonist, motivation, antagonist, and believability. Use when: evaluating a screenplay draft, identifying story weaknesses, preparing for rewrites, or validating fundamentals before submission.
Deep persona simulation and skeptical buyer review for cold emails. Builds a full prospect "world" from LinkedIn + company data, defines their professional reality (KPIs, pain points, inbox behavior), then runs a skeptical buyer roast — emotional reaction first, business evaluation second. One prospect at a time, Tier 1 only. Triggers on: "review email", "copy feedback", "email feedback", "would they reply", "persona review", "check this email", "review this draft", "roast this email", "skeptical buyer".
Use this skill when the user asks to "evaluate MCP tools", "test tool selection", "improve tool descriptions", "check MCP schema quality", "eval my MCP server", or wants to measure whether Claude uses their MCP tools correctly. Tests tool selection accuracy, analyzes schema quality, and iteratively optimizes descriptions. Companion to build-mcp-server.
Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.