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Found 2,052 Skills
General-purpose NocoBase reference utilities covering cross-cutting topics such as evaluator engines, expression syntax, and more. Use when you need authoritative reference information that applies across multiple NocoBase features.
Produces a concrete eval suite plan grounded in Microsoft's Eval Scenario Library and MS Learn agent evaluation guidance — scenario types, evaluation methods, quality signals, thresholds, and priority order — before any test cases are generated or evals are run.
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
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.
Investment proposal generation via Longbridge Securities — produces a structured investment memo covering: executive summary, company overview, investment thesis (3–5 core points), financial analysis, valuation, catalysts and timeline, risk factors, and position recommendation. Triggers: "投资提案", "投资建议书", "投资报告", "投资摘要", "核心逻辑", "投资理由", "建仓建议", "投資提案", "投資建議書", "投資報告", "投資摘要", "核心邏輯", "建倉建議", "investment proposal", "investment memo", "investment summary", "investment rationale", "position recommendation", "investment case", "buy memo".
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
This skill is designed for any requests related to running/executing/evaluating code (such as run this code, execute script, plot with Python, run code/plot/execute script). Create a code_latest sandbox via AgentBay SDK, call run_code to execute and return results. Supports Python, JavaScript, R, Java.
Use this skill whenever the user asks for a security analysis, vulnerability assessment, security audit, or any form of Security Assessment Report (SAR) over a codebase, infrastructure, API, database, or system. Triggers include: "audit my code", "find security issues", "run a security check", "generate a SAR", "check for vulnerabilities", "is this code secure", or any request that involves evaluating the security posture of a project. Also triggers when the user uploads or references source code, config files, environment variables, or architecture diagrams and asks for a security opinion. Do NOT use for generic coding tasks, code reviews focused on quality rather than security, or performance optimization unless a security angle is explicitly present.
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.
Help users create and run AI evaluations. Use when someone is building evals for LLM products, measuring model quality, creating test cases, designing rubrics, or trying to systematically measure AI output quality.
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
Build and run evaluators for AI/LLM applications using Phoenix.