Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
MCP Agent Mail - Mail-like coordination layer for multi-agent workflows. Identities, inbox/outbox, file reservations, contact policies, threaded messaging, pre-commit guard, Human Overseer, static exports, disaster recovery. Git+SQLite backed. Python/FastMCP.
Databricks 开发指南，包括 Python SDK、Databricks Connect、CLI 和 REST API。使用 databricks-sdk、databricks-connect 或 Databricks API 时使用。
Admin operations for the Heurist Skill Marketplace. Use this skill when the user asks to add, ingest, approve, reject, remove, update, or list skills in the Heurist marketplace. Also triggers for checking upstream changes, updating external API dependencies, updating metrics (stars/downloads), re-ingesting skills, or any marketplace database management task. Working directory is heurist-agent-framework. Always use uv run python to run scripts.
使用 python-pptx 以及 YAML 驱动的内容和样式生成和管理 PowerPoint 幻灯片 - 由 microsoft/hve-core 提供
Panel data analysis with Python using linearmodels and pandas.
Reverse engineer web APIs by capturing browser traffic (HAR files) and generating production-ready Python API clients. Use when the user wants to create an API client for a website, automate web interactions, or understand undocumented APIs. Activate on tasks mentioning "reverse engineer", "API client", "HAR file", "capture traffic", or "automate website".
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation. --- # Auto Arena Skill End-to-end automated model comparison using the OpenJudge `AutoArenaPipeline`: 1. **Generate queries** — LLM creates diverse test queries from task description 2. **Collect responses** — query all target endpoints concurrently 3. **Generate rubrics** — LLM produces evaluation criteria from task + sample queries 4. **Pairwise evaluation** — judge model compares every model pair (with position-bias swap) 5. **Analyze & rank** — compute win rates, win matrix, and rankings 6. **Report & charts** — Markdown report + win-rate bar chart + optional matrix heatmap ## Prerequisites ```bash # Install OpenJudge pip install py-openjudge # Extra dependency for auto_arena (chart generation) pip install matplotlib ``` ## Gather from user before running | Info | Required? | Notes | |------|-----------|-------| | Task description | Yes | What the models/agents should do (set in config YAML) | | Target endpoints | Yes | At least 2 OpenAI-compatible endpoints to compare | | Judge endpoint | Yes | Strong model for pairwise evaluation (e.g. `gpt-4`, `qwen-max`) | | API keys | Yes | Env vars: `OPENAI_API_KEY`, `DASHSCOPE_API_KEY`, etc. | | Number of queries | No | Default: `20` | | Seed queries | No | Example queries to guide generation style | | System prompts | No | Per-endpoint system prompts | | Output directory | No | Default: `./evaluation_results` | | Report language | No | `"zh"` (default) or `"en"` | ## Quick start ### CLI ```bash # Run evaluation python -m cookbooks.auto_arena --config config.yaml --save # Use pre-generated queries python -m cookbooks.auto_arena --config config.yaml \ --queries_file queries.json --save # Start fresh, ignore checkpoint python -m cookbooks.auto_arena --config config.yaml --fresh --save # Re-run only pairwise evaluation with new judge model # (keeps queries, responses, and rubrics) python -m cookbooks.auto_arena --config config.yaml --rerun-judge --save ``` ### Python API ```python import asyncio from cookbooks.auto_arena.auto_arena_pipeline import AutoArenaPipeline async def main(): pipeline = AutoArenaPipeline.from_config("config.yaml") result = await pipeline.evaluate() print(f"Best model: {result.best_pipeline}") for rank, (model, win_rate) in enumerate(result.rankings, 1): print(f"{rank}. {model}: {win_rate:.1%}") asyncio.run(main()) ``` ### Minimal Python API (no config file) ```python import asyncio from cookbooks.auto_arena.auto_arena_pipeline import AutoArenaPipeline from cookbooks.auto_arena.schema import OpenAIEndpoint async def main(): pipeline = AutoArenaPipeline( task_description="Customer service chatbot for e-commerce", target_endpoints={ "gpt4": OpenAIEndpoint( base_url="https://api.openai.com/v1", api_key="sk-...", model="gpt-4", ), "qwen": OpenAIEndpoint( base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", api_key="sk-...", model="qwen-max", ), }, judge_endpoint=OpenAIEndpoint( base_url="https://api.openai.com/v1", api_key="sk-...", model="gpt-4", ), num_queries=20, ) result = await pipeline.evaluate() print(f"Best: {result.best_pipeline}") asyncio.run(main()) ``` ## CLI options | Flag | Default | Description | |------|---------|-------------| | `--config` | — | Path to YAML configuration file (required) | | `--output_dir` | config value | Override output directory | | `--queries_file` | — | Path to pre-generated queries JSON (skip generation) | | `--save` | `False` | Save results to file | | `--fresh` | `False` | Start fresh, ignore checkpoint | | `--rerun-judge` | `False` | Re-run pairwise evaluation only (keep queries/responses/rubrics) | ## Minimal config file ```yaml
Backend specialist for APIs, databases, authentication using FastAPI with clean architecture (Repository/Service/Router pattern)
当用户要求“查找类”、“搜索符号”、“查找用法”、“查找实现”、“搜索代码库”、“查找文件”、“类层次结构”、“查找调用者”、“模块依赖项”、“未使用的依赖项”、“项目映射”、“项目约定”、“项目结构”、“什么框架”、“什么体系结构”、“查找 Perl 子项”、“Perl 导出”时，应该使用此技能。 “查找Python类”，“Go结构”，“Go接口”，“查找React组件”，“查找TypeScript接口”，“查找Rust结构”，“查找Ruby类”，“查找C#控制器”，“查找Dart类”，“查找Flutter小部件”，“查找mixin”，“查找Scala特征”，“查找案例类”，“查找对象”，“查找PHP类”，“查找Laravel模型”， “查找 PHP 特征”，或者需要在 Android/Kotlin/Java、iOS/Swift/ObjC、Dart/Flutter、TypeScript/JavaScript、Rust、Ruby、C#、Scala、PHP、Perl、Python、Go、C++ 或 Protocol Buffers 项目中快速搜索代码。 也是由提及“ast-index”CLI 工具触发的。
面向中文科研论文的AI写作助手。默认先讨论再执行，自动创建并维护plan上下文，支持去AI化写作、文献综述、Python图表与Miniconda环境配置，默认以Markdown/纯文本交付并提供Word迁移指引。
将仅 Python 的图形参考技能添加到 BioClaw 安装中。当用户希望在代理容器内提供出版质量的绘图指导而不添加源代码功能时使用。使用仅限 Python 的“SKILL.md”和根级“seaborn_reference.md”创建“container/skills/figure/”。
