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a-evolve
a-evolve contiene 18 skills recopiladas de A-EVO-Lab, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
Keyboard shortcuts for common desktop applications — LibreOffice, GIMP, Chrome, Thunderbird, VS Code.
When to prefer GUI mouse clicks over keyboard shortcuts — especially for formatting, multi-step visual tasks, and cross-application workflows.
General GUI navigation patterns for desktop environments — finding elements, interacting with menus, and handling dialogs.
How to interpret accessibility tree elements and correlate them with screenshot regions for accurate GUI interaction.
Verification patterns to confirm task completion before submitting. Read this before calling submit().
Workarounds for bot detection, CAPTCHA, 403 errors, and Cloudflare challenges when browsing the web.
How to build C/C++/Cython/Fortran extensions and frameworks like Caffe, OpenCV, protobuf-dependent projects from source.
Strategies for debugging build failures, runtime errors, writing complete output files (ICS/JSON/config), and constraint satisfaction tasks.
Strategies for quickly discovering what tools, languages, and files are available in a Terminal-Bench container.
Best practices for multi-step Python tasks including data analysis, HuggingFace datasets, token counting, and any task requiring state across multiple python() calls.
Strategies for scientific computing, numerical methods, bioinformatics/DNA tasks, logic circuit design, algorithmic challenges, and ML training tasks.
Verification patterns to confirm task completion before submitting. Read this before calling submit().
Strategies for avoiding dead ends and premature conclusions. Read this when stuck or when an approach seems to not work.
Optimize an AI agent's harness for MCP-Atlas benchmark. Use when analyzing execution traces, diagnosing failures, and proposing improved prompts, skills, or harness code.
Reading, writing, and converting common data formats (CSV, Excel, JSON, YAML) with correct handling of encoding, types, and edge cases.
Strategies for quickly discovering what tools, languages, data files, and skills are available in a SkillBench container.
Installing and using common Python packages in SkillBench containers. Covers scientific computing, data analysis, and file format libraries.
How to discover, load, and effectively use skills to solve SkillBench tasks.