EvoSkill is a CLI tool and Python library for automatically discovering and improving skills for coding agents. It implements a self-improvement loop: a base agent is evaluated on a task, failures are analyzed by a proposer agent which suggests skill/prompt mutations, a generator creates new skills/prompts, and the evolved agent is re-evaluated. The best programs are tracked via git branches, enabling frontier management and rollback. The system supports multiple agent SDKs (Claude Code, Codex CLI, OpenCode, OpenHands, Goose, Harbor) through a unified harness abstraction, allowing the same evolution loop to work across different agent platforms. The architecture is modular: CLI commands (init, run, eval, skills, diff, logs, reset, remote) provide the user interface; an API layer (EvoSkill, EvalRunner) offers programmatic access; the loop engine (SelfImprovingLoop) orchestrates the evolution; the registry (ProgramManager) handles git-based program versioning; the harness layer abstracts SDK-specific execution with retry, timeout, and structured output parsing; agent profiles define role-specific configurations (base agent, proposer, generator, evaluator); and evaluation modules support multiple scoring methods (exact match, tolerance, LLM-as-judge, script-based, Harbor). External dependencies include various agent SDKs (claude-agent-sdk, opencode-ai, openhands-ai, etc.), LLM providers (OpenRouter, Fireworks), and infrastructure tools (Docker, Daytona for remote execution). The repository also includes example benchmarks (OfficeQA, SEAL-QA, Dabstep, LiveCodeBench) with datasets and evaluation scripts, demonstrating the system's application to real-world tasks. The codebase exhibits strong engineering practices with clean abstractions, reusable components, and comprehensive testing, though there are some areas of duplication (e.g., skill-creator scripts) and global mutable state in the SDK configuration.
This repository is an 'awesome list' — a static Markdown index of externally hosted Claude Code skills, organized into 14 categories (documents, development, data, science, writing, learning, media, health, collaboration, security, utility, articles, and collections). It contains no source code, no executable skills, no CI/CD, and no tests. The production function is discovery: a human or AI agent reads the README to find third-party skill repositories, then clones or installs those skills separately. Architecture is a single README with external links plus a few embedded sponsor images. There are no data flows or APIs. The list is actively maintained with a recent commit and a large volume of open issues, indicating ongoing community input. The core value is the curation and categorization of 150+ external skills, not software engineering craftsmanship. Sponsored placements appear at the top, so it is not purely community-driven. Overall, a useful and well-organized directory, but with zero in-repo technical content.
This repository serves as a curated directory of GitHub projects focused on 'human distillation skills'—the practice of extracting and encoding human expression styles, decision-making frameworks, and interaction patterns from individuals (such as personal contacts, idols, or public figures) into AI-readable formats. It does not contain any executable code, data processing pipelines, or analytical models itself; instead, it functions as a community-maintained index linking to external repositories that implement specific skill sets. The project is organized into thematic categories (self, interpersonal relationships, idols, and niche communities), each containing a markdown-formatted table of linked repositories with concise descriptions. Contributions are accepted via pull requests following a strict format to ensure consistency and usability. While the repository lacks traditional software engineering artifacts, it provides value as a discovery tool for researchers, developers, and enthusiasts seeking pre-existing work in the niche domain of personality-driven AI skill development. From a data perspective, the repository's sole asset is its README.md file, which acts as a structured dataset of skill project metadata. However, it does not include any data transformation, validation, or analytical components, limiting its reusability as a data engineering asset. The project exhibits minimal technical drift due to its simplicity and absence of external dependencies, and its maintenance status appears maintained based on recent commit activity (last commit 2026-04-08) and zero open issues, indicating ongoing community stewardship without significant backlog.