EvoMap Evolver is a self-evolving AI agent engine built as a Node.js >=22.12 CLI daemon. It runs a genome-evolution loop: scans session memory/logs, extracts signals, selects 'Genes' (prompt/strategy patches) and 'Capsules' (validated skill bundles) from a local JSON/JSONL asset store, generates GEP-framed prompts for coding agents (Claude Code, Codex, Cursor, Gemini CLI, opencode, Kiro), executes those sessions, and records outcomes in a memory graph to adapt future selections. The system solidifies successful patterns into new reusable assets, auto-distills failure patterns and SKILL.md files into Genes/Capsules, and can open PRs or run validation sandboxes against candidate changes. It also ships an LLM proxy (Anthropic/OpenAI/Gemini/Vertex/Ollama routes with trace encryption and token-savings attribution), an ATP (Agent Transaction Protocol) marketplace for buying/completing agent tasks with auto-buyer and auto-deliver, a validation daemon for decentralized task verification, a local Web UI dashboard, and an experiment runner for A/B comparing evolution strategies. Hub synchronization (SSE event delivery, mailbox sync, OAuth device login) connects nodes to the evomap.ai network, but the system is designed to degrade gracefully into fully offline solo mode.
This repository is a curated collection of 177+ domain-specific scientific 'skills' for AI agents, following the Agent Skills standard. Each skill is a self-contained module providing a CLI tool, documentation, and tests for a specific scientific or technical task—ranging from single-cell RNA-seq analysis (scanpy) and molecular docking (diffdock) to clinical report validation and ISO standards readiness. The architecture is a monorepo of independent skills, each with its own scripts/, references/, assets/, and tests/. Skills are designed to be safe, bounded, and reproducible: they validate inputs before execution, enforce resource limits, avoid network access unless explicitly required, and produce structured JSON reports. The repository also includes a shared test infrastructure (skill_contract) and a meta-skill (autoskill) that can automatically propose new skills from screen activity. While primarily an application library for AI agents, it has strong data/research characteristics due to its heavy use of scientific data formats and validation of research artifacts.
Agentic Awesome Skills is a curated marketplace infrastructure for AI coding agent skills—instructional playbooks (SKILL.md files) with accompanying scripts and assets. The repository provides a multi-platform distribution system supporting Claude Code, Cursor, Gemini CLI, Codex CLI, Kiro, and others. It includes a React+Vite web application for browsing, searching, and installing skills; a CLI and MCP server for local agent integration; and a comprehensive validation, security scanning, and release pipeline. The codebase implements a transaction engine with deterministic content addressing (canonical JSON + SHA-256), durable state management, and schema-validated skill installation, forming a robust foundation for agent-owned tool selection and workflow planning. While the core infrastructure (AAS v1 library, MCP server, catalog search) demonstrates high craftsmanship and reusability, the repository is hampered by massive file duplication: identical skill scripts and governance modules appear under `skills/`, `plugins/agentic-awesome-skills/`, and `plugins/agentic-awesome-skills-claude/`, creating synchronization risk. Some skills contain hardcoded paths or incomplete boilerplate. The repository is actively maintained with CI/CD, automated audits, and security scrutiny, though the sheer volume of 2,474+ skills imposes ongoing maintenance burden. Overall, it is a well-engineered, production-quality skill distribution platform with notable reusable design patterns.
This repository is a curated skill library for data analysis workflows, designed to be consumed by AI agents (Claude Code, Codex). It contains 31 skills organized into 6 categories: data quality validation, documentation/knowledge management, data analysis/investigation, data storytelling/visualization, stakeholder communication, and workflow optimization. Each skill is defined by a SKILL.md file with YAML frontmatter, a step-by-step process, required inputs/outputs, and optional scripts, reference materials, and asset templates. The scripts are standalone Python CLIs that generate synthetic data for demo purposes and can be adapted to real datasets. The repo is not a deployable application or a data pipeline; it is a structured knowledge base and toolset for performing data analysis tasks programmatically. It covers common analytical patterns such as null counting, duplicate detection, cohort retention, funnel conversion, A/B testing, time series anomaly detection, metric calculation, and more. The codebase is well-organized and follows consistent conventions, but shows signs of staleness (future dates in README and LICENSE, no recent activity). Dependencies are not pinned, and no environment definitions are provided, limiting reproducibility without manual setup.
This repository is a comprehensive, structured library of over 50 marketing 'skills' designed for AI coding agents (Claude Code, Codex, Cursor, etc.). Each skill is a self-contained directory with a SKILL.md (frontmatter + workflow), a references/ folder with deep-dive documentation and benchmarks, and evals/evals.json for behavioral testing. The skills cover the full marketing lifecycle: copywriting, SEO, paid ads (Meta, Google, LinkedIn), email, social, CRO, pricing, offers, attribution, market research, and more. Additionally, the repository includes 40+ zero-dependency Node.js CLI scripts that act as API clients for marketing tools like Ahrefs, Semrush, GA4, Meta Ads, Mailchimp, etc., each following a consistent pattern with dry-run support and environment variable authentication. Several integration guides and a central tool registry (REGISTRY.md) complete the ecosystem. While primarily a content library, the CLI scripts and validation/sync tooling demonstrate strong engineering patterns. The skills are designed to be consumed by AI agents to execute complex, multi-step marketing tasks, with cross-skill references and a shared context document (product-marketing.md) enabling orchestration.