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The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
This repository page is useful for visitors, but Git-Stars keeps it out of search indexing until more original evidence and comparison context are available.
90
review score
Problem solved
ECC solves the fragmentation of agent configurations across different AI coding harnesses by providing a single, reusable system that works with Claude Code, Codex, Cursor, and others. It also addresses the lack of built-in performance optimization, security scanning, and continuous learning in agent workflows, enabling more reliable and efficient agentic development.
Deployment reality
The available setup signal starts with: npm install ecc-universal. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build production-ready AI agent workflows that include custom skills, instinct-driven behaviors, memory optimization, and automated security scanning. Real-world use cases include automated code review, multi-harness agent orchestration, and research-first development pipelines. The ceiling is high: with 211K+ stars and 230+ contributors, it supports complex, cross-platform agent systems for enterprise-grade software engineering.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
affaan-m/ECC is tracked as a JavaScript project in the AI Agent, MCP Server, LLM Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 240k total stars, with +143 today, +1.5k this week, and +9.6k this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Moderate; the latest push was 59 days ago, and the open issue queue is 122, about 0.05% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 36k forks and 240k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 1 explicit source reference(s) for this report and a recent growth signal of 9.6k. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
LimitedNeeds clearer source references.
License
ReadyMIT is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
240k Stars
Reuse
36k Forks
Attention
240k Watchers
Maintenance
moderate
License
MIT
Open issues
122
ECC is a harness-native operator system for agentic work, providing a complete system of skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. It works across multiple AI agent harnesses including Codex, Claude Code, Cursor, OpenCode, Gemini, Zed, and GitHub Copilot.
Key Features
- Cross-harness agent workflows supporting 7+ AI agent harnesses - Built-in security scanning (AgentShield) and continuous learning - Production-ready skills, hooks, rules, MCP configurations, and legacy command shims
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
MCP Server
Model Context Protocol servers, clients, and integrations
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
npm install ecc-universal80
Health Score
Moderate
Commit Activity
Jan 18, 2026
Created
Aug 15, 2026
Last push
Star history will appear after daily syncs accumulate data.
Forks
Open
Watchers
affaan-m
GitHub profileobra/superpowers
An agentic skills framework & software development methodology that works.
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The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
multica-ai/andrej-karpathy-skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
n8n-io/n8n
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
✓
License
✓
Forked
~ Moderate
Maintained
Problem Solved
ECC solves the fragmentation of agent configurations across different AI coding harnesses by providing a single, reusable system that works with Claude Code, Codex, Cursor, and others. It also addresses the lack of built-in performance optimization, security scanning, and continuous learning in agent workflows, enabling more reliable and efficient agentic development.
Capabilities
Developers can build production-ready AI agent workflows that include custom skills, instinct-driven behaviors, memory optimization, and automated security scanning. Real-world use cases include automated code review, multi-harness agent orchestration, and research-first development pipelines. The ceiling is high: with 211K+ stars and 230+ contributors, it supports complex, cross-platform agent systems for enterprise-grade software engineering.
Bottom Line
ECC is ideal for developers and teams using multiple AI coding agents who want a unified, optimized, and secure workflow. It may be overkill for single-harness users or those seeking a simple configuration tool. The key trade-off is its comprehensive feature set versus the initial complexity of setup and learning.