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Run cross-harness AI agent workflows with 68 agents, 286 skills, and AgentShield security scanning across Claude Code, Codex, Cursor, and OpenCode.
Teams using Claude Code or Codex gain a centralized skills registry where conventions, TDD workflows, and security rules are published once and synced to every seat at session start. New developers pick up team patterns without manual onboarding documentation.
If your team has no shared coding conventions and no plans to build them, the skills layer adds configuration overhead without a matching benefit.
AgentShield scans CLAUDE.md files, hook configurations, MCP server settings, and prompt surfaces against 102 rules. Developers running AI agent tooling at scale can audit locally or integrate with the GitHub App for PR-level enforcement before conventions reach the broader team.
If your security requirements are already covered by existing tooling and you have no AI agent-specific exposure surface to audit, the scanning layer may not justify the setup cost.
The 68 agents library covers specialized planning, build repair, research, and code review tasks. The continuous learning system observes sessions and promotes repeated workflows to named instincts with confidence scoring, so the harness improves over time without manual updates.
Developers who use a single harness casually may find the full install profile heavier than their workflow requires. The plugin install path is lighter than the full manual setup.
Public repos get the GitHub App for free. Commenting /ecc-tools analyze on any issue triggers an analysis of git history and generates a pull request with reusable skills and defaults. Teams can evaluate the full repo-native workflow before committing to a paid tier.
Repos with minimal git history or no established patterns will get sparse output from the analysis. The GitHub App's value scales with the richness of your existing commit history.
AI coding agents are powerful in isolation but stateless by default. Each session starts without knowledge of your team's conventions, preferred testing patterns, or past decisions. Developers configure their agents differently, so the same codebase gets inconsistent treatment depending on who is at the keyboard. Security checks, TDD workflows, and code review steps are ad hoc at best.
The challenge scales with team size. Without a shared skills layer or centralized convention management, onboarding a new developer means documenting how they should configure their agent setup separately from how the team actually works. Commercial orchestration platforms like Factory.ai and CrewAI address some of this but require adopting their specific runtime rather than extending the tools teams already use.
Works across Claude Code, Codex, Cursor, OpenCode, Gemini CLI, Zed, and more via a single install. Each harness gets a matching adapter or plugin so ECC's skills and agents load from the same source without duplication across setups.
Ships with 286 skills covering TDD, security review, research, documentation, frontend, data, ML, and operations, plus 68 specialized agents for planning, build repair, architecture, and code review. Teams publish skills centrally and every seat syncs on session start.
Scans every agent session against 102 security rules covering prompt injections, hook configurations, MCP server settings, permissions, secrets exposure, and agent files. Runs locally with no automatic data upload; integrates with the GitHub App for PR-level enforcement.
Observes sessions and extracts repeated corrections, error-to-fix sequences, and workflows into atomic instincts with confidence scoring. Idle instincts decay on a schedule; a reviewable prune plan shows what goes and why before anything is removed.
A local-first control plane for cross-harness session visibility, token optimization, operator review, and shared policy context. Session and task state are visible across multiple harnesses from a single surface, shipped as of ECC 2.0.0 stable.
Analyzes repository git history via a /ecc-tools analyze comment trigger and generates a pull request with reusable skills and defaults. Free for public repos; private repo coverage and deeper AgentShield-backed review checks start at $19 per seat per month.
GitHub Copilot is a per-seat AI coding assistant focused on inline completions, chat, and multi-file edits inside editors. ECC is a harness layer that adds structure above the agent session, not a completion engine. The two serve different jobs and can coexist.
Teams running Claude Code, Codex, or Cursor can add ECC to gain session memory, skills libraries, and security scanning without changing their primary AI coding tool. Copilot's value is the completion itself; ECC's value is the convention, memory, and governance around it.
| Feature | ECC | GitHub Copilot |
|---|---|---|
| License | MIT (OSS layer) | Proprietary |
| Self-hosting | Yes | No |
| Session memory | Yes (continuous learning) | No |
| Security scanning | Yes (AgentShield, 102 rules) | Limited |
| Skills library | 286 skills, 68 agents | None |
| Cost | Free OSS; $19/seat GitHub App | $10-19/user/month |
ECC is the stronger choice for teams that need reproducible agent behavior, cross-harness portability, or session-level security governance. GitHub Copilot is the stronger choice for teams whose primary need is AI-powered inline completion inside VS Code or JetBrains without additional orchestration setup.
Factory.ai is a commercial AI software engineering service that runs managed agents on its own infrastructure. ECC extends the coding tools teams already use rather than routing work through a separate runtime.
Factory.ai handles infrastructure centrally and abstracts the underlying model. ECC is explicit about which harness it extends and what each install profile adds. Teams that want a fully managed engineering automation service without local infrastructure should evaluate Factory.ai. Teams that want to stay local, keep agent configurations auditable, and extend existing harnesses at no base cost should start with ECC's OSS layer.
| Feature | ECC | Factory.ai |
|---|---|---|
| License | MIT (OSS layer) | Proprietary |
| Deployment | Local harness extension | Cloud-managed |
| Agent runtime | Your existing harness | Factory's infrastructure |
| Base cost | Free (OSS) | Commercial, contact sales |
| Auditability | Full (local) | Limited (managed) |
Install ECC globally via npm for cross-harness setup across Claude Code, Codex, and Cursor.
```bash
npm install -g ecc-universal
```The OSS layer is MIT licensed and free. This covers the full skills library, 68 agents, hooks, and AgentShield scanner without any per-seat cost. The GitHub App is also free for public repos. Private repo coverage and deeper review features start at $19 per seat per month in the Pro tier. Enterprise pricing is available on request.
ECC has native support for Claude Code (via plugin commands) and Codex (via the repo-marketplace plugin). Cursor, OpenCode, Gemini CLI, Zed, and others are supported via install adapters. The README notes that non-Claude harnesses receive capability-limited adapters rather than full feature parity. Check the platform support matrix in the README before assuming a feature is available on your harness.
AgentShield scans agent configuration files, including CLAUDE.md, hook definitions, MCP server settings, and prompt surfaces, against 102 security rules. It runs locally with no automatic data upload. It integrates with the GitHub App for PR-level scanning when teams want automated enforcement on incoming configuration changes.
Skills are reusable instruction sets that define how the coding agent should behave in specific workflows, such as test-driven development, security review, or documentation generation. Agents are specialized subagents that handle a distinct task autonomously, such as planning a feature, repairing a build failure, or reviewing code quality. ECC ships 286 skills and 68 agents.
The OSS layer runs locally with no remote dependency. You clone the repo, pick an install profile, and run the install command for your target harness. The GitHub App is a managed tier for teams who want repo-native automation and PR review integration without running infrastructure. The two paths can be combined: run the OSS layer locally and add the GitHub App for team-level governance.
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