[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f18it3ix4h0c80":3},{"code":4,"message":5,"data":6,"timestamp":39},10000,"success",{"full_name":7,"position":8,"description":9,"language":10,"stars":11,"forks":12,"stars_gained":13,"age_days":14,"hn_threads":15,"one_liner":22,"summary_md":23,"topic":24,"github_topics":27,"license":35,"homepage":36,"updated_at":37,"history":38},"affaan-m\u002FECC",2,"The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.","JavaScript",287734,42650,22804,279,[16],{"id":17,"title":18,"points":19,"comments":20,"posted_at":21},50012876,"The operating system for AI harnesses",3,0,"2026-10-08","A shared layer of skills, memory and guardrails that makes coding agents behave consistently across different CLI harnesses.","## The problem\n\nAnyone running more than one coding agent knows the drift: a workflow tuned for Claude Code does not survive the jump to Codex or Cursor, so rules, memory and review habits get rebuilt per tool. ECC positions itself as an \"agent harness operating system\" that installs a common set of skills, instincts, memory and research-first rules on top of whichever harness you use, so behavior stays the same as you switch.\n\n## What's different\n\nThe usual approach is per-tool configuration: separate CLAUDE.md-style instruction files, ad-hoc slash commands, and hand-copied prompt libraries that rot quickly. ECC instead ships one plugin-installable package that targets several harnesses at once, paired with a companion security package (`ecc-agentshield`) and an optional hosted GitHub App for private repositories. That cross-harness packaging, plus a built-in security layer, is the real differentiator; most competing setups are single-tool and security is an afterthought.\n\n## Who it's for\n\nDevelopers who already use coding agents daily and rotate between two or more of them, or who want enforced review and security behavior rather than hoping the model remembers. Teams standardizing agent behavior across members can benefit from the GitHub App. Casual users of a single assistant, and anyone who dislikes prompt-layer abstraction or wants full control over every instruction, should stay away — the added indirection will cost more than it saves.\n\n## Cost of adoption\n\nNode.js-based install via npm or the `ecc@ecc` plugin; the README warns explicitly to install only from official channels because third-party mirrors may carry malware. The open-source core is MIT-licensed, but the hosted GitHub App for private repos is a paid tier, so budget accordingly. Maturity is the open question: the README is heavy on branding, sponsor logos and links, and light on concrete evidence about how the skills behave, how memory is scoped, or what the security layer actually checks. One warning sign is that it insists on not stacking a manual install on top of the plugin, which hints at configuration sensitivity.\n\n## Verdict\n\nThe repo is only months old yet has accumulated an enormous star count and is still gaining tens of thousands monthly — clearly a spike riding the coding-agent wave rather than a long-established tool. A single Hacker News thread with 3 points suggests the wider technical audience has barely scrutinized it yet. The core premise — one portable behavior layer across harnesses — is genuinely useful and the MIT license lowers the risk of trying it. But with a single maintainer, a sponsor-heavy README and thin technical documentation, treat it as promising and experimental: try it on a throwaway project before making it part of your daily workflow.",{"slug":25,"name":26},"developer-tools","Developer Tools",[28,29,30,31,25,32,33,34],"ai-agents","anthropic","claude","claude-code","llm","mcp","productivity","MIT","https:\u002F\u002Fecc.tools","2026-10-24T14:10:36Z",[],1792864252]