by LearnPrompt · Codex Skill · ★ 223
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CC Harness Skills "The parts that separate a fun demo from a stable toolchain: memory, compression, verification, routing, proactive jobs." Portable agent skills distilled from a publicly mirrored coding-agent codebase, then rewritten so they can be installed in , , and without depending on private runtime internals. This repo is not a source dump. It is a cleaned skill pack: prompts extracted into reusable templates host-agnostic helper scripts portable bundles release and smoke-test docs for public distribution If you are building with coding agents, these are the parts that usually separate a fun demo from a stable toolchain: memory that stays useful instead of rotting compression that preserves user corrections verification that does not trust "done" multi-agent routing that does not pollute the main context proactive jobs with explicit limits This repo packages those patterns into six installable skills. Why This Exists Most agent repos share the same hard problems: how t
| Stars | 223 |
| Forks | 63 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.7880672137727/100 |
| Last Updated | 2026-07-10 |
| Created | 2026-04-01 |
| Platforms | codex, python |
| Est. Tokens | ~4k |
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cc-harness-skills is Portable CC-inspired skills for memory, verification, multi-agent coordination, context compression, and proactive coding-agent workflows.. It is categorized as a Codex Skill with 223 GitHub stars.
cc-harness-skills is primarily written in Python. It covers topics such as agent-harness, agent-memory, ai-agent.
You can find installation instructions and usage details in the cc-harness-skills GitHub repository at github.com/LearnPrompt/cc-harness-skills. The project has 223 stars and 63 forks, indicating an active community.
cc-harness-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to cc-harness-skills on Agent Skills Hub include SWE-AF, Overture, mateclaw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.