🌐 英语 ·中文

CC 背带技能

“区分有趣演示与稳定工具链的部分：内存、压缩、验证、路由、主动作业。”

便携代理技能从公开镜像的CC编码代理代码库提炼出来，然后重写，使其能够安装在Claude Code、Codex和OpenClaw中，无需依赖私有运行时内部结构。

快速启动

一行安装（任何技能兼容的代理）：

先做基本的束检验：

或者手动复制一个技能到主机运行时：

然后在Claude Code中调用：

然后在Codex中调用：

然后在OpenClaw中核对：

检查后发布到 ClawHub：

这个仓库不是源代码导出。它是一个清理过的技能包：

• 提取到可复用模板中的提示
• 宿主无关辅助脚本
• 便携式SKILL.md捆绑包
• 公开分发的释放和烟雾测试文件

如果你用编码代理构建，这些通常是区分有趣演示和稳定工具链的关键部分：

• 记忆保持有用而非腐朽
• 保持用户更正的压缩
• 不信任“已完成”的验证

🌐 English · 中文

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 CC coding-agent codebase, then rewritten so they can be installed in Claude Code, Codex, and OpenClaw without depending on private runtime internals.

Quick Start

One-line install (any skills-compatible agent):

# all six skills
npx skills add LearnPrompt/cc-harness-skills -g

# or a single skill
npx skills add LearnPrompt/cc-harness-skills -s verification-gate -g

Run the basic bundle checks first:

bash ./skills/check_all.sh

Or copy one skill into a host runtime manually:

mkdir -p ~/.claude/skills
cp -R ./skills/dream-memory ~/.claude/skills/

Then invoke it in Claude Code:

Use /dream-memory to consolidate the memory directory before we continue.

mkdir -p ~/.codex/skills
cp -R ./skills/dream-memory ~/.codex/skills/

Then invoke it in Codex:

Use /dream-memory and consolidate recent memory files into one stable index.

mkdir -p ~/.openclaw/workspace/skills
cp -R ./skills/dream-memory ~/.openclaw/workspace/skills/

Then check it in OpenClaw:

openclaw skills info dream-memory

Publish to ClawHub after checks:

bash ./skills/publish_all.sh 0.1.0

This repo is not a source dump. It is a cleaned skill pack:

• prompts extracted into reusable templates
• host-agnostic helper scripts
• portable SKILL.md 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 to keep memory useful without storing stale code facts
• how to compress long conversations without losing user corrections
• how to verify claimed completion instead of trusting it
• how to coordinate multiple agents without turning the main context into garbage
• how to add lightweight proactive behavior without building a dangerous daemon