Compile a goal into a verified agent-harness loop for a domain and drive it to close — /cs:harness <domain> <goal>
复制下面这句话,粘贴给 Claude Code、Codex、Cursor 等 AI 编程工具,它会读取安装说明并在你确认后完成安装。
请阅读 https://ai.atlankj.com/install/asset/gh-claude-skills-1e436bf70b3d ,按照其中的说明把「cs-harness」安装到你(当前 AI 工具)中。执行前先告诉我将运行的命令和写入的位置,等我确认。
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Parse $ARGUMENTS: the first token is the domain (one of the 18 manifest names under
engineering/agent-harness/skills/agent-harness/assets/harnesses/); the rest is the goal.
If the domain token doesn't match a manifest file, list the available manifests and ask.
python3 engineering/agent-harness/skills/agent-harness/scripts/goal_compiler.py --goal "<goal>" --manifest engineering/agent-harness/skills/agent-harness/assets/harnesses/<domain>.json --out .agent-harness/plan.json
nearest_candidates, ask whether to switch domain or refine the goal.python3 .../scripts/loop_controller.py init --plan .agent-harness/plan.json --state .agent-harness/state.jsonnext → execute the task per its skill's SKILL.md → record →
verify. For long goals, spawn the harness-runner agent per task instead of executing
inline, one at a time (writes stay serialized).status and the failing evidence; the user decides:
fix and continue, waive with a reason, or abandon.close --state .agent-harness/state.json; paste the handoff block
(tasks, statuses, evidence, waivers) as the deliverable summary..agent-harness/ is git-ignorable working state; the handoff block is the record.