# cao-learning

> Claude/Codex skill by [@awslabs](https://omgskills.com/library/awslabs/index.md) · 969 stars · Updated 2026-08-01

Report task outcomes and distill lessons so the team improves across runs — report\_outcome after each unit of work, retrospector handoffs at natural boundaries, and applying injected lessons. Use in workflows that run repeatedly over similar work items. Requires memory.learning\_enabled; degrade silently when the tools report disabled.

## Install

```sh
git clone https://github.com/awslabs/cli-agent-orchestrator /tmp/cli-agent-orchestrator && ln -s /tmp/cli-agent-orchestrator/skills/cao-learning ~/.claude/skills/cao-learning
```

## From README

CAO Self-Learning CAO workflows can improve as they repeat: outcomes you report feed a retrospector agent that distills durable lessons into memory, and those lessons reach future sessions automatically. Your job depends on your role. All of this is opt-in infrastructure. If reportoutcome or a memory tool returns disabled: true, skip it silently and continue your task — learning is off for this run (often deliberately, e.g. a control run) and that is expected, not an error. If you are a SUPERVISOR Report an outcome after each meaningful unit of work One reportoutcome call per completed step, delegated task, or work item — after validation/review, not before: Rules for frictionnotes: 1–3 sentences, conclusions only — the root cause, not the story. NEVER paste transcripts, logs, stack traces, file contents, or secrets. Empty string on a clean pass is fine; the success flag already carries signal. Report failures faithfully — failed iterations are the most valuable learning signal.

## Links

- HTML page: https://omgskills.com/skills/awslabs/cli-agent-orchestrator/cao-learning/
- GitHub: https://github.com/awslabs/cli-agent-orchestrator
- Author: [@awslabs](https://omgskills.com/library/awslabs/index.md)

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