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**Org-level AI autopilot operator**— scans GitHub issues labeled `autofix + queued`, invokes Codex to generate fixes, and opens pull requests automatically across the Coding-Autopilot-System organization.
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**Org-level AI autopilot control plane**- scans GitHub issues labeled `autofix + queued`, invokes Codex to generate fixes, and opens pull requests automatically across the Coding-Autopilot-System organization.
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Part of the [Coding-Autopilot-System](https://github.com/Coding-Autopilot-System) autonomous CI repair platform alongside [ci-autopilot](https://github.com/Coding-Autopilot-System/ci-autopilot) and [autopilot-demo](https://github.com/Coding-Autopilot-System/autopilot-demo).
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## Repo boundary
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-`autopilot-core` is the control plane: org-wide intake governance, operator scheduling, PR creation, and rollout visibility.
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-`ci-autopilot` is the worker/runtime pattern: the runner-hosted Python agent and workflow assets used to execute repairs.
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-`autopilot-demo` is the proof repo: a safe target that demonstrates the full failure-to-fix loop.
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## How it works
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```mermaid
@@ -28,30 +34,39 @@ flowchart LR
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## Quick start
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1. Set org variable `ORG` in GitHub Actions for this repo.
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2. Install `autopilot-create-issue.yml` into target repos (or use `autopilot-org-installer.yml`).
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2. Install `autopilot-create-issue.yml` into target repos, or use `autopilot-org-installer.yml`.
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3. Ensure a self-hosted Windows runner with Codex and `OPENAI_API_KEY` is online.
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4. Trigger `autopilot-operator.yml` manually to validate the setup.
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## Enterprise proof points
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- Centralized control plane with explicit issue-queue handoff instead of opaque direct mutation.
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- Auditable lifecycle: CI failure, intake issue, operator run, fix branch, and PR are all visible in GitHub.
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- Guardrailed execution: label-gated intake, skip labels for risky work, and verification before PR creation.
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