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If you are using an AI coding agent, you can ask it to install Aegis for you:
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```text
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Please read the installation instructions in https://github.com/GanyuanRan/Aegis carefully, identify the correct path for my AI coding host, install Aegis globally, restart or reload the host if needed, and verify that Aegis is fully available, including skill discovery and project workspace support.
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Please read the installation instructions in https://github.com/GanyuanRan/Aegis carefully, identify the correct path for my AI coding host, install Aegis globally, restart or reload the host if needed, then run complete-install verification from the Aegis method-pack root with `python scripts/aegis-doctor.py --write-config --json`. Treat the install as complete only if the JSON output includes `"ok": true`, `"workspaceSupport": "available"`, and `"configStatus": "configured"`; if the host uses a separate skill discovery directory, also verify it with `--discovery-root <path>`.
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```
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## Updating Aegis
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If Aegis is already installed, you can ask your AI coding agent to update it for you:
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```text
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Please update my installed Aegis to the latest main branch version from https://github.com/GanyuanRan/Aegis, using the correct update path for my current AI coding host, then restart or reload the host if needed and verify that Aegis is fully available, including skill discovery and project workspace support.
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Please update my installed Aegis to the latest main branch version from https://github.com/GanyuanRan/Aegis, using the correct update path for my current AI coding host, then restart or reload the host if needed and run complete-install verification from the Aegis method-pack root with `python scripts/aegis-doctor.py --write-config --json`. Treat the update as complete only if the JSON output includes `"ok": true`, `"workspaceSupport": "available"`, and `"configStatus": "configured"`; if the host uses a separate skill discovery directory, also verify it with `--discovery-root <path>`.
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```
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## Optional Lite Global Rules
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- Medium-complexity tasks require a baseline read set, a Spec Brief or stable requirements, a plan, and atomic tasks before TDD.
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- High-complexity tasks require a Design Spec and plan first, with user review where the workflow calls for it.
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Workflow Quality guardrails keep that routing practical: simple tasks stay on
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the fast path, medium/high-risk tasks get the right evidence and artifacts, and
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outputs use compact contracts before expanding into full workflow structure.
**Exploring approaches:** Propose 2-3 approaches with trade-offs and recommendation. Make scope boundary explicit: what's in, what's deferred, what belongs elsewhere.
unverified assumption, or "long-term stable" / `长期稳定` claim. Do not make it a
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universal design ceremony; return to this workflow once the decision surface is
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clean.
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**Presenting the design:** Scale sections to complexity. Cover only the surfaces that matter: architecture, components, data flow, error handling, testing, compatibility boundary. Get approval for the design before implementation when behavior, contract, architecture, or user-facing flow is being decided.
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2.**Internal consistency:** Do any sections contradict each other? Does the architecture match the feature descriptions?
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3.**Scope check:** Is this focused enough for a single implementation plan, or does it need decomposition?
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4.**Ambiguity check:** Could any requirement be interpreted two different ways? If so, pick one and make it explicit.
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5.**Boundary check:** Did you clearly mark invariants, compatibility boundaries, owners, and non-goals?
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5.**Boundary check:** Did you clearly mark invariants, compatibility
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boundaries, owners, and non-goals? If the spec endorses a risky approach,
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confirm the `first-principles-review``Decision Hygiene Review` result is
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reflected or explicitly marked unnecessary.
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Fix any issues inline. No need to re-review — just fix and move on.
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