Aegis

作者 GanyuanRan已验证

Make AI coding agents architecture-aware: baseline-first, evidence-verified, drift-checked, and safe across long tasks.

1,113
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Python
语言
2026/8/23
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⚠️ 第三方软件声明

本 Skill 为第三方开源软件,独立托管于 GitHub。SkillTip 仅为信息目录,不控制或维护底层仓库。所显示的安全检查为自动化且范围有限,安装前请自行审查源码。

阅读服务条款

安装

添加到你的 Claude Code skills 目录:

# Add to your Claude Code skills
git clone https://github.com/GanyuanRan/Aegis

快速入门

使用 Aegis 等 Skills 的指南。

安全报告

已验证

上次扫描:—

{
  "status": "PASSED",
  "issues": []
}

README.md

Aegis on olud.ai

Aegis architecture-driven AI coding agent hero banner

Aegis

Aegis Method Pack
Make your AI coding agent trustworthy: fewer reworks, safer changes, proof before "done".

English · 中文 · Fast-Track Playbook · 速通秘籍

Stop babysitting your agent. Aegis makes your agent plan against your real baseline before it edits, prove completion with fresh evidence, and leave simple tasks alone — you get fewer reworks, safer changes, and less blind trust in "done".

What You Get

Aegis is a method pack that makes AI coding agents work like disciplined engineers — so you don't have to watch them.

  • Fewer reworks. Your agent aligns with your project's real baseline — owners, contracts, boundaries — before touching code. It stops guessing, and so do you.
  • Safer changes. Measured on a frozen held-out A/B benchmark: contract pass rate 61.67% → 93.33%, unsafe outcomes 13.33% → 0%.
  • Proof before "done". Completion claims ship with fresh verification evidence, covered scope, and residual risk. You read evidence, not vibes.
  • No ghost code. Retired fallbacks and old paths are tracked or removed with a retirement trigger — technical debt stops accumulating silently.
  • Simple tasks stay simple. Trivial requests stay on the fast path; ceremony only appears when the task genuinely needs it.
  • One method pack, every host. The same discipline works across Codex, Claude Code, OpenCode, Kimi, and other skill-aware hosts.

The numbers above are bounded advisory evidence from the frozen benchmark below, not a universal-quality or completion-authority claim.

Measured Agentic Benchmark

A frozen held-out A/B benchmark for Aegis 2.7.6 (2026-08-11) kept the Codex client, prompts, projects, tool policy, and requested the same gpt-5.6-sol / xhigh setting in both arms; only the Aegis projection differed. Across 120 valid runs on 20 cases, contract pass rate was 61.67% → 93.33% (+31.67 pp) and unsafe outcomes were 13.33% → 0%. The 95% case-cluster interval was +15.00 pp to +50.00 pp. This is bounded advisory evidence; review was arm-hidden technical review, not independent human review, and host events did not return the observed model identity.

Aegis agentic benchmark: with and without Aegis

Sanitized JSON · English table · 中文表格 · Methodology

Quick Install

New here? The fastest start is one prompt to your agent — the full install-and-verify flow is below.

Give this prompt to your AI coding agent:

Read https://github.com/GanyuanRan/Aegis, identify my current AI coding host, and install Aegis globally using the correct host guide. If the host is the official DeepSeek Harness (`dsh`), treat global/minimal installation as native profile-plugin installation with `dsh plugin --profile <profile> add "git+https://github.com/GanyuanRan/Aegis.git"`; do not silently substitute the direct-child compatibility path unless the plugin manager is unavailable and I explicitly approve compatibility mode. Restart or reload the host if needed, then run complete-install verification from the installed Aegis method-pack root. Do not run the doctor command from the target project directory. First locate `<aegis-method-pack-root>`, then run `cd <aegis-method-pack-root> && python scripts/aegis-doctor.py --write-config --json`. Treat the install as complete only if the JSON includes `"ok": true`, `"workspaceSupport": "available"`, and `"configStatus": "configured"`; if the host uses a separate skill discovery directory, also verify it with `--discovery-root <path>`; if the host guide declares a skill directory name prefix, also pass `--discovery-name-prefix <prefix>`. Also complete the selected host guide's native activation and automatic-entry checks; file discovery or a generic doctor result alone is not sufficient when the host provides a plugin, hook, or session-start bootstrap contract.

Updating Aegis

After a complete install has registered the current host, later updates can use natural language such as update Aegis or the explicit skill request aegis:update. The agent can route either form through the local update path: locate the installed method-pack root, use the host-scoped registry, and call scripts/aegis-update.py for the current host by default. Updating every registered host requires an explicit --all request. Aegis does not run background automatic updates by default.

Before You Use It

Aegis is currently:

Aegis Method Pack (runtime-ready)

It is not the full Aegis Platform, a daemon, a background runner, a runtime core, an authoritative GateDecision, an authoritative PolicySnapshot, or final completion authority. User instructions and target-project rules outrank Aegis guidance.

The global routing prefix is an optional, manually copied host/profile projection. It does not install Aegis or prove skill discovery. If the host already has reliable Aegis bootstrap and routing, no extra global rule is usually needed. Otherwise, add the prefix at the very beginning of the existing global user rules without changing unrelated content.

This copied prefix is not managed by aegis:update. Users who previously copied the retired Lite / Advanced profiles should replace only those old Aegis blocks with the unified prefix. The prefix follows the host or session activation mode, defaults to auto when none is declared, and leaves detailed workflow governance to the loaded Aegis skills. Host-native skill matching may still remain host-controlled.

Activation mode defaults to automatic. To switch to explicit mode, run this from the installed method-pack root:

cd <aegis-method-pack-root>
python scripts/aegis-doctor.py activation-mode explicit

Restart the host after changing activation mode. Details and host caveats live in docs/current/AEGIS_ACTIVATION_MODE.md.

TDD mode defaults to off: Aegis does not automatically require TDD, and completion verification still applies. To enable automatic TDD routing when you want Aegis to choose strict, light, or skipped by task risk:

cd <aegis-method-pack-root>
python scripts/aegis-doctor.py tdd-mode auto

You can also request strict TDD directly in a query with explicit markers such as TDD Route: strict, strict TDD, test-first, or RED / GREEN / REFACTOR.

Details live in docs/current/AEGIS_TDD_MODE.md.

Supported Hosts

Aegis keeps a multi-host, plugin-installable distribution goal.

Host groupCurrent statusStart here
Codex, OpenCodeFresh evidence exists for the current method-pack scopeCodex, OpenCode
Claude Code, CodeBuddy, DeepSeek-TUI, DeepSeek Harness, Trae, GitHub Copilot, Qoder, Kimi Code CLI, ZCode, Grok BuildInstall guides exist; release-level fresh host smoke is still pendingClaude Code, CodeBuddy, DeepSeek-TUI, DeepSeek Harness, Trae, GitHub Copilot, Qoder, Kimi Code CLI, ZCode, Grok Build
CC GUI (JetBrains IDEA)Structural IDE plugin layer support for Claude Code / OpenAI-GPT provider paths; release-level fresh host smoke is still pendingCC GUI
Antigravity CLI, Antigravity IDE, Antigravity AppAntigravity CLI is the current active closeout target; IDE/App remain structural targets and release-level fresh host smoke is still pendingAntigravity
Pi CLI, OMP, OpenClaw, Hermes AgentStructural Agent Skills / SKILL.md skill-host adaptations; release-level fresh host smoke is still pendingPi CLI, OMP, OpenClaw, Hermes Agent
Gemini CLIRetired; Aegis no longer ships or verifies a Gemini CLI adapterCompatibility Matrix

Read the current host verdict before making support claims:

Start Fast With Aegis

After installation and host restart, use normal language. Aegis matches the method to the work; name a mode directly when you want less ambiguity.

Why does this login failure happen? Diagnose it before changing code.
Grill me on whether we should ship a hosted version first.
Aegis goal: Fix the auth refresh bug without rewriting the auth system.
Review this diff independently before I merge it.

Read the Fast-Track Playbook for Aegis's lightweight operating model, how it differs from standalone skill packs, its five engineering moats, project workspace lifecycle, natural trigger phrases, controls, and troubleshooting. The Chinese version is Aegis 速通秘籍.

Use these explicit requests when you need tighter control:

  • Aegis goal: ... frames scope, success evidence, and boundaries.
  • Grill me ... or 审问我 ... starts a decision interview; it asks one decision question at a time and does not plan or implement.
  • TDD Route: strict, strict TDD, or test-first explicitly requests strict test-first work. TDD is otherwise off by default.
  • aegis:first-principles-review or review this from first principles pressure-tests a complex direction before implementation.
  • aegis:update updates the installed method pack through its host-aware path.

For non-trivial project work, Aegis can passively reuse relevant canonical language from CONTEXT.md or a bounded context selected by CONTEXT-MAP.md. It activates domain modeling only when a term is resolved, ambiguous, renamed, deprecated, or conflicting. High-confidence existing facts may synchronize directly; unresolved domain decisions remain user-owned. Files are created lazily on the first resolved term and remain glossary-only. Stable bytes can be cache-friendly, but Aegis does not guarantee provider cache hits or savings.

Aegis preserves Workflow Quality by keeping simple work light and expanding only when risk warrants it. For deeper method detail, read the Workflow Guide, Workflow Quality Baseline, Complexity Governance Baseline, and TDD mode.

If a capability does not trigger as expected, use trigger-chain diagnosis: install/version visibility, host skill discovery, activation mode, using-aegis routing, task-to-skill matching, and context pressure. Read the Trigger Health Baseline.

For Maintainers

Primary verification entry:

bash tests/e2e/run-all.sh --full --host-profile fast

Focused docs / method-pack checks:

bash tests/e2e/boundary-compliance-check.sh
bash tests/e2e/workflow-quality-check.sh
bash tests/e2e/install-verification-policy-check.sh
bash tests/e2e/layer1-fast-check.sh --host-profile none

Read:

Community & Extending

Relationship To Superpowers

Aegis is derived from Superpowers, created by Jesse Vincent. Superpowers pioneered composable, multi-harness agent skills. Aegis keeps that foundation and adds an architecture- and evidence-focused method layer for real software projects.

Additional inspiration comes from mattpocock/skills, especially concise communication, shared language, and disciplined debugging patterns. These ideas were re-implemented in Aegis format rather than copied verbatim.

License

MIT License. See LICENSE.

常见问题

What is Aegis?

Aegis is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by GanyuanRan. Make AI coding agents architecture-aware: baseline-first, evidence-verified, drift-checked, and safe across long tasks. It has 1,113 GitHub stars.

Is Aegis safe to use?

Yes. Aegis passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install Aegis?

Clone the repository with "git clone https://github.com/GanyuanRan/Aegis" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is Aegis written in?

Aegis is primarily written in Python. It is open-source under GanyuanRan on GitHub, so you can review or fork the full source.

Are there alternatives to Aegis?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Aegis against similar tools.

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