Marketplace version 2.14.2-build-2 is based on upstream 2.14.2. Installation and operation on DSH 0.2.1-rc.3 have not been verified.

Aegis

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.
• UI/UX rules on demand. [UI/UX governance]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/skills/ui-ux-governance/SKILL.md) carries project design rules, lower user effort, and scoped experience evidence through design, implementation, review, and verification.
• One method pack, every host. The same discipline works across Codex, Claude Code, OpenCode, Kimi, and other skill-aware hosts.

These numbers are bounded advisory evidence from Aegis 2.7.6 (2026-08-11), not a measurement of this release, a universal-quality claim, or completion authority. See the full benchmark and its limitations.

Quick Install

Install and enable Aegis from the NanoAI marketplace, reload DSH when prompted, and describe your task in a new session. Enable only one copy of the Aegis skills.

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.

[Sanitized JSON]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/benchmarks/results/gpt-5-6-sol-xhigh-extended-20260811-v2-7-6.json) · [English table]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/benchmarks/results/gpt-5-6-sol-xhigh-extended-20260811-v2-7-6.en.md) · [中文表格]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/benchmarks/results/gpt-5-6-sol-xhigh-extended-20260811-v2-7-6.zh-CN.md) · [Methodology]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_AGENTIC_BENCHMARK_BASELINE.md)

A newer [matrix-v7 standard snapshot for Aegis 2.10.8]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/benchmarks/results/gpt-5-6-sol-xhigh-standard-20260925-v2-10-8.en.md) covers 22 cases with one observation per arm and case. It uses a different run profile from the extended snapshot above and does not measure the changes in this release.

Try Aegis Methods in a Coding Runtime

Autoloom is our AI coding desktop client. It brings Aegis's change necessity analysis, complexity governance, and delivery review into development execution, helping you spend less time repeating instructions and inspect the evidence behind delivered changes.

Currently available as a Windows x64 Alpha. The client is free; choose your model provider, which bills model usage separately.

Explore Autoloom · Download Alpha

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]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/GLOBAL_USER_RULES_TEMPLATE.md) 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]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/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][i18n1].

Supported Hosts

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

Read the current host verdict before making support claims:

• [Host compatibility matrix][i18n2]
• [Known limitations]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_KNOWN_LIMITATIONS.md)

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]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_FAST_TRACK_PLAYBOOK.md) 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]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_FAST_TRACK_PLAYBOOK_ZH.md).

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][i18n3], [Workflow Quality Baseline]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_WORKFLOW_QUALITY_BASELINE.md), [Complexity Governance Baseline]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_COMPLEXITY_GOVERNANCE_BASELINE.md), and [TDD mode][i18n1].

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]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_TRIGGER_HEALTH_BASELINE.md).

Community & Extending

• Feedback and discussion: [GitHub Discussions]([repository withheld]/discussions) · [Issues]([repository withheld]/issues) · LINUX DO · DEV.to
• Extend Aegis: write your own skill with aegis:writing-skills; see the [Workflow Guide][i18n3].
• Follow along: [RELEASE-NOTES.md]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/RELEASE-NOTES.md) · [Releases]([repository withheld]/releases)

Aegis and Autoloom

• Aegis is our open-source method pack for bringing engineering discipline to your existing AI coding host. It remains independently usable with multi-host support; Autoloom is not required.
• Autoloom is our independent desktop product for experiencing Aegis's core methods in the coding runtime. Its client source code is not currently public. Its repository maintains downloads, release scope, and feedback.

Autoloom carries the runtime-core direction while Aegis retains its method-pack boundary. The benchmarks on this page measure Aegis, not Autoloom. See the [product relationship]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_PRODUCT_BASELINE.md#current-product-relationship-autoloom) for the boundary, or bring a scoped task to Autoloom and share your experience.

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]([repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/LICENSE).

[i18n1]: [repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_TDD_MODE.md [i18n2]: [repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_HOST_COMPATIBILITY_MATRIX_SNAPSHOT.md [i18n3]: [repository withheld]/blob/28dfb41e4d08239c6b47c6425396c81cdd10601e/docs/current/AEGIS_WORKFLOW_GUIDE.md