Aegis is a method pack that guides coding agents to inspect a project's baseline, make bounded changes, and verify their work with fresh evidence. It is for people using coding-agent hosts who want fewer unverified changes and less unnecessary process. The catalogue add-ons implement this method through skills, instructions, commands, a hook, and a plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ganyuanran/aegis/subagent-driven-developmentnpx skills add GanyuanRan/Aegis --skill subagent-driven-developmentgit clone --depth 1 https://github.com/GanyuanRan/AegisWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ganyuanran/aegis/subagent-driven-development)<a href="https://agentmods.dev/skills/ganyuanran/aegis/subagent-driven-development"><img src="https://agentmods.dev/badge/skills/ganyuanran/aegis/subagent-driven-development.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00047 | $0.01901 |
| Opus 5 | $0.00023 | $0.00950 |
| Sonnet 5 | $0.00009 | $0.00380 |
| Haiku 4.5 | $0.00005 | $0.00190 |
Grade A, and why
subagent-driven-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execute
→ Have an implementation plan with independent tasks? → Fresh subagent per task + two-stage review.
- Read plan, extract all tasks, create TodoWrite
- Per task: dispatch implementer → answer questions → implementer completes
- Review stage 1 (spec compliance) → fix gaps → re-review until ✅
- Review stage 2 (code quality) → fix issues → re-review until ✅
- Coordinator verifies, commits the coherent task, updates checkpoint/drift → next task → All tasks done: final review → verification receipt; branch finishing only when needed.
Subagent-Driven Development
Execute plan by dispatching fresh subagent per task, with two-stage review after each: spec compliance review first, then code quality review.
Why subagents: You delegate tasks to specialized agents with isolated context. By precisely crafting their instructions and context, you ensure they stay focused and succeed at their task. They should never inherit your session's context or history — you construct exactly what they need. This also preserves your own context for coordination work.
Core principle: Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration
When to Use
Use when you have a written implementation plan with mostly independent tasks and want to stay in the current session. For cross-session execution, use executing-plans instead.
The Process
- Read plan, extract all tasks with full text, create TodoWrite
- Per task: dispatch implementer with task text + baseline refs + checkpoint + non-goals
- Implementer completes → dispatch spec compliance reviewer → fix gaps → re-review until ✅
- Dispatch code quality reviewer → fix issues → re-review until ✅
- Coordinator runs fresh verification, stages only task-owned paths, commits the coherent task, reads back Git state, updates checkpoint/drift → next task
- All tasks done → final code reviewer → completion verification; use branch finishing only for a task-created branch/worktree or requested integration
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +18 lines 0d7c1a36d5d0
- 5d ago First seen · 158 lines · 47 tokens per session scan A 825062793795
subagent-driven-development is a skill published in the GitHub repository GanyuanRan/Aegis (1,167 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 1,901 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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