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/skill-forge-ai/aegis/cc-skillnpx skills add skill-forge-ai/aegis --skill cc-skillgit clone --depth 1 https://github.com/skill-forge-ai/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/skill-forge-ai/aegis/cc-skill)<a href="https://agentmods.dev/skills/skill-forge-ai/aegis/cc-skill"><img src="https://agentmods.dev/badge/skills/skill-forge-ai/aegis/cc-skill.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 | $0.00089 | $0.02528 |
| Opus 5 | $0.00044 | $0.01264 |
| Sonnet 5 | $0.00018 | $0.00506 |
| Haiku 4.5 | $0.00009 | $0.00253 |
Grade A, and why
aegis 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 4d ago.
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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Aegis — AI Development Quality Guardian
Five-layer defense for AI-assisted software development.
Modes
Lite Mode (Default for small tasks)
- Design Brief only → straight to implementation
- Use when: solo dev, single-service feature, quick fix
Full Mode (Multi-service / team projects)
- Complete contract-first workflow
- Use when: multiple API boundaries, team collaboration, complex features
Phase 0: Workspace Architecture Detection
Before starting the Aegis workflow, detect the project architecture to choose the right contract strategy.
Auto-Detection
Scan the workspace for indicators:
- Both
frontend//client//web/ANDbackend//server//api/directories → Monorepo package.jsonwith workspaces containing both frontend and backend → Monorepo- Only one side present (pure frontend or pure backend) → Split Workspace
If Split Workspace detected, ask the human:
Detected: This workspace contains only {frontend|backend} code.
Where does the other side live?
(a) Another repo managed by the same agent (I can access it)
(b) Another repo managed by a different agent/workspace (I cannot access it)
(c) This is actually a monorepo (I missed something)
Architecture Modes
| Mode | Contract Location | Sync Method |
|---|---|---|
| Monorepo | contracts/ in project root |
Direct (same repo) |
| Multi-Repo, Single Agent | Lead workspace's contracts/, copied to each repo |
Copy before dispatch |
| Cross-Agent, Cross-Workspace | Dedicated contract repository | Git submodule / package / lead copy-sync |
Cross-Workspace: Contract lives in an independent Git repo. Each agent's workspace integrates it as read-only. Contract Change Requests go through the Lead who has merge rights. See references/multi-agent-protocol.md for the full protocol.
Phase 1: Design
Before any non-trivial feature, create a Design Brief:
- Read
templates/design-brief.mdfor the template - Fill in: Problem Statement, Architecture Overview, Key Decisions, Module Boundaries, API Surface, Known Gaps, Testing Strategy
- Submit for human review
- Gate: Do not proceed to Phase 2 until Design Brief is approved
What ships with it
20 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.
- references/contract-first-guide.md 2.3 KB
- references/contract-guide.md 2.5 KB
- references/dispatch-protocol.md 2.5 KB
- references/multi-agent-protocol.md 4.4 KB
- references/testing-strategy.md 13 KB
- scripts/detect-stack.sh 6.0 KB runs code
- scripts/gap-report.sh 1.5 KB runs code
- scripts/generate-shared-types.sh 802 B runs code
- scripts/generate-types.sh 4.5 KB runs code
- scripts/init-project.sh 6.0 KB runs code
- scripts/setup-guardrails.sh 15 KB runs code
- scripts/validate-contract.sh 2.1 KB runs code
- templates/api-spec-starter.yaml 2.3 KB
- templates/claude-md.md 2.7 KB
- templates/contract-test-example.ts 3.0 KB runs code
- templates/design-brief.md 2.3 KB
- templates/docker-compose.integration.yml 1.9 KB
- templates/errors-starter.yaml 1.2 KB
- templates/implementation-summary.md 673 B
- templates/shared-types-starter.ts 1000 B runs code
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.
- 4d ago First seen · 270 lines · 89 tokens per session scan A 0899c78b6538
aegis is a skill published in the GitHub repository skill-forge-ai/aegis (2 stars, last pushed 5mo ago), licensed MIT. It adds 89 tokens to every session and 2,528 once invoked, about $0.0004 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 2026-08-31.
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Review a GitHub pull request and post one formal review — advance the existing discussion and give precision-first, high-signal feedback. Judgement on the diff, not a build gate — CI validates that it builds, and a targeted probe is allowed as evidence. Use when asked to review a PR or on a cron PR scan.
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