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 skills add bgigurtsis/aegis-mcp --skill claudegit clone --depth 1 https://github.com/bgigurtsis/aegis-mcpWrote 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/bgigurtsis/aegis-mcp/claude)<a href="https://agentmods.dev/skills/bgigurtsis/aegis-mcp/claude"><img src="https://agentmods.dev/badge/skills/bgigurtsis/aegis-mcp/claude/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bgigurtsis/aegis-mcp/claude"><img src="https://agentmods.dev/badge/skills/bgigurtsis/aegis-mcp/claude.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00020 | $0.00381 |
| Opus 5 | $0.00010 | $0.00191 |
| Sonnet 5 | $0.00004 | $0.00076 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
aegis-guardrails 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 11d 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.
What it actually says
Aegis Guardrails
At session start:
- Read
aegis://summaryonce. - Use canonical IDs and
known_actionsfrom the summary for all tool calls.
Before producing code, call aegis_assess when the task involves any of:
- New endpoints
- Database access/query changes
- Authentication or authorization changes
- External API integrations
- File upload or file-processing logic
- Handling classified data in the
security-context.yaml - Input validation paths (request body, URL params, query params)
- Security configuration changes (CORS, headers, cookie settings, TLS behavior)
- Secrets or credential handling
- CI/CD or release pipeline changes
- Error handling changes for externally reachable flows
When calling aegis_assess, send canonical IDs from security-context.yaml:
boundary_crossingdata_types[]services[]auth_scopeinput_sources[]action(must be selected fromaegis://summary.known_actions)
After receiving results:
- Implement all returned requirements.
- Prefer returned approved patterns.
- Avoid returned anti-patterns.
- Apply returned security config, secrets, pipeline, and error-handling policies when present.
- If warnings include unknown ids, flag that the schema may need updates.
After generating security-relevant code, call aegis_review:
- Pass the
requirementsreturned byaegis_assess. - Pass
mappingswhere each item contains:requirement_idsatisfied_by(short explanation of the implementation)
- If
pass=falseormissingis non-empty, revise code before finalizing.
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.
- 11d ago First seen · 51 lines · 20 tokens per session scan A 8fbc58222465
aegis-guardrails is a skill published in the GitHub repository bgigurtsis/aegis-mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 20 tokens to every session and 381 once invoked, about $0.0001 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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