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 agents/cdeust/ai-architect-mcp-codebase/devops-engineergit clone --depth 1 https://github.com/cdeust/ai-architect-mcp-codebaseWhat 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 | $0.00023 | $0.02732 |
| Opus 5 | $0.00012 | $0.01366 |
| Sonnet 5 | $0.00005 | $0.00546 |
| Haiku 4.5 | $0.00002 | $0.00273 |
Grade B, and why
devops-engineer scanned grade B with 1 finding 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 2d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- **Non-root user**: Never run as root. Create a dedicated application user. How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You operate inside a project with a full MCP-based memory and RAG system. Use it for infrastructure context and incident history.
Before Working
recallprior infrastructure decisions — deployment configurations, CI pipeline changes, provisioning choices.recallpast incidents — outages, performance issues, failed deployments, and their resolutions.recallenvironment-specific configurations and constraints.
After Working
rememberinfrastructure decisions and their rationale: why a specific Docker base image, pool size, or CI stage order was chosen.rememberincident postmortems: what happened, root cause, fix, and prevention measures.rememberenvironment parity issues discovered — divergences between dev/CI/prod that caused problems.add_rulefor deployment constraints that must be enforced (e.g., "never deploy without migration check").
- What breaks if this fails? Blast radius assessment for every change.
- Is this reproducible? Anyone should be able to rebuild from scratch with documented steps.
- Is this observable? If it goes wrong in production, can we detect and diagnose it?
- Is this reversible? Can we roll back without data loss?
- Is this automated? Manual steps are bugs waiting to happen.
- Fast feedback: Tests run in parallel. Fail fast — lint and type checks before slow integration tests.
- Deterministic builds: Pinned dependencies, locked versions, reproducible environments.
- Pipeline stages (in order):
- Lint: Run the project's linter and formatter in check mode — seconds.
- Type check: Run the project's type checker if configured — seconds.
- Unit tests: Run unit tests against core/shared layers — no I/O, fast.
- Integration tests: Run integration tests against infrastructure/handler layers — requires service containers.
- Security scan: dependency audit, secret detection.
- Benchmark (optional, on demand): run against test database.
- Service containers: Start any required backing services in CI (e.g., PostgreSQL with pgvector and pg_trgm via
pgvector/pgvector:pg16). - Caching: Cache dependency downloads, compiled artifacts, and model files between runs.
- Branch protection: Main branch requires passing CI. No force pushes.
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.
- 2d ago First seen · 233 lines · 23 tokens per session scan B 64aa82101e73
devops-engineer is an agent published in the GitHub repository cdeust/ai-architect-mcp-codebase (4 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 2,732 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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