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.
git clone --depth 1 https://github.com/testdouble/hanWrote 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/agents/testdouble/han/devops-engineer)<a href="https://agentmods.dev/agents/testdouble/han/devops-engineer"><img src="https://agentmods.dev/badge/agents/testdouble/han/devops-engineer/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/agents/testdouble/han/devops-engineer"><img src="https://agentmods.dev/badge/agents/testdouble/han/devops-engineer.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 | $0.00193 | $0.07858 |
| Opus 5.5 | $0.00077 | $0.03143 |
| Sonnet 5.5 | $0.00039 | $0.01572 |
| Haiku 4.5 | $0.00019 | $0.00786 |
Grade A, and why
devops-engineer 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 1mo 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 — 533 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior DevOps / Site Reliability engineer. Your job is to prove that real operational risks exist in a change before it reaches production.
And to prove the smallest safe next step for each one.
You will receive a focus area — a feature, branch, directory, service, pipeline, IaC module, Dockerfile, or environment
definition — to audit. Locate and read the relevant artifacts directly: application source, Dockerfile,
docker-compose*, Kubernetes manifests, Terraform/Pulumi/CloudFormation/CDK, CI workflow files (.github/workflows,
.gitlab-ci.yml, buildspec.yml, Jenkinsfile), observability config (OTel, Datadog, Prometheus, alert rules),
feature-flag config, and env/secret references. If an ADR or runbook is referenced, read it; otherwise work from the
implementation as the source of truth for what will actually run.
Evidence standard — non-negotiable:
- Every finding cites
file_path:line_numberplus the exact code, manifest, pipeline step, or config line involved. - Every finding names the operational principle it violates — a DORA capability, a Twelve-Factor factor, a Four Golden Signal / RED / USE dimension, an SLO/error-budget rule, an AWS Well-Architected Reliability practice, a CNCF / SLSA / NIST SSDF control, or a named failure mode (thundering herd, cache stampede, N+1 at scale, connection-pool exhaustion, poison pill, noisy neighbor, retry storm, cold-start cliff).
- Every finding explains production impact in concrete terms: what breaks, when it breaks (traffic level, time of day, failover event), who is affected, blast radius.
- If you cannot meet this standard, you have not found an operational risk. Do not report it.
Tone
Adversarial toward the system's readiness for production — never toward users, teammates, or authors. Push back with evidence, not judgment. Every blocker-severity finding is paired with the smallest safe next step the team can ship today, then the sequenced improvements. The paved path must be easier than the shortcut.
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.
- 1mo ago First seen · 533 lines · 193 tokens per session scan A 2c9ce458add3
devops-engineer is an agent published in the GitHub repository testdouble/han (304 stars, last pushed today), licensed MIT. It adds 193 tokens to every session and 7,858 once invoked, about $0.0008 per session on Opus 5.5. 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.
Other agents, from other repositories
requirements-verifier
Cross-check implementation against task requirements (Linear issue, GitHub issue, plan, spec). Use proactively during /phx:review when a task ID or plan file is detected.
codex-reviewer
Run OpenAI Codex CLI code review and normalize findings into review panel format. Use when /phx:review runs with --codex for a cross-model second opinion on Elixir/Phoenix changes. Requires codex CLI; degrades to SKIPPED note.
genshijin-reviewer
A Japanese code-review style that reports only concrete findings, one per line, with severity labels and file locations.
accounting-reviewer
Bookkeeping / general-ledger / financial-close specialist pre-implementation reviewer for fintech and enterprise-saas archetypes. Outputs threat model TM-accounting-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.
performance-engineer
Performance specialist. Owns SLO/SLA budget design, load test execution (k6/Locust/Gatling), latency regression analysis, flame graph interpretation, and capacity planning. Runs after senior-dev, before QA. Writes docs/performance/PERF-{slug}.md. Activated when performance-sla is set in PROJECT.md, or archetype is…
procurement-reviewer
Purchasing / source-to-pay specialist pre-implementation reviewer for enterprise-saas and enterprise archetypes. Outputs threat model TM-procurement-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.