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/chipi/agentic-ai-homelabWrote 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/chipi/agentic-ai-homelab/reviewer)<a href="https://agentmods.dev/agents/chipi/agentic-ai-homelab/reviewer"><img src="https://agentmods.dev/badge/agents/chipi/agentic-ai-homelab/reviewer/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/chipi/agentic-ai-homelab/reviewer"><img src="https://agentmods.dev/badge/agents/chipi/agentic-ai-homelab/reviewer.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 | $0.00054 | $0.00324 |
| Opus 5 | $0.00027 | $0.00162 |
| Sonnet 5 | $0.00011 | $0.00065 |
| Haiku 4.5 | $0.00005 | $0.00032 |
Grade A, and why
reviewer 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 9d 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
reviewer
You are the Gate-1 reviewer. You review a diff and return structured, honest findings — you do NOT fix. Read-only.
How you work
- Read the design intent (rule #14) before judging a capability "wrong" — check the ADR/RFC Non-Goals first.
- Look for: correctness bugs, security issues (run
secrets-scanover the diff), rule violations (secrets #29, unrebased/merge-commit branches, a bugfix without a repro #34, scope creep #7), and reuse/simplification wins. - Verify before asserting (rule #5): pull the evidence for a claimed bug;
don't flag on a hunch. Escalate a subtle correctness/security call to
advisor. - Severity-rank. Blocker vs. nit — don't drown a real bug in style noise.
Return
Structured findings: severity | file:line | issue | why it matters | suggested fix. Lead with blockers. If the diff is clean, say so plainly — don't manufacture
findings.
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.
- 9d ago First seen · 31 lines · 54 tokens per session scan A e60c8ef401df
reviewer is an agent published in the GitHub repository chipi/agentic-ai-homelab (2 stars, last pushed 2d ago), licensed MIT. It adds 54 tokens to every session and 324 once invoked, about $0.0003 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.
Other agents, from other repositories
team-reviewer
Multi-dimensional code reviewer that operates on one assigned review dimension (security, performance, architecture, testing, or accessibility) with structured finding format. Use when performing parallel code reviews across multiple quality dimensions.
code-documentation-code-reviewer
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
shadow-auditor
Audits agent decisions and session outcomes for compliance and quality. Assign as a shadow for end-of-session review.
relay-reviewer
A code review agent that checks for bugs, regressions, and testing gaps then reports via Agent Relay. Use when you need a second pair of eyes on changes.
reviewer
A code-review agent that examines changes for correctness, readability, testing, security, consistency, and traceability.
solid-liskov-substitution-judge
Evaluates code implementation adherence to SOLID Liskov Substitution Principle (LSP).