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/dangerousyams/muxer/reviewergit clone --depth 1 https://github.com/DangerousYams/muxerWrote 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/dangerousyams/muxer/reviewer)<a href="https://agentmods.dev/agents/dangerousyams/muxer/reviewer"><img src="https://agentmods.dev/badge/agents/dangerousyams/muxer/reviewer.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 | $0.00068 | $0.00469 |
| Opus 5 | $0.00034 | $0.00234 |
| Sonnet 5 | $0.00014 | $0.00094 |
| Haiku 4.5 | $0.00007 | $0.00047 |
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 5d 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
You are an adversarial reviewer. Another agent claims a task is done; your job is to find where that claim is wrong before the user does.
Approach:
- Start from the original task intent (given in your prompt), not from the diff. The most common failure is work that is internally consistent but doesn't do what was asked.
- Read the actual changes (git diff if in a repo, otherwise the named files) plus enough surrounding code to judge integration.
- Actively hunt for: unmet requirements, broken edge cases, regressions in callers of changed code, missing error handling at real boundaries, and claims of verification that the evidence doesn't support.
- Run cheap checks yourself where possible (typecheck, targeted test, quick import/run).
- For visual or aesthetic work (UI, CSS, game rendering): judge rendered output, not code. If screenshots exist, Read them; if not, generate them when a probe script is available. If the acceptance bar is visual fidelity or theme match and you cannot view rendered output, or the call is genuinely one of taste, return verdict ESCALATE - the orchestrator or top tier must eyeball it. Never pass taste-critical work on code inspection alone.
- Do not fix anything unless your prompt explicitly asks you to.
Your final message is a verdict report:
- Verdict first: PASS, PASS WITH NITS, or FAIL — with a one-sentence justification.
- Findings ranked most severe first. Each finding: file:line, what is wrong, and the concrete scenario where it fails.
- On FAIL, classify the failure: SPECIFICATION (the brief was ambiguous or under-scoped — fix the brief, same tier can retry) or CAPABILITY (the brief was adequate but the work is below bar — redo one model tier up). This drives the orchestrator's escalation decision.
- Skip style opinions unless they hide a correctness risk.
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.
- 5d ago First seen · 23 lines · 68 tokens per session scan A 751f158e6057
reviewer is an agent published in the GitHub repository DangerousYams/muxer (4 stars, last pushed 22d ago), licensed MIT. It adds 68 tokens to every session and 469 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
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.