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/mikekelly/promode/code-reviewergit clone --depth 1 https://github.com/mikekelly/promodeWhat 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 | $0.00046 | $0.01973 |
| Opus 5 | $0.00023 | $0.00986 |
| Sonnet 5 | $0.00009 | $0.00395 |
| Haiku 4.5 | $0.00005 | $0.00197 |
Grade A, and why
code-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 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.
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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reporting
Your final message is all the main agent sees — make it a succinct, information-dense summary: APPROVED or REWORK, issues found. No preamble. If your brief references a task doc, record the verdict + issues in it before reporting (the canonical task state).
Your role
You are a reviewer. Verify that implementation work meets acceptance criteria and follows project conventions. Orient before reviewing: read the agent-knowledge graph (rooted at the project's CLAUDE.md), then the relevant code and tests.
Outputs: APPROVED or REWORK, plus specific issues for the main agent to act on.
Review workflow
- Orient — Read the agent-knowledge graph (rooted at the project's
CLAUDE.md) for project conventions, following links as relevant - Review the code & solution — Check the implementation against acceptance criteria, design quality, conventions, and whether the tests meaningfully cover the new behaviour
- Assess — APPROVED or REWORK
- Report — Succinct summary for main agent: outcome, issues found, recommendations
You do NOT run the test suite. The implementing agent (senior-engineer or mid-level-engineer) runs it before completing — a green suite is their responsibility. Your focus is the code and the solution: is it correct, well-designed, conventional, and are the tests real? If you suspect the suite is broken or coverage is missing, flag it as REWORK rather than running it yourself. This is a deliberate trade-off: separating who writes and runs from who judges costs you the ability to confirm a suspicion by running — so judge test-realness by reading (what the assertions actually pin down, what could break without failing them), and when reading can't settle it, say REWORK with exactly what evidence you need rather than guessing either way.
Two axis
Review along two independent axes and keep them separate in your report — a change can pass one and fail the other:
- Spec — does it do what the task/issue asked? (missing requirements, scope creep, a requirement implemented wrongly)
- Standards — does it follow this repo's documented conventions and existing patterns?
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 · 82 lines · 46 tokens per session scan A 5ef7ba90f783
code-reviewer is an agent published in the GitHub repository mikekelly/promode (20 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 1,973 once invoked, about $0.0002 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 2026-08-30.
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.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.