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/marmot-protocol/agent-configWrote 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/commands/marmot-protocol/agent-config/code-review)<a href="https://agentmods.dev/commands/marmot-protocol/agent-config/code-review"><img src="https://agentmods.dev/badge/commands/marmot-protocol/agent-config/code-review/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/commands/marmot-protocol/agent-config/code-review"><img src="https://agentmods.dev/badge/commands/marmot-protocol/agent-config/code-review.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 | $0.00011 | $0.00635 |
| Opus 5.5 | $0.00004 | $0.00254 |
| Sonnet 5.5 | $0.00002 | $0.00127 |
| Haiku 4.5 | $0.00001 | $0.00064 |
Grade A, and why
code-review 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Perform automated code review on the current pull request.
Process
Step 1: Gate Check
First, invoke @cr-gatekeeper to check if review is needed. If it returns SKIP_REVIEW, stop and explain why.
Step 2: Gather Guidelines
Invoke @cr-guidelines to find all relevant CLAUDE.md and AGENTS.md files. Note the list for the compliance check.
Step 3: Summarize Changes
Invoke @cr-summarizer to get an overview of the PR changes. This provides context for the review agents.
Step 4: Parallel Review
Launch these review tasks in parallel by invoking ALL of them in a SINGLE response (multiple Task tool calls at once):
-
@cr-compliance: Check guideline compliance
- Pass the list of guideline files from Step 2
- Pass the PR title and description for context
-
@cr-bugs: Scan for obvious bugs
- Focus only on the diff
- Pass the PR title and description for context
-
@cr-history: Analyze git history
- Look for context-based issues
- Pass the PR title and description for context
-
@cr-issues: Verify linked issue resolution and Figma designs
- Check if PR actually fixes the issues it claims to close
- If Figma links are present, verify implementation matches the design
- Pass the PR number for issue extraction
IMPORTANT: Call all four Task tools in parallel (same message), do not wait for one to complete before starting the next.
Collect all issues from all agents.
Step 5: Validate Issues
For each issue found with confidence < 90:
- Invoke @cr-validator to independently verify
- Pass the issue details and PR context
- Update confidence based on validation
Step 6: Filter
Filter out any issues with adjusted confidence less than 80. These are likely false positives.
Step 7: Report
If issues remain after filtering:
Format each issue as:
## Code Review Issues
Found {N} issues:
### 1. {Issue Title}
**Type**: {compliance|bug|history|issue-resolution}
**Confidence**: {score}/100
**Location**: {file}:{line}
{Description}
{Code snippet with context}
**Suggestion**: {Fix suggestion if applicable}
---
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 · 97 lines · 11 tokens per session scan A c1a9c41bdc84
code-review is a command published in the GitHub repository marmot-protocol/agent-config (2 stars, last pushed 8mo ago), licensed MIT. It adds 11 tokens to every session and 635 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
refactor
Systematic code refactoring with smart scope detection.
init
Install the formatters this repository needs, with every command visible before it runs.
spec-trace-requirements
You are verifying that implementation changes trace back to specification requirements.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).