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 commands/marmot-protocol/agent-config/code-reviewgit clone --depth 1 https://github.com/marmot-protocol/agent-configWhat 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 | $0.00011 | $0.00635 |
| Opus 5 | $0.00005 | $0.00318 |
| Sonnet 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 yesterday.
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.
- yesterday 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 7mo ago), licensed MIT. It adds 11 tokens to every session and 635 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.