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/mattmre/EVOKORE-MCP-PUBLICWrote 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/mattmre/evokore-mcp-public/review-pr)<a href="https://agentmods.dev/commands/mattmre/evokore-mcp-public/review-pr"><img src="https://agentmods.dev/badge/commands/mattmre/evokore-mcp-public/review-pr/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/mattmre/evokore-mcp-public/review-pr"><img src="https://agentmods.dev/badge/commands/mattmre/evokore-mcp-public/review-pr.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 | $0.00013 | $0.00715 |
| Opus 5 | $0.00006 | $0.00358 |
| Sonnet 5 | $0.00003 | $0.00143 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
review-pr 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 11d 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.
This is a copy
100% identical to review-pr — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arguments
$ARGUMENTS: The PR number or URL to review
Your task
Review the specified pull request and provide feedback.
Step 1: Checkout the PR
First, checkout the PR using:
gh pr checkout $ARGUMENTS
Step 2: Gather PR context
Get the PR details:
gh pr view $ARGUMENTS
gh pr diff $ARGUMENTS
Step 3: Review the code changes
Use the Task tool with subagent_type: "code-reviewer" to perform a thorough code review of the changes. Pass the diff and changed files to the agent for analysis.
The code-reviewer agent will analyze:
- Code quality and best practices
- Potential bugs or issues
- Security concerns
- Performance considerations
- Documentation and comments
Step 4: Present the review
After the code review is complete, present the review to the user using this format:
## PR Review
**Recommendation**: APPROVE | REQUEST_CHANGES | COMMENT
### Summary
[1-2 sentence overview of what this PR does]
### Actionable Feedback (N items)
- [ ] `file.py:42` - Description of issue or required change
- [ ] `notebook.ipynb` (in cell with `some_code = ...`) - Description
### Detailed Review
#### Code Quality
[Analysis of code patterns, readability, maintainability]
#### Security
[Any security considerations]
#### Suggestions
[Optional improvements]
#### Positive Notes
[What was done well]
Guidelines:
- Use checkboxes for actionable items so authors can track progress
- For Jupyter notebooks, reference code snippets instead of cell numbers
- Be specific with file:line references where possible
Step 5: Ask about posting the review
Use the AskUserQuestion tool to ask the user:
- Whether they want to post this review to GitHub
- What review action to take: APPROVE, REQUEST_CHANGES, or COMMENT
Step 6: Post the review (if approved)
If the user confirms, post the review using:
gh pr review $ARGUMENTS --body "YOUR_REVIEW_BODY" --approve|--request-changes|--comment
When posting to GitHub, wrap the Detailed Review section in a collapsible <details> tag to reduce noise:
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.
- 11d ago First seen · 120 lines · 13 tokens per session scan A 2e6578f10177
review-pr is a command published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 13 tokens to every session and 715 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to review-pr, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
final-review
Command "final-review" from adriannoes/awesome-agentic-ai, covering final review - comprehensive pr review & testing, step 0: determine review pass, step 1: create or update the pr, step 2: launch three review agents in parallel and agent 1: codebase consistency reviewer.
code-review
Perform a thorough code review that verifies functionality, maintainability, and security before approving a change. Focus on architecture, readability, performance implications, and provide actionable suggestions for improvement.
review-branch
Review an existing branch holistically before merging — blast radius, conventions, security, and spec compliance.
eg-precommit-review
Run the pre-commit independent-reviewer loop on the current branch's pending changes.
pr
Clean up code, stage changes, and prepare a pull request.
review
Perform a comprehensive code review focusing on style, bugs, and security.