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/penwyp/claudepreference/m-review-completiongit clone --depth 1 https://github.com/penwyp/ClaudePreferenceWrote 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/penwyp/claudepreference/m-review-completion)<a href="https://agentmods.dev/commands/penwyp/claudepreference/m-review-completion"><img src="https://agentmods.dev/badge/commands/penwyp/claudepreference/m-review-completion.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 | $0.00000 | $0.00744 |
| Opus 5 | $0.00000 | $0.00372 |
| Sonnet 5 | $0.00000 | $0.00149 |
| Haiku 4.5 | $0.00000 | $0.00074 |
Grade A, and why
m-review-completion 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 4d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review code against a plan and generate a quality report
Target: $ARGUMENTS (Default: a plan or requirement document, such as .md, .txt, or direct text)
Scope: Analyze code implementation against a plan, verify completion, and identify quality issues.
Execution Steps
-
Plan Analysis and Code Discovery:
- Parse the input plan to extract requirements and technical specifications.
- Discover relevant code files by searching the codebase and analyzing dependencies.
-
Implementation Verification:
- Check if features are implemented according to the plan's acceptance criteria.
- Trace requirements to specific code segments and flag deviations.
-
Code Quality Analysis:
- Detect bugs, including runtime errors, logic flaws, and security vulnerabilities.
- Identify code smells like high complexity, duplication, and poor design patterns.
- Analyze architectural issues such as circular dependencies and layering violations.
-
Enhancement and Reporting:
- Suggest opportunities for improvement, such as using third-party libraries or performance optimizations.
- Generate a structured report summarizing all findings.
- Save the report to
reports/code-review-report-MM-dd-HH-mm-ss.md.
Output Format
The output is a single markdown report.
English Report
summary
- A high-level overview of the review.
- Key metrics for plan completion, code quality, and critical issues.
- A list of critical action items that require immediate attention.
completion_analysis
- A breakdown of completed, partially completed, and missing features from the plan.
- JSON structure for a feature:
{ "feature": "string", "status": "Completed | Partial | Missing", "location": "string", "notes": "string" }
quality_analysis
- A detailed list of bugs, security vulnerabilities, and code smells.
- JSON structure for an issue:
{ "id": "string", "severity": "Critical | High | Medium | Low", "description": "string", "file": "string", "recommendation": "string" }
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.
- 4d ago First seen · 101 lines · 0 tokens per session scan A d6cf645efb19
m-review-completion is a command published in the GitHub repository penwyp/ClaudePreference (137 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 744 tokens. 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.