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 skills/magic3007/dotfiles/pr-reviewnpx skills add magic3007/dotfiles --skill pr-reviewgit clone --depth 1 https://github.com/magic3007/dotfilesWrote 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/skills/magic3007/dotfiles/pr-review)<a href="https://agentmods.dev/skills/magic3007/dotfiles/pr-review"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/pr-review.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 | $0.00028 | $0.01651 |
| Opus 5 | $0.00014 | $0.00826 |
| Sonnet 5 | $0.00006 | $0.00330 |
| Haiku 4.5 | $0.00003 | $0.00165 |
Grade A, and why
pr-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 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Code Review (Dynamic Agent Allocation)
Intelligent code review for the current branch's Pull Request. Dynamically generates targeted review tasks based on PR changes.
Usage
Invoke with /pr-review or when the task matches.
Arguments:
- No arguments: Review PR for current branch
- PR number: Review specific PR (e.g.,
pr-review 123) --quick: Quick mode, only run Phase 1 analysis
Quick Start
- Get current branch PR:
gh pr view --json number,title,state,isDraft - If PR doesn't exist or is closed, stop and explain
- Execute Phases 1-4 in order
Workflow Overview
Phase 1: Deep PR Analysis
├─ 1.0 PR Status Check
├─ 1.1 Get PR Summary
└─ 1.2-1.4 Change Type Detection
↓
Phase 2: Dynamic Agent Planning
↓
Phase 3: Execute Review Tasks [Parallel]
↓
Phase 4: Confidence Scoring & Summary
Model Configuration
| Mode | CRITICAL/HIGH | MEDIUM | LOW |
|---|---|---|---|
| Default | Opus | Sonnet | Haiku |
Quick (--quick) |
Sonnet | Sonnet | Sonnet |
Economy (--economy) |
Sonnet | Haiku | Haiku |
Phase 1: Deep PR Analysis
1.0 PR Status Check
Check if PR should be reviewed:
- Is it closed? → Stop
- Is it a draft? → Note but continue
- Is it bot-generated? → Skip
1.1 Get PR Summary
Get basic PR info: title, description, modified files, change summary.
1.2 Change Type Detection
Analyze each file change, detecting change types by risk level.
CRITICAL level types: FrameworkA, FrameworkB, FrameworkC, FrameworkD HIGH level types: distributed comm, DTensor, MoE, TP/EP/CP MEDIUM level types: tensor ops, workflow, API, compile LOW level types: tests, docs, config
1.3 Framework-Specific Risk Identification
Based on detected types, identify corresponding risks.
1.4 Output Change Analysis Report
CHANGE_ANALYSIS_REPORT:
- detected_types: [FRAMEWORK_PARALLEL, COMPONENT_A, FRAMEWORK_CORE, ...]
- risk_level: CRITICAL | HIGH | MEDIUM | LOW
- affected_files: [file1.py, file2.py, ...]
- identified_risks: [risk1, risk2, ...]
- related_frameworks: [frameworkA, frameworkB, frameworkC, ...]
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 · 233 lines · 28 tokens per session scan A d0b9782dd7a5
pr-review is a skill published in the GitHub repository magic3007/dotfiles (11 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 1,651 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 skills, from other repositories
git-commit
Create a new git commit for staged changes. Use when the user asks to commit staged changes with an auto-generated Conventional Commits message, or when Claude Code itself wants to run git commit.
inspect-malicious-code
Inspect a project for potentially malicious code (malware, spyware, etc.) using static analysis only. Use when the user asks to audit a project or dependency for malicious or suspicious code.
verify-git-command-location
Verify that the correct git binary is in use, especially when running under WSL with a Windows filesystem. Use the first time a git command is executed in a session.
define-markdown-format
Load and apply Markdown formatting rules for this project. Use when writing or editing Markdown files.
git-verify-identity
Verify that git user.name and user.email are configured. Use automatically before any git operation that requires identity (commits, rebases, cherry-picks, etc.), and when the user asks to check or verify their git identity.
create-oss-skill
Create well-formed Agent Skills following the agentskills.io specification. Scaffold directories, write SKILL.md files, bundle scripts, and structure instructions for progressive disclosure. Use when creating a new skill, reviewing skill structure, optimizing a skill description, or setting up evals for skill quality.