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/dgalarza/claude-code-workflows/parallel-code-reviewnpx skills add dgalarza/claude-code-workflows --skill parallel-code-reviewgit clone --depth 1 https://github.com/dgalarza/claude-code-workflowsWhat 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 | $0.00062 | $0.01617 |
| Opus 5 | $0.00031 | $0.00809 |
| Sonnet 5 | $0.00012 | $0.00323 |
| Haiku 4.5 | $0.00006 | $0.00162 |
Grade A, and why
parallel-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 2d 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Code Review
This skill provides guidance for launching multiple specialized code review agents in parallel for comprehensive, efficient analysis from different perspectives.
Purpose
Parallel code reviews maximize efficiency and coverage by running multiple specialized reviewers simultaneously. Instead of sequential reviews that take time proportional to the number of reviewers, parallel execution completes in the time of the slowest reviewer while providing comprehensive feedback from all perspectives.
When to Use This Skill
Use this skill when:
- Performing comprehensive code review before merging
- Need multiple specialized perspectives (security, architecture, performance)
- Want faster review by parallelizing analysis
- Reviewing large changesets that benefit from division of labor
- Implementing continuous review practices
Benefits of Parallel Reviews
Speed: 2+ specialized reviews complete in the time of 1 Depth: Each agent focuses on specific expertise area Comprehensive Coverage: Security + Architecture + Performance simultaneously
Core Workflow
Phase 1: Prepare for Review
1. Check Decision Log (Prevent Redundancy)
# Search memory for previous code review decisions
mcp__memory__search_nodes query:"code_review_decision"
# Read decision log file
cat code_review_decisions.md
Decision log format:
# Code Review Decisions
## 2025-01-15: Result Pattern Required
**Decision**: All service objects must return Result objects
**Rationale**: Explicit success/failure handling improves error management
**Status**: Accepted standard pattern
2. Get Code Changes
# Get diff for review
git diff main...HEAD
# Or specific branch
git diff main...feature-branch
Phase 2: Launch Parallel Reviewers
Use Task tool to launch multiple agents concurrently:
Example: Launch 2 reviewers in parallel by sending a SINGLE message with MULTIPLE Task tool calls:
Task({
subagent_type: "cybersecurity-expert",
description: "Security review of changes",
prompt: "Review git diff for security vulnerabilities..."
})
Task({
subagent_type: "rails-backend-expert",
description: "Architecture review of changes",
prompt: "Review git diff for code quality..."
})
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.
- 2d ago First seen · 283 lines · 62 tokens per session scan A 868de3eecc86
parallel-code-review is a skill published in the GitHub repository dgalarza/claude-code-workflows (59 stars, last pushed 5d ago), licensed MIT. It adds 62 tokens to every session and 1,617 once invoked, about $0.0003 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 2026-08-30.
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