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 skills add viktorbezdek/skillstack --skill code-reviewgit clone --depth 1 https://github.com/viktorbezdek/skillstackWrote 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/viktorbezdek/skillstack/code-review)<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/code-review"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/code-review/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/skills/viktorbezdek/skillstack/code-review"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/code-review.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 | $0.00142 | $0.01304 |
| Opus 5.5 | $0.00057 | $0.00522 |
| Sonnet 5.5 | $0.00028 | $0.00261 |
| Haiku 4.5 | $0.00014 | $0.00130 |
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 1mo 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comprehensive Code Review Skill
This skill is a curated merge of four complementary code review skills:
- Evidence-Based Code Review: Evidence-based multi-agent swarm review with specialized reviewers
- PR Comment Analysis: Extract, consolidate, and prioritize GitHub PR comments
- Development Workflow Specialist: TDD, debugging, performance optimization, and automated code review
- Consultant: AI-powered deep analysis for complex architectural and security reviews
When to Use
Use this skill when:
- Reviewing PRs systematically with multiple quality dimensions
- Extracting and consolidating feedback from multiple reviewers
- Need evidence-based findings with file:line references
- Assessing security vulnerabilities, performance bottlenecks, or code quality
- Creating action plans from PR comments
- Complex architectural decisions requiring deep analysis
- Audit requirements mandate systematic review (compliance, release gates)
Do NOT use for:
- Simple formatting fixes (use linter/prettier directly)
- Non-code files (documentation, configuration without logic)
- Trivial changes (typo fixes, comment updates)
- Generated code (build artifacts, vendor dependencies)
Core Capabilities
1. Evidence-Based Code Review
Every finding MUST include:
- Code location:
[file:line]with surrounding context (5 lines before/after) - Evidence type: DIRECT, STYLE_RULE, or BEST_PRACTICE
- Severity: CRITICAL, MAJOR, MINOR, or NIT
- Confidence score: 0.0-1.0
- Suggested fix with specific code changes
2. PR Comment Analysis
- Fetches ALL comments from GitHub PR (inline + conversation)
- Groups comments by file path and code section
- Identifies "High Consensus Issues" (2+ reviewers same concern)
- Three-phase analysis: Consolidation, Context Validation, Impact Analysis
3. Multi-Agent Swarm Review
Five specialized review agents working in parallel:
- Security Reviewer: Vulnerabilities, unsafe patterns, secrets
- Performance Analyst: Bottlenecks, optimization opportunities
- Style Reviewer: Code style, best practices, maintainability
- Test Specialist: Test coverage, quality, edge cases
- Documentation Reviewer: Comments, API docs, README updates
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/evals.json 1.2 KB
- evals/trigger-evals.json 2.0 KB
- examples/ai-powered-testing.py 9.1 KB runs code
- examples/console_logging.py 1.0 KB runs code
- examples/element_discovery.py 1.4 KB runs code
- examples/example-1-security-review.md 9.2 KB
- examples/example-2-performance-review.md 16 KB
- examples/example-3-style-review.md 19 KB
- examples/static_html_automation.py 955 B runs code
- modules/ai-debugging.md 23 KB
- modules/automated-code-review.md 41 KB
- modules/performance-optimization.md 35 KB
- modules/README.md 7.3 KB
- modules/smart-refactoring.md 35 KB
- modules/tdd-context7.md 36 KB
- references/advanced-patterns.md 14 KB
- references/analysis-prompt-v2.md 14 KB
- references/analysis-prompt.md 6.6 KB
- references/best-practices.md 17 KB
- references/code-review-skill-summary.md 4.0 KB
- references/COMPLETION-SUMMARY.md 12 KB
- references/example-analysis.md 10.0 KB
- references/examples.md 18 KB
- references/extended-patterns.md 7.8 KB
- references/github-api.md 7.4 KB
- references/glob-patterns.md 4.9 KB
- references/impact-analysis-methodology.md 13 KB
- references/index.md 9.6 KB
- references/optimization.md 11 KB
- references/README.md 11 KB
- references/reference.md 14 KB
- references/review-categories.md 14 KB
- references/USAGE-GUIDE.md 8.0 KB
- references/validation-workflow.md 7.6 KB
- scripts/__init__.py 129 B runs code
- scripts/analyze-pr.sh 4.6 KB runs code
- scripts/config.py 1.3 KB runs code
- scripts/consultant_cli.py 17 KB runs code
- scripts/diagnose-qodo.py 5.3 KB runs code
- scripts/file_handler.py 9.9 KB runs code
- scripts/litellm_client.py 8.6 KB runs code
- scripts/model_selector.py 4.5 KB runs code
- scripts/multi_agent_review.py 21 KB runs code
- scripts/performance_check.py 13 KB runs code
- scripts/pr_comment_filter.py 20 B runs code
- scripts/pr-comment-filter.py 20 KB runs code
- scripts/pr-comment-grabber.py 13 KB runs code
- scripts/pyproject.toml 463 B
- scripts/README.md 6.5 KB
- scripts/response_strategy.py 23 KB runs code
- scripts/review-loop.sh 6.5 KB runs code
- scripts/security_scan.sh 9.0 KB runs code
- scripts/session_manager.py 8.8 KB runs code
- scripts/show-with-status.py 6.7 KB runs code
- scripts/style_audit.py 15 KB runs code
- scripts/with_server.py 7.0 KB runs code
- templates/alfred-integration.md 8.4 KB
- templates/performance-thresholds.json 8.0 KB
- templates/review-checklist.yaml 6.9 KB
- templates/security-rules.json 9.8 KB
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.
- 1mo ago First seen · 140 lines · 142 tokens per session scan A e10f8a252d07
code-review is a skill published in the GitHub repository viktorbezdek/skillstack (14 stars, last pushed 4mo ago), licensed MIT. It adds 142 tokens to every session and 1,304 once invoked, about $0.0006 per session on Opus 5.5. 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.
Other skills, from other repositories
luna
Reviews code for objective correctness, security, and reliability.
max
Cleans up and improves existing code without changing behavior.
andrej-karpathy
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
address-github-comments
Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
007
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.