Agentic Plugin Marketplace is a collection of reusable plugins, agents, skills, commands, and rules for coding-agent tools including Claude Code, Codex CLI, Cursor, OpenCode, Antigravity CLI, and GitHub Copilot. It is for developers assembling agentic workflows across multiple harnesses from shared Markdown sources, and the catalogue entries are examples or subsets of those workflow components.
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 wshobson/agents --skill code-review-excellencegit clone --depth 1 https://github.com/wshobson/agentsWrote 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/wshobson/agents/code-review-excellence)<a href="https://agentmods.dev/skills/wshobson/agents/code-review-excellence"><img src="https://agentmods.dev/badge/skills/wshobson/agents/code-review-excellence/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/wshobson/agents/code-review-excellence"><img src="https://agentmods.dev/badge/skills/wshobson/agents/code-review-excellence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00042 | $0.01580 |
| Opus 5.5 | $0.00017 | $0.00632 |
| Sonnet 5.5 | $0.00008 | $0.00316 |
| Haiku 4.5 | $0.00004 | $0.00158 |
Grade A, and why
code-review-excellence 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- code-review-excellence — 100% identical, 267 lines differ
- code-review-excellence — 100% identical, 267 lines differ
- code-review-excellence — 100% identical, 267 lines differ
- code-review-excellence — 91% identical, 269 lines differ
- code-review-excellence — 91% identical, 324 lines differ
- code-review-excellence — 89% identical, 276 lines differ
- code-review-excellence — 89% identical, 281 lines differ
- code-review-excellence — 88% identical, 290 lines differ
How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Excellence
Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.
When to Use This Skill
- Reviewing pull requests and code changes
- Establishing code review standards for teams
- Mentoring junior developers through reviews
- Conducting architecture reviews
- Creating review checklists and guidelines
- Improving team collaboration
- Reducing code review cycle time
- Maintaining code quality standards
Core Principles
1. The Review Mindset
Goals of Code Review:
- Catch bugs and edge cases
- Ensure code maintainability
- Share knowledge across team
- Enforce coding standards
- Improve design and architecture
- Build team culture
Not the Goals:
- Show off knowledge
- Nitpick formatting (use linters)
- Block progress unnecessarily
- Rewrite to your preference
2. Effective Feedback
Good Feedback is:
- Specific and actionable
- Educational, not judgmental
- Focused on the code, not the person
- Balanced (praise good work too)
- Prioritized (critical vs nice-to-have)
❌ Bad: "This is wrong."
✅ Good: "This could cause a race condition when multiple users
access simultaneously. Consider using a mutex here."
❌ Bad: "Why didn't you use X pattern?"
✅ Good: "Have you considered the Repository pattern? It would
make this easier to test. Here's an example: [link]"
❌ Bad: "Rename this variable."
✅ Good: "[nit] Consider `userCount` instead of `uc` for
clarity. Not blocking if you prefer to keep it."
3. Review Scope
What to Review:
- Logic correctness and edge cases
- Security vulnerabilities
- Performance implications
- Test coverage and quality
- Error handling
- Documentation and comments
- API design and naming
- Architectural fit
What Not to Review Manually:
- Code formatting (use Prettier, Black, etc.)
- Import organization
- Linting violations
- Simple typos
Review Process
Phase 1: Context Gathering (2-3 minutes)
What ships with it
2 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.
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 Changed · -263 lines 1e264e04e7d2
- 1mo ago First seen · 530 lines · 42 tokens per session scan A 014fff1c8db7
code-review-excellence is a skill published in the GitHub repository wshobson/agents (41,158 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 1,580 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
blast-radius
Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', reviewing a small diff you don't trust, or a brief that asserts something about existing code…
thermo-nuclear-review
Comprehensive security and correctness audit of a branch's changes. Use for thermo nuclear, thermonuclear, or deep review requests, or branch/PR diff audits focused on bugs, breaking changes, security issues, devex regressions, and feature-gate leaks.
no-comments
Spawn the comment-sicko subagent, fix accepted findings, and offer encodings for claimed constraints.
tech-debt-audit
Thorough, file-cited technical debt audit across 9 dimensions using AST-grep (tree-sitter), grep, LSP, and language-native tooling. Produces TECHDEBTAUDIT.md with severity, effort estimates, and prioritized fixes. Use when asked for codebase health check, tech debt audit, architecture review, code quality assessment…
chisle-review
Review a diff or file through the Chisle lens: flag over-engineering, speculative abstractions, reinvented stdlib, and verbose code that a lazier approach would shrink. One finding per line, no praise. Use when the user says "chisle review", "review this for bloat", or invokes /chisle-review.
chisle-help
Quick-reference card for Chisle commands. One-shot display, not a persistent mode. Use when the user says "chisle help", "how do I use chisle", "what chisle commands", or invokes /chisle-help.