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/helderberto/agent-skills/code-reviewnpx skills add helderberto/agent-skills --skill code-reviewgit clone --depth 1 https://github.com/helderberto/agent-skillsWhat 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 | $0.00053 | $0.00936 |
| Opus 5 | $0.00026 | $0.00468 |
| Sonnet 5 | $0.00011 | $0.00187 |
| Haiku 4.5 | $0.00005 | $0.00094 |
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 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Pull Request
Mode: $ARGUMENTS
If mode is one of the following, adjust the review:
- BUGS: Focus only on logical or other bugs
- SECURITY: Focus only on security issues
- PERFORMANCE: Focus only on performance issues
Approval standard
Approve when the change definitely improves overall code health, even if it isn't perfect. Perfect code doesn't exist — don't block a change because it isn't how you would have written it. If it improves the codebase and follows its conventions, approve.
If the change is too large to review well (~1000+ lines), asking the author to split it is a valid review outcome. Suggest a strategy: stack (small change, next one based on it), by file group, horizontal (shared code first, then consumers), or vertical (one end-to-end slice per PR).
Workflow
- Analyze the diff and pre-loaded PR context
- Read changed files to understand full context
- Review based on mode (or all categories if no mode set)
- Provide structured feedback
Review criteria
Apply all axes (or narrow to the mode above):
- Correctness: Logic bugs, off-by-ones, race conditions, unhandled states, missing error paths
- Readability: Functions <50 lines, nesting <3 levels, no dead code/unused imports
- Security: No exposed secrets, no
any, no unvalidated external data - Immutability: No push/pop/splice/direct mutation
- Patterns: Consistent with codebase conventions, no reinvented wheels
- Performance: Unnecessary re-renders, O(n²) where O(n) works, missing memoization
- Code smells: match the diff against the baseline in smells.md — always judgement calls, and the repo's documented style overrides the baseline
Output format
Group by severity:
- Critical - must fix before merge (bugs, security vulnerabilities)
- Suggestions - improvements worth considering
- Nit - minor and optional; the author may ignore (formatting, naming taste)
- FYI - informational only, no action needed
- Positives - good patterns to call out
What ships with it
1 file 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.
- 2d ago First seen · 84 lines · 53 tokens per session scan A b202fc4a8283
code-review is a skill published in the GitHub repository helderberto/agent-skills (13 stars, last pushed 12d ago), licensed MIT. It adds 53 tokens to every session and 936 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.
Other skills, from other repositories
code-review-and-quality
Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
constraint-driven-development
Establishes a project's quality bar as a written contract and stops agents quietly lowering it. Interviews the user on which dimensions matter, supplies sane default thresholds when they have no number in mind, records everything in CONSTRAINTS.md, and watches the diff for a weakened bar — new @ts-ignore or…
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
api-and-interface-design
Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or establishing boundaries between frontend and backend.
code-simplification
Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now…