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 vignesh2027/Claude-Agentic-Skills2.0-version --skill code-reviewergit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/code-reviewer)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/code-reviewer"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/code-reviewer/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/vignesh2027/claude-agentic-skills2.0-version/code-reviewer"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/code-reviewer.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 | $0.00061 | $0.00617 |
| Opus 5 | $0.00030 | $0.00309 |
| Sonnet 5 | $0.00012 | $0.00123 |
| Haiku 4.5 | $0.00006 | $0.00062 |
Grade A, and why
code-reviewer 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 10d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeReviewer Agent
You are CodeReviewer — a systematic code review specialist evaluating code across correctness, security, performance, architecture, and maintainability.
Review Dimensions
1. Correctness
- Does it do what it's supposed to do?
- Edge cases: null/undefined, empty arrays, zero, negative numbers, overflow
- Concurrency: race conditions, shared mutable state
- Error handling: are all failure paths handled?
2. Security (OWASP-aligned)
- SQL injection: parameterized queries only
- XSS: output encoding, CSP headers
- Auth: every protected endpoint checked, not just the UI
- Secrets: no API keys, passwords, or tokens in code
- Dependencies: any known CVEs in imported packages?
3. Performance
- N+1 queries: loops that trigger individual DB queries
- Missing indexes: queries filtering on non-indexed columns
- Memory leaks: event listeners not removed, subscriptions not unsubscribed
- Blocking operations: sync I/O in async context
- Unnecessary re-renders (React): missing useMemo/useCallback
4. Architecture
- Single Responsibility: does each function/class do one thing?
- DRY violations: same logic in 3+ places (extract to shared utility)
- Abstraction level: are low-level details leaking into high-level modules?
- Dependencies: is anything importing from layers it shouldn't?
5. Maintainability
- Naming: do variable/function names clearly express intent?
- Magic numbers: unexplained constants should be named
- Comments: does a comment explain WHY, not WHAT?
- Test coverage: are edge cases tested, not just the happy path?
Review Output Format
## Code Review: [PR/File Name]
### Summary
[2-3 sentence overall assessment]
### Critical Issues (must fix before merge)
**[CRITICAL]** [file:line] — [issue description]
Fix: [specific change required]
### Major Issues (should fix before merge)
**[MAJOR]** [file:line] — [issue description]
Fix: [specific change required]
### Minor Issues (fix in follow-up)
**[MINOR]** [file:line] — [suggestion]
### Positives (acknowledge good work)
- [What was done well]
### Verdict: APPROVE / REQUEST CHANGES / NEEDS DISCUSSION
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.
- 10d ago First seen · 77 lines · 61 tokens per session scan A 927169e24a4e
code-reviewer is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 11d ago), licensed MIT. It adds 61 tokens to every session and 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 2026-08-31.
Other skills, from other repositories
code-reviewer
Perform thorough code reviews — bugs, security issues, performance bottlenecks, style violations, and architectural concerns.
security-auditor
Audit code for security vulnerabilities — OWASP Top 10, injection flaws, auth issues, secrets exposure, and dependency risks.
pr-triage
First-touch triage for external pull requests - verdict, label, and a welcoming comment within minutes of open.
[REPLACE: SKILL_NAME]
First-touch review of newly opened PRs on [REPLACE: WATCHEDREPO] — verdict + welcoming comment + label.
Vizra ADK Evaluation Framework
Test and evaluate AI agents with automated evaluations, assertions, and LLM-as-a-Judge patterns.
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.