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/techgangboss/agentstore/code-reviewernpx skills add techgangboss/agentstore --skill code-reviewergit clone --depth 1 https://github.com/techgangboss/agentstoreWhat 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 | $0.00070 | $0.00563 |
| Opus 5 | $0.00035 | $0.00282 |
| Sonnet 5 | $0.00014 | $0.00113 |
| Haiku 4.5 | $0.00007 | $0.00056 |
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 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer
AI-powered code review that analyzes code for bugs, security vulnerabilities, performance issues, and adherence to best practices.
Capabilities
- Bug detection and logical error analysis
- Security vulnerability scanning (OWASP Top 10)
- Performance issue identification
- Best practice and style suggestions
- Dependency risk assessment
Instructions
When the user asks for a code review:
1. Gather Context
- Identify the files to review (user may specify, or review staged/changed files via
git diff) - Determine the language and framework
- Check for project conventions (linting config,
.editorconfig, etc.)
2. Analyze for Issues
Review each file systematically for:
Bugs & Logic Errors:
- Off-by-one errors, null/undefined access, race conditions
- Incorrect type handling, missing error cases
- Logic that doesn't match stated intent
Security (OWASP Top 10):
- SQL injection, XSS, command injection
- Hardcoded secrets or credentials
- Insecure deserialization, SSRF
- Missing input validation at system boundaries
- Improper authentication/authorization checks
Performance:
- N+1 queries, unnecessary re-renders
- Missing indexes for database queries
- Unbounded loops or memory leaks
- Synchronous operations that should be async
Best Practices:
- Dead code, unused variables/imports
- Missing error handling at API boundaries
- Inconsistent naming conventions
- Functions doing too many things
3. Report Findings
Format the review as:
## Code Review Summary
### Critical Issues (must fix)
- [FILE:LINE] Description of critical bug or security issue
### Warnings (should fix)
- [FILE:LINE] Description of potential problem
### Suggestions (nice to have)
- [FILE:LINE] Style or best practice improvement
### What Looks Good
- Positive observations about well-written code
4. Offer Fixes
For each critical issue and warning, offer to generate a fix. Use the Edit tool to apply fixes the user approves.
Example
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 · 91 lines · 70 tokens per session scan A c74f4b1a675a
code-reviewer is a skill published in the GitHub repository techgangboss/agentstore (4 stars, last pushed 6mo ago), licensed MIT. It adds 70 tokens to every session and 563 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.
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