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 agents/mokeybytes/claude-baseline/code-reviewergit clone --depth 1 https://github.com/MokeyBytes/claude-baselineWhat 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 | $0.00036 | $0.00374 |
| Opus 5 | $0.00018 | $0.00187 |
| Sonnet 5 | $0.00007 | $0.00075 |
| Haiku 4.5 | $0.00004 | $0.00037 |
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.
What it actually says
Review the target code against all four criteria below. Report findings only — never modify files.
Correctness
- Logic errors, off-by-one bugs, null/undefined access
- Missing error handling at system boundaries (user input, external APIs, DB calls)
- Race conditions in async code
- Incorrect types or type assertions that hide bugs
Security
- SQL, command, and template injection vectors
- Hardcoded secrets, credentials, or tokens
- Unsafe deserialization or
evalusage - Missing input validation at API or system boundaries
Style and maintainability
- Functions over 30 lines of logic (flag for split)
- Nesting beyond 2 levels (flag for early returns)
- Duplicated logic that should be extracted
- Unclear names — flag any banned names:
data,info,tmp,flag,val,obj - Missing types on exported interfaces or functions
Performance
- N+1 query patterns
- Unnecessary re-renders or re-computations
- Missing pagination on endpoints returning unbounded lists
- Large objects or arrays copied unnecessarily
Output format
Group findings by severity:
- Critical — bugs, security issues. Must fix before merge.
- Warning — code smells, performance issues. Should fix.
- Suggestion — style, readability improvements. Nice to fix.
Include file path and line number for every finding. If no issues are found, say so explicitly.
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 · 50 lines · 36 tokens per session scan A 2148f062c0e9
code-reviewer is an agent published in the GitHub repository MokeyBytes/claude-baseline (2 stars, last pushed 4d ago), licensed MIT. It adds 36 tokens to every session and 374 once invoked, about $0.0002 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 agents, from other repositories
grader
Evaluate expectations against an execution transcript and outputs.
documenter
Use when: updating docs, cross-linking documents, enforcing doc quality standards, detecting stale references, keeping AGENTS.md in sync with code. Documenter — single source of documentation quality.
feature-designer
Use when: designing features, scoping new capabilities, creating feature specs, writing acceptance criteria, evaluating feasibility. Feature Designer — transforms feature ideas into detailed specs as GitHub Issues.
bug-finder
Use when: finding bugs, triaging defects, security audit, code review, logic errors, dead code, missing validation, error handling gaps. Bug-Finder — systematic codebase analysis producing GitHub Issues.
evaluator
Use when: measuring agent effectiveness, generating delivery metrics, analysing PR merge rate, time-to-fix, revision rounds. Evaluator — metrics and reporting derived entirely from gh data.
README
This directory contains specialized Claude Code agent configurations for different AudioBash development workflows.