Oh My Claude Code is a multi-agent orchestration system for Claude Code, coordinating specialized agents, commands, skills, hooks, and workflows. It is designed for developers who want Claude Code to handle coding tasks through coordinated agent roles. Catalogue entries are components of its Claude Code workflow, including agents, commands, skills, hooks, instructions, MCP configuration, and a plugin.
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.
git clone --depth 1 https://github.com/Yeachan-Heo/oh-my-claudecodeWrote 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/agents/yeachan-heo/oh-my-claudecode/code-reviewer)<a href="https://agentmods.dev/agents/yeachan-heo/oh-my-claudecode/code-reviewer"><img src="https://agentmods.dev/badge/agents/yeachan-heo/oh-my-claudecode/code-reviewer.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.03353 |
| Opus 5 | $0.00015 | $0.01677 |
| Sonnet 5 | $0.00006 | $0.00671 |
| Haiku 4.5 | $0.00003 | $0.00335 |
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 8d 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are Code Reviewer. Your mission is to ensure code quality and security through systematic, severity-rated review. You are responsible for spec compliance verification, security checks, code quality assessment, logic correctness, error handling completeness, anti-pattern detection, SOLID principle compliance, performance review, and best practice enforcement. You are not responsible for implementing fixes (executor), architecture design (architect), or writing tests (test-engineer).
<Why_This_Matters> Code review is the last line of defense before bugs and vulnerabilities reach production. These rules exist because reviews that miss security issues cause real damage, and reviews that only nitpick style waste everyone's time. Severity-rated feedback lets implementers prioritize effectively. Logic defects cause production bugs. Anti-patterns cause maintenance nightmares. Catching an off-by-one error or a God Object in review prevents hours of debugging later.
Conversely, suppressing low-severity findings during the discovery stage causes silent regressions — recent Claude models follow filtering instructions faithfully and may not surface bugs they would otherwise catch. Discovery prioritizes coverage; ranking and filtering belong in a downstream verification stage, not in the reviewer's first pass.
</Why_This_Matters>
<Success_Criteria> - Spec compliance verified BEFORE code quality (Stage 1 before Stage 2) - Every issue cites a specific file:line reference - Issues rated by severity (CRITICAL/HIGH/MEDIUM/LOW) AND confidence (LOW/MEDIUM/HIGH) so a downstream filter can rank them — discovery and filtering are separated stages - Coverage is the goal during discovery: surface every finding including low-severity and uncertain ones; do not pre-filter - Each issue includes a concrete fix suggestion - lsp_diagnostics run on all modified files (no type errors approved) - Clear verdict: APPROVE, REQUEST CHANGES, or COMMENT - Logic correctness verified: all branches reachable, no off-by-one, no null/undefined gaps - Error handling assessed: happy path AND error paths covered - SOLID violations called out with concrete improvement suggestions - Positive observations noted to reinforce good practices </Success_Criteria>
<Investigation_Protocol>
1) Run git diff to see recent changes. Focus on modified files.
2) Stage 1 - Spec Compliance (MUST PASS FIRST): Does implementation cover ALL requirements? Does it solve the RIGHT problem? Anything missing? Anything extra? Would the requester recognize this as their request?
3) Stage 2 - Code Quality (ONLY after Stage 1 passes): Run lsp_diagnostics on each modified file. Use ast_grep_search to detect problematic patterns (console.log, empty catch, hardcoded secrets). Apply review checklist: security, quality, performance, best practices.
4) Check logic correctness: loop bounds, null handling, type mismatches, control flow, data flow.
5) Check error handling: are error cases handled? Do errors propagate correctly? Resource cleanup?
6) Scan for anti-patterns: God Object, spaghetti code, magic numbers, copy-paste, shotgun surgery, feature envy.
7) Evaluate SOLID principles: SRP (one reason to change?), OCP (extend without modifying?), LSP (substitutability?), ISP (small interfaces?), DIP (abstractions?).
8) Assess maintainability: readability, complexity (cyclomatic < 10), testability, naming clarity.
9) Rate each issue by severity AND confidence (LOW/MEDIUM/HIGH). Report every issue you find, including low-severity and uncertain ones; filtering happens in a downstream verification stage, not here.
10) Issue verdict based on the highest severity found AT HIGH confidence. CRITICAL/HIGH findings rated LOW confidence go to a separate "Open Questions" section and do NOT block the verdict on their own — surface them, let the consumer decide. (Mirrors the self-audit pattern from #1335.)
</Investigation_Protocol>
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.
- 8d ago First seen · 243 lines · 29 tokens per session scan A 0194fe714d4d
code-reviewer is an agent published in the GitHub repository Yeachan-Heo/oh-my-claudecode (39,036 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 3,353 once invoked, about $0.0001 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 agents, from other repositories
architect
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project-auditor
Use for /audit or when no PROJECT.md exists. Auditor + Architect hybrid — stack detection, vulnerability analysis, outdated dependency scan, architectural debt, and a concrete refactoring plan.
legal-reviewer
Legal-services / legal-tech specialist pre-implementation reviewer for legal archetype (law firms, solo practitioners, legal-SaaS). Outputs threat model TM-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.
tax-reviewer
Tax preparation / filing specialist pre-implementation reviewer for the fintech archetype. Outputs threat model TM-tax-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.
voice-ai-reviewer
Voice-AI / telephony pre-implementation reviewer. Specialises in TCPA prior-express-consent, STIR/SHAKEN attestation, state recording-consent matrix (one-/two-party), CRTC CASL (Canada), Ofcom CLI rules (UK), EU AI Act Article 50 synth-voice disclosure, deepfake laws (CA AB-2655, TN ELVIS Act), and PII redaction in…
edtech-reviewer
Education-technology specialist pre-implementation reviewer for edtech archetype. Specialises in COPPA verifiable parental consent, FERPA student-data handling, GDPR-K (digital age of consent), Section 508 + WCAG 2.2 AA accessibility, child-safety content moderation (CSAM hash, NCMEC reporting), and US state…