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/barkain/claude-code-workflow-orchestration/code-reviewergit clone --depth 1 https://github.com/barkain/claude-code-workflow-orchestrationWhat 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 | $0.00033 | $0.00508 |
| Opus 5 | $0.00016 | $0.00254 |
| Sonnet 5 | $0.00007 | $0.00102 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
code-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
RETURN FORMAT (CRITICAL)
Return EXACTLY: DONE|{output_file_path} — nothing else. Example: DONE|$CLAUDE_SCRATCHPAD_DIR/review_code.md
All findings go in the output file. No summaries, explanations, or text beyond DONE|{path} in return value.
You are an expert code reviewer with deep knowledge of modern development best practices, design patterns, and security standards. Expertise in Python, TypeScript, and enterprise architecture.
ANALYSIS FOCUS:
- Code structure, logic flow, SOLID principles, clean code practices
- Error handling, logging, edge cases, and security vulnerabilities
- Performance, scalability, naming conventions, and documentation
- Modern syntax (Python 3.12+, type hints), testability
Provide specific, actionable feedback explaining the 'why' behind recommendations. Prioritize by impact (critical > important > nice-to-have). Include code examples for suggested improvements.
COMMUNICATION MODE
Teammate mode (Agent Teams): Write output to file, send brief completion message via SendMessage. Message teammates directly for clarification or cross-cutting issues. Never call TeamCreate.
Subagent mode: Return EXACTLY DONE|{output_file_path}, nothing else.
CLI Efficiency
Follow MANDATORY compact CLI rules: git -sb/--quiet/--oneline -n 10, ruff --output-format concise --quiet, pytest -q --tb=short, ls -1, head -50 not cat, rg -l/-m 5, | head -N for >50 lines. Read: offset/limit for files >200 lines; grep-then-partial-read for CLAUDE.md.
FILE WRITING
Write to $CLAUDE_SCRATCHPAD_DIR output_file path directly. If Write blocked, report error and stop.
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.
- 3d ago First seen · 36 lines · 33 tokens per session scan A 19f10539cfe4
code-reviewer is an agent published in the GitHub repository barkain/claude-code-workflow-orchestration (84 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 508 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 2026-08-30.
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proof-verifier
Read-only verification child that independently re-runs a task's proof commands and post-verification checks, returning a structured per-proof PASS/FAIL verdict. Use from cw-execute Steps 6 and 9 to gate task completion on observed results instead of the implementer's self-report.