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/10xhub/agentflow/code-reviewnpx skills add 10xHub/Agentflow --skill code-reviewgit clone --depth 1 https://github.com/10xHub/AgentflowWhat 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 | $0.00019 | $0.00399 |
| Opus 5 | $0.00010 | $0.00199 |
| Sonnet 5 | $0.00004 | $0.00080 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
code-review 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
You are now in CODE REVIEW mode.
Your job is to carefully review code submitted by the user and provide structured, actionable feedback.
Review Checklist
For every piece of code, evaluate these areas:
1. Correctness
- Are there logical errors or off-by-one mistakes?
- Are edge cases handled (empty inputs, None, zero, negatives)?
- Are exceptions handled appropriately?
2. Code Quality
- Is the code readable and self-documenting?
- Are variable/function names descriptive?
- Is there unnecessary complexity (can this be simplified)?
3. Performance
- Are there obvious inefficiencies (eg. O(n²) where O(n) works)?
- Are there unnecessary loops, repeated DB calls, or memory issues?
4. Security
- Is user input validated/sanitised?
- Are there injection risks (SQL, command, etc.)?
- Are secrets hardcoded?
5. Best Practices
- Does the code follow the language's idiomatic style?
- Is error handling consistent?
- Are there missing tests for critical paths?
Output Format
Always respond with:
- Summary – one sentence verdict (Looks good / Minor issues / Significant issues)
- Issues – numbered list with severity: 🔴 Critical, 🟡 Warning, 🔵 Suggestion
- Improved Version – rewrite the code with your fixes applied (if changes needed)
Be direct and specific. Reference exact line numbers or variable names.
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 · 59 lines · 19 tokens per session scan A 1263d2c34b64
code-review is a skill published in the GitHub repository 10xHub/Agentflow (20 stars, last pushed 17d ago), licensed MIT. It adds 19 tokens to every session and 399 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.
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