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 skills add srtab/daiv --skill code-reviewgit clone --depth 1 https://github.com/srtab/daivWrote 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/skills/srtab/daiv/code-review)<a href="https://agentmods.dev/skills/srtab/daiv/code-review"><img src="https://agentmods.dev/badge/skills/srtab/daiv/code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/srtab/daiv/code-review"><img src="https://agentmods.dev/badge/skills/srtab/daiv/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 35 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00164 | $0.02256 |
| Opus 5.5 | $0.00066 | $0.00902 |
| Sonnet 5.5 | $0.00033 | $0.00451 |
| Haiku 4.5 | $0.00016 | $0.00226 |
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 1mo 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Run a comprehensive review using multiple specialized agents, each focusing on a different aspects.
The final message is the only deliverable. Never create a platform discussion, review, comment, or note yourself. When the request came from a pull-request or merge-request comment, the platform layer posts your final message automatically.
1. Resolve the review context
Use the same workflow for GitHub pull requests and GitLab merge requests.
- Platform review: the request arrived from a PR or MR comment. Use the platform context and read tools to identify the change and previous DAIV reviews. Include the review marker, run number, and footer in the final message.
- Interactive review: any chat or CLI request, including a PR or MR the user merely pointed at, a local change, or a pasted diff. Return the report directly without a marker or footer.
Choose the review scope in this order:
- Use an explicit diff, commit range, branch range, path set, PR, or MR named by the user.
- Treat a pasted diff as the authoritative change when repository refs are unavailable.
- For a platform review, use the PR or MR base and head.
- Otherwise review the current working-tree change, including staged, unstaged, and new files.
Ask when the intended scope is ambiguous. Resolve refs to commit SHAs before using them in shell commands.
2. Find the previous platform review
For a platform review, load the appropriate GitHub or GitLab read tool and list all comments or notes, following pagination. Do not use any write action.
DAIV review comments contain:
<!-- daiv:code-review run=N head=<full-sha> -->
## Code Review #N
A marker counts only when:
- the comment was authored by DAIV's account;
- it matches the marker grammar exactly, with a 40-character hexadecimal head SHA;
- the comment contains at least one review section or a
No findingsresult.
Ignore every other marker. The next run number is the highest valid run plus one. The previous review is the newest valid comment among those with the highest run number. Its marker supplies the previous reviewed head.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 181 lines · 164 tokens per session scan A 7a9aec313152
code-review is a skill published in the GitHub repository srtab/daiv (24 stars, last pushed yesterday), licensed Apache-2.0. It adds 164 tokens to every session and 2,256 once invoked, about $0.0007 per session on Opus 5.5. 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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