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 RiggdAI/uniqent --skill code-reviewgit clone --depth 1 https://github.com/RiggdAI/uniqentWrote 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/riggdai/uniqent/code-review)<a href="https://agentmods.dev/skills/riggdai/uniqent/code-review"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/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/riggdai/uniqent/code-review"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/code-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00011 | $0.00040 |
| Opus 5.5 | $0.00004 | $0.00016 |
| Sonnet 5.5 | $0.00002 | $0.00008 |
| Haiku 4.5 | $0.00001 | $0.00004 |
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.
What it actually says
Code Review
Read the diff; flag correctness bugs first, then unclear naming and missing tests. Cite lines.
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 · 9 lines · 11 tokens per session scan A cae33b816514
code-review is a skill published in the GitHub repository RiggdAI/uniqent (15 stars, last pushed 8d ago), licensed Apache-2.0. It adds 11 tokens to every session and 40 once invoked, about $0.0000 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.
Other skills, from other repositories
requesting-code-review
Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.
github-code-review
Review code changes by analyzing git diffs, leaving inline comments on PRs, and performing thorough pre-push review. Works with gh CLI or falls back to git + GitHub REST API via curl.
pragma-code-review
A code-review guide written in Chinese. It reviews code across correctness, architectural boundaries, risk, and verification.
code-quality
Apply when writing or refactoring code. Generic rules to prevent the most common review comments — function length, naming, error handling, security, and tooling.
review-deployment
Apply when reviewing code or planning a deployment. Pre-merge checks, migration ordering, rollback strategy, post-deploy verification.
honcho
Configure and use Honcho memory with Zorro -- cross-session user modeling, multi-profile peer isolation, observation config, and dialectic reasoning. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation and recall settings.