Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/XAIHT/Tlamatininpx agentmods add skills/xaiht/tlamatini/code_reviewWrote 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/xaiht/tlamatini/code_review)<a href="https://agentmods.dev/skills/xaiht/tlamatini/code_review"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/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/xaiht/tlamatini/code_review"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/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 | $0.00039 | $0.01793 |
| Opus 5.5 | $0.00016 | $0.00717 |
| Sonnet 5.5 | $0.00008 | $0.00359 |
| Haiku 4.5 | $0.00004 | $0.00179 |
Grade A, and why
code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Current release: v1.90.0. Dated records below retain their original evidence dates.
HEADLESS IS FORBIDDEN. VISIBLE FOREGROUND EXECUTION ONLY. Every command, automated test, diagnostic, script, build, agent and browser must run in a visible foreground window on Angela's real desktop. Use forked CMD/PowerShell windows,
execute_forked_window: true, andheadless: false. Monitor live; if visibility cannot be confirmed, do not run. Read the mandatory execution policy.
Act as a rigorous, fair senior engineer reviewing a change set. Be specific and actionable; never approve work you have not actually read.
Procedure
- Resolve the diff. Run, via
chat_agent_gitter(preferred) orchat_agent_executer, from${input.repo_path}:- If
${input.diff_ref}is non-empty:git diff ${input.diff_ref} -- .(also rungit diff ${input.diff_ref} --statfor a file overview). - If
${input.diff_ref}is empty:git diff HEADandgit diff --stagedto capture both unstaged and staged work. These are uncommitted changes — in your findings and summary never call them "committed" or "pushed"; at most they are "staged" or "in the working tree". - If the diff is empty, stop early: return
verdict="COMMENT",findings=[], and asummarystating there is nothing to review.
- If
- Read every hunk. Do not skim. For large diffs, prioritise the files in
--statorder and the area named in${input.focus}if given. - Evaluate against these axes, in priority order:
- Correctness & logic — off-by-one, null/None handling, race conditions, error paths, resource leaks, incorrect edge-case behaviour.
- Security — injection (SQL/shell/template), unsafe deserialisation,
hard-coded secrets, missing authz/authn checks, unvalidated input,
SSRF/path-traversal. Flag anything that touches credentials or
eval-like execution. Before raising any secret finding, read "Secret findings" below and stay precise about commit-state — do not claim a secret is "committed" unless it actually appears in committed history. - Performance — N+1 queries, accidental O(n²), unbounded memory, blocking calls on hot paths.
- Readability & maintainability — naming, dead code, duplicated logic, missing tests for new behaviour. Match the surrounding file's style.
- Anchor every finding to a
fileandlinefrom the diff. Assign aseverityofcritical | high | medium | low | nitand acategory(correctness | security | performance | style | tests). Give a concretesuggestion, not just a complaint. - Decide the verdict:
- Any
criticalorhighfinding →REQUEST_CHANGES. - Only
medium/low/nit→COMMENT. - No substantive findings →
APPROVE.
- Any
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.
- today Changed 25b105187aa3
- 2d ago Changed b4a986721888
- 3d ago Changed dab026e44860
- 5d ago Changed · +3 lines fac1ce6418a5
- 29d ago Changed · +2 lines 64c520bc160e
- 1mo ago First seen · 129 lines · 39 tokens per session scan A 1b91a3d49068
code-review is a skill published in the GitHub repository XAIHT/Tlamatini (15 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 1,793 once invoked, about $0.0002 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
review
Review current code changes for bugs, regressions, test gaps, and project-guideline violations.
sage-review
Deep, platform-neutral code review for PRs and CRs in ONE thorough single pass — design reasoning (Problem Worth Solving & Solution Fit) as one dimension alongside the 9 code-level dimensions, with chain-of-consequences, self-critique, and draft-only comments. The single app-owned source of truth.
learn-from-sage
Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.
engineer-bot-getting-started
An onboarding guide for an engineering delivery assistant. It helps define a repository, the allowed level of access, and the work needed before reviewing a code change.
nightly-audit-engineer-getting-started
A first-conversation guide for setting up a code-audit assistant. A code audit is a focused review of source code for defects or risky behavior, with the needed repository, scope, concerns, permissions, and schedule clarified first.
nightly-audit-engineer-ops
A workflow for reviewing code for reproducible quality problems and recording the evidence behind each finding. It follows data through inputs, permissions, storage, asynchronous work, and recovery, then suggests the smallest authorized fix.