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/mhashim6/kauri/reviewnpx skills add mhashim6/kauri --skill reviewgit clone --depth 1 https://github.com/mhashim6/kauriWrote 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/mhashim6/kauri/review)<a href="https://agentmods.dev/skills/mhashim6/kauri/review"><img src="https://agentmods.dev/badge/skills/mhashim6/kauri/review.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00545 |
| Opus 5 | $0.00019 | $0.00272 |
| Sonnet 5 | $0.00008 | $0.00109 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
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 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
Review Changes Against Decisions
After implementing changes, verify they are consistent with recorded project decisions. This catches violations before they're committed.
Steps
-
Identify what changed: If
$ARGUMENTSis a file path, read it. If it's a commit range, rungit diff $ARGUMENTS. If empty, rungit diffto see unstaged changes, orgit diff --cachedfor staged changes. -
Find related decisions: For each changed file, query Kauri:
kauri_querywith--file <path>for directly associated decisions.kauri_querywith--textusing keywords from the changed code (function names, module names, concepts).
-
Read the full decisions: Call
kauri_show <id>for every match. Don't skip any — a decision might look irrelevant from the title but contain a constraint in the body. -
Assess consistency: For each relevant decision, determine:
- Consistent: The changes respect the decision. Note this.
- Potential conflict: The changes might violate the decision. Explain specifically what conflicts and why.
- Decision should be updated: The changes are intentional improvements that make the old decision obsolete.
-
Report to the user:
- List each relevant decision and your assessment.
- For conflicts: recommend either adjusting the code or superseding the decision.
- For decisions that should be updated: offer to record a superseding
decision via
/kauri:propose. - For decisions now validated by the changes: offer to run
kauri_validate still_validto refresh their timestamp.
When to use this
- Before
git commiton significant changes - When reviewing a PR or diff
- When the user asks "does this look right?" or "anything I'm missing?"
- After refactoring that touches many files
- When the post-commit staleness hook reports stale decisions
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 · 53 lines · 39 tokens per session scan A f99f7069fd9e
review is a skill published in the GitHub repository mhashim6/kauri (5 stars, last pushed 4mo ago), licensed MIT. It adds 39 tokens to every session and 545 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 2026-08-31.
Other skills, from other repositories
ai-style
当任务是用中文撰写或改写面向读者的文案(产品发布稿、公众号文章、邮件、README 等), 或用户反馈文字「AI 味太重」「不像人写的」时,加载本 Skill。.
triage
你是当前任务的分诊协调者。先识别用户的全部目标、顺序依赖和验收条件,再按 “事实检索 → 计算/执行 → 写作”顺序逐步请求切换到需要的专业能力。不要替专业 能力完成它的工作,也不要在信息缺失时臆造结果。.
writing
将共享历史中的已验证事实和计算结果整理成符合受众、格式与长度约束的成稿。.
data_analysis
基于已确认数据执行可审计的数学计算和描述统计。.
coding
编写并运行 Python 代码,验证脚本逻辑和输出。.
kungfu-agent-onboarding
Use when a user asks to understand, start, inspect, extend, or safely operate installed Kungfu; verify the installed pack, select one intent route, personalize the explanation, and propose one smallest safe next action.