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 cruxible-ai/cruxible --skill review-stategit clone --depth 1 https://github.com/cruxible-ai/cruxibleWrote 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/cruxible-ai/cruxible/review-state)<a href="https://agentmods.dev/skills/cruxible-ai/cruxible/review-state"><img src="https://agentmods.dev/badge/skills/cruxible-ai/cruxible/review-state/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/cruxible-ai/cruxible/review-state"><img src="https://agentmods.dev/badge/skills/cruxible-ai/cruxible/review-state.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.01650 |
| Opus 5 | $0.00017 | $0.00825 |
| Sonnet 5 | $0.00007 | $0.00330 |
| Haiku 4.5 | $0.00003 | $0.00165 |
Grade A, and why
review-state 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 12d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review State
Use this skill on an existing state — the user's Crux — when the goal is diagnosis, quality review, or prioritizing follow-up work.
This skill is for:
- finding structural or behavioral problems
- inspecting representative queries and receipts
- reviewing governed groups and their outputs
- spotting repeated feedback or outcome patterns
- telling the user what is wrong and what should be fixed next
This skill is not the main build flow. By default, review first and surface findings before changing config, workflows, or graph state.
Phase 1: Establish review scope
Start by identifying:
- what the user is worried about
- whether the review is about:
- graph quality
- governed relationships
- query quality
- feedback or outcome patterns
- overall state health
- whether the state is a root state or an overlay
- which user-facing queries or downstream decisions matter most
- whether source files, prepared files, or
prepare-dataoutputs are available for a source-to-state audit
Then inspect the current state:
cruxible state status
cruxible stats
cruxible evaluate
If the state is an overlay and the review is about overall health, local fit quality, or handoff readiness, include upstream pull compatibility in scope by default.
Phase 2: Inspect the most important surfaces
Work from the surfaces that actually affect users.
Workflows, providers, and implementation
If findings may come from how the state is built rather than only from final query outputs, inspect the implementation surfaces too:
- canonical
workflows - proposal
workflows providersintegrations- relevant config sections and provider code
Use this to answer:
- is the bad behavior caused by graph shape, or by how workflows and providers are wired?
- are canonical
workflowswriting only deterministic, trusted results? - are proposal
workflowsproducing the right candidates, signals, and groups? - is the likely fix in config design, provider code, or workflow implementation?
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.
- 12d ago First seen · 226 lines · 34 tokens per session scan A 76698f78bbb4
review-state is a skill published in the GitHub repository cruxible-ai/cruxible (17 stars, last pushed today), licensed Apache-2.0. It adds 34 tokens to every session and 1,650 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 2026-08-30.
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