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 mtarcure/claude-vibe-squad --skill review-severity-laddergit clone --depth 1 https://github.com/mtarcure/claude-vibe-squadWrote 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/mtarcure/claude-vibe-squad/review-severity-ladder)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/review-severity-ladder"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/review-severity-ladder/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/mtarcure/claude-vibe-squad/review-severity-ladder"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/review-severity-ladder.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 | $0.00061 | $0.00495 |
| Opus 5.5 | $0.00024 | $0.00198 |
| Sonnet 5.5 | $0.00012 | $0.00099 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
review-severity-ladder 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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Severity Ladder
Rank findings on one shared ladder so severity means the same thing across reviewers, specialists, and models.
Steps
- Place each finding on the ladder by consequence, not by effort to fix and not by how interesting it is:
- critical — data loss, credential exposure, unauthorized state change, or silent corruption reachable on a normal path.
- high — wrong results, broken invariant, or availability loss on a reachable path; correct behavior depends on luck.
- medium — wrong behavior on an edge, boundary, or error path; degraded but recoverable.
- low — quality, clarity, or duplication that will cause a future defect but causes none now.
- note — observation with no defect claim.
- Justify each placement with the consequence and the path that reaches it. "Could be a problem" is not a placement.
- Apply the reachability test before assigning critical or high: name the entry point, the actor, and the input that gets there. Unreachable code caps at medium.
- Apply the intrinsic-impact test: a finding that only reveals information already disclosed, or that requires privileges the attacker would not have, does not clear high.
- Separate severity from confidence. A high-severity finding you are unsure of is
high / unconfirmed— never demote severity to express doubt, and never inflate confidence to justify severity. - Set a severity floor for the deliverable and report everything at or above it; list the rest as notes rather than discarding them.
- When the ladder produces repeated critical findings on the same mechanism, the correct move is to remove or bound the mechanism, not to keep escalating the threat model.
Acceptance
- Every finding carries exactly one rung and a one-line consequence statement.
- Critical and high findings name entry point, actor, and reaching input.
- Severity and confidence are recorded as separate fields.
- The deliverable states its severity floor.
- Placements are defensible without reference to how hard the fix is.
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 · 31 lines · 61 tokens per session scan A f3c205d1f26f
review-severity-ladder is a skill published in the GitHub repository mtarcure/claude-vibe-squad (165 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 495 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 2026-09-19.
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