review-severity-ladder

review-severity-ladder is a skill for Claude Code, Codex from mtarcure/claude-vibe-squad. It costs 61 tokens per session (495 once invoked), scanned A, original, MIT.

A shared scale for rating security findings by their real-world consequence: critical, high, medium, low, or note. It keeps severity separate from confidence and from the effort needed to fix an issue.

In plain words
What is it for?
It is for reviewing and writing security reports consistently. It helps decide the reporting threshold and justify each rating with the entry point, input, reachability, and impact.
Why use it?
Different reviewers may otherwise rate the same problem inconsistently or call an unproven possibility severe. The scale requires a reachable path, an affected actor, and a stated consequence.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit It is for reviewing and writing security reports consistently. It helps decide the reporting threshold and justify each rating with the entry point, input, reachability, and impact.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mtarcure/claude-vibe-squad/review-severity-ladder
Install

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.

Any agent
npx skills add mtarcure/claude-vibe-squad --skill review-severity-ladder
Clone the repo
git clone --depth 1 https://github.com/mtarcure/claude-vibe-squad

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for review-severity-ladder

README.md
[![agentmods](https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/review-severity-ladder/github.svg)](https://agentmods.dev/skills/mtarcure/claude-vibe-squad/review-severity-ladder)
Your own site
<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.

agentmods 80×15 button for review-severity-ladder

Your own site · 80×15
<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>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 495 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash f3c205d1f26f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.agents/skills/review-severity-ladder/SKILL.md · 31 lines

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

  1. 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.
  2. Justify each placement with the consequence and the path that reaches it. "Could be a problem" is not a placement.
  3. 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.
  4. 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.
  5. 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.
  6. Set a severity floor for the deliverable and report everything at or above it; list the rest as notes rather than discarding them.
  7. 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.

Read the full file on GitHub · 31 lines

Changes

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.

  1. 1mo ago First seen · 31 lines · 61 tokens per session scan A f3c205d1f26f

Subscribe to this mod's changes

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.