exposure-risk-quantification

exposure-risk-quantification is a skill for Claude Code, Codex from elementalsouls/Claude-OSINT. It costs 235 tokens per session (10,962 once invoked), scanned A, original, MIT.

A risk-scoring method for turning completed security-recon findings into a 0–100 score, an A–F grade, and an estimated financial-loss range. It also ranks attack paths and prepares a short briefing for company leaders.

In plain words
What is it for?
Use it after a security-recon exercise to score findings, estimate possible losses, rank chains of related weaknesses, and create a board or executive one-page summary.
Why use it?
It helps teams explain which findings matter most and why a risk grade was assigned. Ownership and proof checks prevent uncertain or weakly linked findings from inflating the result.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it after a security-recon exercise to score findings, estimate possible losses, rank chains of related weaknesses, and create a board or executive one-page summary.

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Install with agentmods
npx agentmods add skills/elementalsouls/claude-osint/exposure-risk-quantification
About the project

Claude-OSINT is a library of structured skills that guide Claude through external reconnaissance, including methodology, attack-surface analysis, security checks, risk assessment, and related investigative techniques. It is intended for authorized red-team and bug-bounty engagements. The catalogue entries are the project's own Claude skills for these reconnaissance workflows.

elementalsouls/Claude-OSINT · 2,568 stars · on GitHub

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 elementalsouls/Claude-OSINT --skill exposure-risk-quantification
Clone the repo
git clone --depth 1 https://github.com/elementalsouls/Claude-OSINT

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 exposure-risk-quantification

README.md
[![agentmods](https://agentmods.dev/badge/skills/elementalsouls/claude-osint/exposure-risk-quantification.svg)](https://agentmods.dev/skills/elementalsouls/claude-osint/exposure-risk-quantification)
Your own site
<a href="https://agentmods.dev/skills/elementalsouls/claude-osint/exposure-risk-quantification"><img src="https://agentmods.dev/badge/skills/elementalsouls/claude-osint/exposure-risk-quantification.svg" alt="Measured on agentmods" height="20"></a>
Per session 235 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,962 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00235 $0.10962
Opus 5 $0.00118 $0.05481
Sonnet 5 $0.00047 $0.02192
Haiku 4.5 $0.00023 $0.01096

Measured 8d ago against content hash b8084039a5fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

exposure-risk-quantification 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 8d 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.

skills/exposure-risk-quantification/SKILL.md · 751 lines

How it starts

The opening of the file, as written. The whole thing — 751 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Exposure Risk Quantification — FAIR Scoring, $-Loss, and the Board Deliverable

Companion skill: osint-methodology (the "how to think" recon skill — see its §9 severity rubric and §16 client deliverable templates). This skill is the "how to quantify and present" layer on top of a finished recon pass: it takes findings the methodology skill's pipeline already produced and turns them into a number a board will act on.

0. When to Use / When NOT

Use this skill when: you have a completed set of recon findings (from any engagement, not just one tool's output) and need to (a) compute a defensible 0–100 + A–F risk score, (b) estimate a $-denominated loss range, (c) rank attack-path chains by exploitability, or (d) assemble a board/exec one-pager. Also use it to explain a score — "why did this grade come out D and not F" is exactly what §7 is for.

Do NOT use this skill when: you still need to go collect findings — that's osint-methodology (methodology) / offensive-osint (arsenal). This skill does not probe anything; it has nothing to say until a recon pass has already produced findings, assets, and (ideally) ownership/proof annotations.


1. Posture: Passive Analysis, Not New Recon

Every computation in this skill is a pure function over findings + assets you already hold — no network calls, no new probes, no target traffic. The reference implementation (reporting/{risk_score,loss_model,board_report,board_render,attack_paths, attack_graph,owner_confidence,proof}.py) is explicit about this: risk_score.py docstring calls itself "Pure compute over scan.db — no network, no schema change"; loss_model.py calls itself "Pure, no network"; attack_graph.py calls itself "Pure + offline."

That means this skill inherits the authorization posture of whatever collected the inputs (see osint-methodology §1) but adds none of its own — quantifying findings you already lawfully hold is never itself an intrusive act. It also means the outputs are only as good as the inputs: garbage findings (unowned namesakes, unverified snippet matches) produce a garbage score unless you apply the demotion cap in §7.5 first.

Read the full file on GitHub · 751 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 8d ago First seen · 751 lines · 235 tokens per session scan A b8084039a5fe

Subscribe to this mod's changes

exposure-risk-quantification is a skill published in the GitHub repository elementalsouls/Claude-OSINT (2,568 stars, last pushed 8d ago), licensed MIT. It adds 235 tokens to every session and 10,962 once invoked, about $0.0012 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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