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.
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 elementalsouls/Claude-OSINT --skill exposure-risk-quantificationgit clone --depth 1 https://github.com/elementalsouls/Claude-OSINTWrote 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/elementalsouls/claude-osint/exposure-risk-quantification)<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>- 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 | $0.00235 | $0.10962 |
| Opus 5 | $0.00118 | $0.05481 |
| Sonnet 5 | $0.00047 | $0.02192 |
| Haiku 4.5 | $0.00023 | $0.01096 |
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.
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.
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.
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.
- 8d ago First seen · 751 lines · 235 tokens per session scan A b8084039a5fe
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.
Other skills, from other repositories
skill-authoring
Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or…
awareness
AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning…
collect-preserve-specimens
Collect and preserve insect specimens following museum-grade standards including legal compliance, collection methods, humane dispatch, dry pinning, wet preservation, labeling, storage, and curation. Covers permit requirements, protected species regulations, sweep nets, beating trays, pitfall traps, light traps…
aikido
Practice aikido as a defensive martial art emphasizing harmony, redirection, and controlled resolution. Covers centering and grounding, ukemi (safe falling and rolling), core techniques (irimi-nage, shiho-nage, kote-gaeshi, ikkyo), blending and tai sabaki (body movement), randori (multiple attacker practice), weapons…
analyze-tensegrity-system
Analyze a tensegrity system by identifying compression struts and tension cables, classifying type (class 1/2, biological/architectural), computing prestress equilibrium, verifying stability via Maxwell's rigidity criterion, and mapping biological tensegrity (microtubules, actin, intermediate filaments). Use when…
appraise-gemstone
Appraise gemstone value using the four Cs (color, clarity, cut, carat), origin assessment, treatment detection, and market factor analysis. Advisory educational guidance only — not a certified appraisal. Use when understanding factors that determine a gemstone's value, pre-screening stones before a professional…