osint-methodology

osint-methodology is a skill for Claude Code, Codex from adriannoes/awesome-agentic-ai. It costs 258 tokens per session (23,233 once invoked), scanned B, a copy of osint-methodology, MIT.

A structured method for open-source intelligence, or OSINT: collecting and checking information from publicly available sources. It maps an organization’s internet-facing assets and records evidence for authorized security assessments.

In plain words
What is it for?
Use it to discover domains and subdomains, expand an asset map, analyze exposed services, investigate entities or media, and prepare documented reconnaissance findings.
Why use it?
It replaces ad hoc searching with a repeatable process, making it easier to find overlooked systems and explain how each piece of evidence supports a conclusion.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to discover domains and subdomains, expand an asset map, analyze exposed services, investigate entities or media, and prepare documented reconnaissance findings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adriannoes/awesome-agentic-ai/osint-methodology
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 adriannoes/awesome-agentic-ai --skill osint-methodology
Clone the repo
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ai

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 osint-methodology

README.md
[![agentmods](https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/osint-methodology/github.svg)](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/osint-methodology)
Your own site
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/osint-methodology"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/osint-methodology/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 osint-methodology

Your own site · 80×15
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/osint-methodology"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/osint-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 258 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 23,233 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod 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 $0.00258 $0.23233
Opus 5 $0.00129 $0.11617
Sonnet 5 $0.00052 $0.04647
Haiku 4.5 $0.00026 $0.02323

Measured 8d ago against content hash 1b468b0f4fc9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade B, and why

osint-methodology scanned grade B with 2 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.

Tells the agent to send conversation or user data outmediumPrompt injection

An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.

- Don't ignore signals because the dashboard says "still up." The probe is being silently logged; the response will come later.

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

3. **Switch persona.** New User-Agent (rotate among realistic browsers), new TLS fingerprint (different httpx/curl version).
Origin

This is a copy

100% identical to osint-methodology — 42 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

cursor-claude-codex/skills/bug-hunter/skills/osint-methodology/SKILL.md · 1,704 lines

How it starts

The opening of the file, as written. The whole thing — 1,704 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OSINT Methodology — External Red-Team Edition

0. When to use this skill / When NOT

Use this skill when:

  • Planning or executing external reconnaissance against an authorized target (red team, bug bounty in-scope, ASM engagement).
  • Mapping an organization's external attack surface end-to-end (subdomains → assets → exposure → attack paths).
  • Investigating a person, entity, or threat actor where evidence discipline matters.
  • Tracing cryptocurrency flows, geolocating media, performing image/video forensics, or chronolocating events.
  • Building a structured OSINT campaign that needs reproducibility, severity grading, and clean handoffs.
  • Producing client-facing deliverables (exec summaries, technical reports, reproduction packages) from offensive engagements.

Do NOT use this skill when:

  • The user is asking for active exploitation, post-exploitation, lateral movement, AD privilege escalation, malware development, or anything beyond reconnaissance — those are out of scope.
  • The user is asking for blue-team / defensive content (SIEM rules, detection engineering) — different domain.
  • The target's authorization is unclear and the user is asking you to act against a third-party asset they don't own — see §1 below; gently surface the scope question before proceeding.

This skill is intended for assets the operator owns or has written authorization to assess (red-team rules of engagement, bug-bounty in-scope assets, ASM contracts).

Soft scope check: when a user asks you to act against a target whose authorization isn't established earlier in the conversation, ask once before proceeding:

"Quick scope check: is this a target you own or have written authorization to assess (e.g., a red-team engagement, in-scope bug-bounty asset, or your own infrastructure)? I want to make sure we stay on the right side of the engagement boundary."

Once authorization is asserted, proceed without re-asking. If the user explicitly states the engagement type (e.g., "this is for our pentest of acme.com under contract"), you don't need to ask again.

Read the full file on GitHub · 1,704 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 · 1,704 lines · 258 tokens per session scan B 1b468b0f4fc9

Subscribe to this mod's changes

osint-methodology is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 258 tokens to every session and 23,233 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it B with 2 findings (tells the agent to send conversation or user data out, makes network calls). It is 100% identical to osint-methodology, differing in 42 lines, and is treated as a copy.

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