Agent skill

Osint

Conduct deep OSINT research on individuals. An agent skill from smixs/osint-skill.

MITAuto-check passedSecurity

Install Osint

skills CLI

$ npx skills add smixs/osint-skill --skill osint -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install smixs/osint-skill osint --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/smixs/osint-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/osint .claude/skills/osint && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "osint" agent skill from https://github.com/smixs/osint-skill/tree/main/osint into .claude/skills/osint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osint", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/smixs/osint-skill/tree/main/osint

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add smixs/osint-skill --skill osint -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install smixs/osint-skill osint --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/smixs/osint-skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/osint .agents/skills/osint && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "osint" agent skill from https://github.com/smixs/osint-skill/tree/main/osint into .agents/skills/osint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osint", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add smixs/osint-skill --skill osint -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install smixs/osint-skill osint --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/smixs/osint-skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/osint .cursor/skills/osint && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "osint" agent skill from https://github.com/smixs/osint-skill/tree/main/osint into .cursor/skills/osint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osint", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/smixs/osint-skill.git --path osint

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add smixs/osint-skill --skill osint -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install smixs/osint-skill osint --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/smixs/osint-skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/osint .gemini/skills/osint && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "osint" agent skill from https://github.com/smixs/osint-skill/tree/main/osint into .gemini/skills/osint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osint", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install smixs/osint-skill osint

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add smixs/osint-skill --skill osint -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/smixs/osint-skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/osint .github/skills/osint && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "osint" agent skill from https://github.com/smixs/osint-skill/tree/main/osint into .github/skills/osint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osint", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add smixs/osint-skill --skill osint -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install smixs/osint-skill osint --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/smixs/osint-skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/osint .opencode/skills/osint && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "osint" agent skill from https://github.com/smixs/osint-skill/tree/main/osint into .opencode/skills/osint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osint", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: osint
GitHub stars: 141
Token cost: ~5.5k tokens
SKILL.md length: 2,358 words
Files: 20 (incl. scripts, references, assets)
Skills in repo: 1
Repo updated: Mar 10, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Conduct deep OSINT research on individuals. An agent skill from smixs/osint-skill.

Works in 8 steps: Tooling Self-Check → Seed Collection → 5: Internal Intelligence → …
Research person
SKILL.md covers Phase Router, Environment, Scripts and Research Escalation Flow, plus 6 more sections
Runs Shell and Python scripts from its folder; calls bash and python3; reaches web.archive.org; needs APIFY_API_TOKEN and APIFY_TOKEN

What it does

Osint is an agent skill from smixs/osint-skill. Conduct deep OSINT research on individuals. Build full digital footprint, psychoprofile (MBTI/Big Five), career history, social graph with confidence scores. Recursive self-evaluation until completeness threshold is met. Includes internal intelligence (Telegram history, email, vault contacts) before going external. Use when: "osint", "досье", "research person", "find everything about", "пробей", "разведка", "due diligence", "background check", "digital footprint", "найди всё про", "собери информацию", "кто это"…

Its SKILL.md is about 5.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 22 other files, including scripts, reference files and assets (for example `assets/dossier-template.md`, `references/content-extraction.md` and `references/platforms.md`).

It sits in Security, covering OSINT, Web scraping and Fundraising and pitch decks. It works with Telegram, Apify, Perplexity and Instagram. The repository describes itself as: OSINT Skill for AI agents (Claude Code, OpenClaw, Codex, OpenCode) — from a name to a scored dossier with psychoprofile, career map, and confidence grades. 55+ Apify actors, 7… The licence is MIT.

When your agent uses it

Research person
Find everything about
Background check
Digital footprint

Example prompts

“research person”
“find everything about”
“due diligence”
“/osint”

Requirements

Python 3
A Bash shell
A credential in PERPLEXITY_API_KEY
A credential in EXA_API_KEY

Workflow steps

8 steps, taken from the step headings in SKILL.md.

Tooling Self-Check
Seed Collection
5: Internal Intelligence
Platform Extraction
Cross-Reference & Confidence Scoring
Psychoprofile
Completeness Evaluation (Recursive)
Dossier Output

What it can do on your machine

Read from SKILL.md and the folder at commit 94f382e. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 12 files in scripts/ (Shell and Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
- bash
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- web.archive.org
Also links to:
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- APIFY_API_TOKEN
- APIFY_TOKEN
- PERPLEXITY_API_KEY
- EXA_API_KEY
- TAVILY_API_KEY
- JINA_API_KEY
- PARALLEL_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Osint loads about 5.5k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 173 tokens; SKILL.md has 2,358 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~173
When it runs · the whole SKILL.md, loaded when a task matches: ~5.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from smixs/osint-skill at commit 94f382e, republished under its MIT licence (© smixs). 2,358 words, ~5,489 tokens.

Download SKILL.mdSave it as .claude/skills/osint/SKILL.md (or your agent's skills folder). This skill also uses 19 other files; get the full folder from GitHub.

name: osint
description: Conduct deep OSINT research on individuals. Build full digital footprint, psychoprofile (MBTI/Big Five), career history, social graph with confidence scores. Recursive self-evaluation until completeness threshold is met. Includes internal intelligence (Telegram history, email, vault contacts) before going external. Use when: "osint", "досье", "research person", "find everything about", "пробей", "разведка", "due diligence", "background check", "digital footprint", "найди всё про", "собери информацию", "кто это", "профиль человека". NOT for: company/product research without a named person, competitive analysis, market research, content generation, or general web scraping tasks.

OSINT Skill v3.2

Systematic intelligence gathering on individuals. From a name or handle to a scored dossier with psychoprofile, career map, and entry points.

Phase Router

Determine entry point from context:

New name/handle/URL, "пробей", "find out about" → Phase 0 (full cycle)
"Add LinkedIn/Instagram data" to existing dossier → Phase 2 (extraction)
"Build psychoprofile" from existing data → Phase 4
"Rate completeness" of existing dossier → Phase 5
"Reformat" or "present" findings → Phase 6

Default (full research request): Phase 0 → 1 → 1.5 → 2 → 3 → 4 → 5 → 6.

Environment

All API keys via environment variables. Never hardcode tokens.

PERPLEXITY_API_KEY — Perplexity Sonar (fast answers + deep research)
EXA_API_KEY — Exa AI (semantic search, company/people research, deep research)
TAVILY_API_KEY — Tavily (agent-optimized search + extract, $0.005/req basic)
APIFY_API_TOKEN — Apify scraping (LinkedIn, Instagram, Facebook)
JINA_API_KEY — Jina reader/search/deepsearch
PARALLEL_API_KEY — Parallel AI search
BRIGHTDATA_MCP_URL — Bright Data MCP endpoint (full URL with token)
MCPORTER_CONFIG — mcporter config path

Scripts

Run from skill dir: bash scripts/<name>.sh. Each validates env vars, exits with descriptive error + URL to get the key.

Search & Research:

diagnose.sh — run FIRST. Capability map of all tools.
perplexity.sh — search <query> | sonar <query> (AI answer) | deep <query> (deep research)
tavily.sh — search <query> (basic $0.005) | deep <query> (advanced) | extract <url>
exa.sh — search <query> | company <name> | people <name> | crawl <url> | deep <prompt>
first-volley.sh "Name" "context" — parallel search, all engines at once.
merge-volley.sh <outdir> — deduplicate and merge first-volley results.

Scraping:

apify.sh — linkedin <url> | instagram <handle> | run | results | store-search
run-actor.sh — universal Apify runner (55+ actors). Embedded from apify/agent-skills. Quick answer: bash scripts/run-actor.sh "actor/id" '{"input":"json"}' Export: bash scripts/run-actor.sh "actor/id" '{"input":"json"}' --output /tmp/out.csv
jina.sh — read <url> | search <query> | deepsearch <query>
parallel.sh — search <query> | extract <url>
brightdata.sh — scrape <url> | scrape-batch | search | search-geo <cc> | search-yandex

Research Escalation Flow

Принцип: от дешёвого к дорогому, от быстрого к глубокому.

Level 1: Quick Answers (секунды, ~$0.00)

Начни ВСЕГДА с этого. Получи быстрый контекст прежде чем копать. Запускай ВСЕ параллельно:

bash

# Perplexity Sonar — AI ответ с цитатами
bash skills/osint/scripts/perplexity.sh sonar "Who is <Name>, <context>"
# Brave Search — классический поиск
web_search "<Name> <company> <role>"
# Tavily — agent-optimized search с AI answer
bash skills/osint/scripts/tavily.sh search "<Name> <context>"
# Exa — семантический поиск + company/people research
bash skills/osint/scripts/exa.sh search "<Name> <context>"
bash skills/osint/scripts/exa.sh people "<Name>"

→ Получаешь: быстрые факты, ссылки, контекст. → Решение: достаточно? → Phase 6. Нужно больше? → Level 2.

Level 2: Source Verification (секунды-минуты, ~$0.01)

Проверяй источники из Level 1 через fetch:

bash

# Читай найденные URL
web_fetch "<url_from_perplexity>"
bash skills/osint/scripts/jina.sh read "<url>"
bash skills/osint/scripts/parallel.sh extract "<url>"

→ Получаешь: подтверждённые факты, cross-reference. → Совпадает? → дополняй досье. Нужно глубже? → Level 3.

Подключай scraping для соцсетей:

bash

# LinkedIn
bash skills/osint/scripts/apify.sh linkedin "<url>"
# Instagram
bash skills/osint/scripts/apify.sh instagram "<handle>"
# Facebook, заблокированные сайты
bash skills/osint/scripts/brightdata.sh scrape "<url>"

→ Получаешь: структурированные профили, фото, связи.

Level 4: Deep Research (~$0.05-0.50)

Если нужно копать ещё глубже — формируй развёрнутый промпт и отправляй в deep research. Запускай ВСЕ параллельно (30-60 сек каждый):

bash

# Perplexity Deep Research
bash skills/osint/scripts/perplexity.sh deep "<detailed research prompt about Name>"
# Exa Deep Research
bash skills/osint/scripts/exa.sh deep "<detailed prompt>"
# Parallel AI Deep Search
bash skills/osint/scripts/parallel.sh search "<detailed query>"
# Jina DeepSearch
bash skills/osint/scripts/jina.sh deepsearch "<query>"

Правило: Level 4 промпт должен быть РАЗВЁРНУТЫМ — включай всё что уже знаешь из Level 1-3, чтобы deep research не повторял базовые факты, а копал дальше.

Swarm Mode (DEFAULT)

OSINT research runs as a swarm of parallel sub-agents on Sonnet. The main agent is the coordinator — it does NOT scrape itself.

How it works:

Main agent runs Phase 0 (tooling check) and Phase 1 (seed collection) to get initial context
Main agent spawns 3-5 sub-agents via sessions_spawn with model: sonnet, mode: run
Each sub-agent gets a focused task + all known data from Phase 1
Sub-agents return results → main agent merges into dossier

Task split pattern:

Agent 1: YouTube/Content — extract transcripts via Apify (NOT yt-dlp, NOT BrightData — YouTube blocks them). 3-5 videos, speech style, topics. Use streamers/youtube-channel-scraper for channel data
Agent 2: Facebook deep — BrightData scrape: profile, posts, about, photos, friends (use m.facebook.com for more data). For public Pages: apify/facebook-pages-scraper + apify/facebook-page-contact-information
Agent 3: Social platforms — Instagram (Apify + tagged/comments scrapers), DOU, company websites, LinkedIn (BrightData). Contact enrichment: vdrmota/contact-info-scraper on found websites
Agent 4: TikTok + Regional — TikTok profile/videos (clockworks/tiktok-profile-scraper), local registries, press, university records, Yandex search, Google Maps (compass/crawler-google-places if business owner)
Agent 5: Deep research — Perplexity deep, Exa deep, Parallel deep (if needed)

Rules:

Always pass ALL known data to each sub-agent (names, URLs, emails, phones, context)
Each sub-agent saves results to /tmp/osint-<subject>-<task>.md
Main agent waits for all results, then runs Phase 3-6 (cross-reference, psychoprofile, dossier)
Budget: each sub-agent ≤$0.15, total swarm ≤$0.50
YouTube transcripts: use Apify actors, NOT BrightData or yt-dlp (both blocked by YouTube)

Why swarm:

5 agents × 5 min = 10 min total (vs 30+ min sequential)
Sonnet is 5x cheaper than Opus
Parallel scraping avoids rate limit stacking on single IP

Phase 0: Tooling Self-Check

Execute bash skills/osint/scripts/diagnose.sh.
Log available vs missing tools.
Check internal tools: tg.py (Telegram history), himalaya (email), vault contacts.
If Bright Data unavailable → Facebook and LinkedIn deep scrape limited. Inform user.
If Apify unavailable → Instagram and LinkedIn structured data limited.
Proceed with available toolset.

Phase 1: Seed Collection

Start with Level 1 (quick answers) ALWAYS before heavy scraping.

Parse user input. Extract identifiers: names, handles, URLs, companies, locations.

Perplexity fast pass:

bash

bash skills/osint/scripts/perplexity.sh search "Who is <Name>, <context>"

Brave + Parallel in parallel:

bash

web_search "<Name> <company>"
bash skills/osint/scripts/first-volley.sh "Full Name" "context"

Review Perplexity citations — fetch and verify top sources:
bash
```
web_fetch "<citation_url_1>"
web_fetch "<citation_url_2>"
```
Parse & merge: bash skills/osint/scripts/merge-volley.sh /tmp/osint-<timestamp>.
Collect all identifiers into seed list. Deduplicate.
Flag name collisions (common names → verify with company/location cross-reference).
Decision point: enough context? → skip to Phase 4. Need social media? → Phase 2. Need deep dive? → Level 4 (deep research).

Rate limiting: wait 1s between Brave queries, 2s between Jina calls. Do NOT hammer APIs in tight loops — stagger parallel launches.

Phase 1.5: Internal Intelligence

Before going external, check what we already know. This phase mines local sources that may contain gold — prior conversations, emails, vault contacts.

Telegram History

If tg.py is available (check Phase 0):

bash

# Search by name/handle in Telegram
python3 skills/telegram/scripts/tg.py search "Name" 20
# If we have their username/id — read conversation history
python3 skills/telegram/scripts/tg.py history <username_or_id> 50

What to extract from Telegram history:

Communication style (formal/informal, language, emoji patterns)
Topics discussed — what they care about, what they ask for
Response patterns — reply speed, active hours → timezone
Shared links/files — projects they work on
How they address the user — relationship dynamics
Mentioned colleagues, partners, competitors → social graph seeds
Pricing discussions, deal terms (if business contact)

⚠️ Telegram history is Grade A intelligence — unfiltered, real-time, authentic. Weight it higher than curated LinkedIn/Instagram profiles. ⚠️ Privacy: internal intelligence stays in the dossier. Never quote DMs in public outputs.

Email History

If himalaya is available:

bash

# Search emails by name or domain
~/.local/bin/himalaya search "from:name@domain.com OR to:name@domain.com" -f INBOX
# Or by name
~/.local/bin/himalaya search "Name Surname" -f INBOX
~/.local/bin/himalaya search "Name Surname" -f Sent

What to extract from email:

Formal communication style vs Telegram style (contrast = insight)
Business proposals, invoices → financial relationship
CC'd people → organizational map
Signature block → title, phone, company, social links (often richer than LinkedIn)

Vault / CRM Check

bash

# Check if we already have a card
grep -rl "Name" vault/crm/ vault/contacts/ 2>/dev/null
# Check MOC indexes (adjust paths to your vault structure)
grep -i "name" vault/MOC/*.md 2>/dev/null

If vault card exists: read it, note last_accessed, existing tags, prior interactions. Don't duplicate — enrich the existing card after research completes.

Node Camera/Location (if paired device available)

If meeting in person and node is available, nodes camera_snap can capture context. Only with explicit user permission.

Internal Intelligence Summary

After Phase 1.5, you should know:

Do we have prior relationship? (cold/warm/hot contact)
What language do they prefer?
What's their communication style?
Any existing business context?
Social graph seeds from conversations

This context shapes Phase 2 priorities — if we already know their career from emails, focus external research on psychoprofile and social media instead.

Phase 2: Platform Extraction

Read references/platforms.md ONLY when needing URL patterns or extraction signals.

Tool priority (primary → fallback). If primary fails, switch immediately. Never retry same tool.

LinkedIn: apify.sh linkedin → brightdata.sh scrape → jina.sh read
Instagram: apify.sh instagram → brightdata.sh scrape
Instagram deep: run-actor.sh "apify/instagram-tagged-scraper" (who tags them), apify/instagram-comment-scraper (sentiment)
Facebook personal: brightdata.sh scrape → none (only Bright Data works)
Facebook pages/groups: run-actor.sh "apify/facebook-pages-scraper" → brightdata.sh scrape
TikTok: run-actor.sh "clockworks/tiktok-profile-scraper" → clockworks/tiktok-scraper (comprehensive)
TikTok discovery: run-actor.sh "clockworks/tiktok-user-search-scraper" (find by keywords)
YouTube: run-actor.sh "streamers/youtube-channel-scraper" → jina.sh read → brightdata.sh scrape
Telegram channels: web_fetch t.me/s/{channel} → jina.sh read
Twitter/X: python3 scripts/twitter.py tweet <url> → jina.sh read
Google Maps (businesses): run-actor.sh "compass/crawler-google-places"
Contact enrichment: run-actor.sh "vdrmota/contact-info-scraper" (extract emails/phones from any URL)
Any site: jina.sh read → brightdata.sh scrape

run-actor.sh = universal Apify runner (embedded, 55+ actors). See references/tools.md for full actor catalog.

Read references/tools.md ONLY when troubleshooting a failed tool.

⚠️ Content Platform Rule (CRITICAL)

When you find YouTube, podcast, blog, or conference talks — read references/content-extraction.md immediately and extract 3-5 pieces of content on the spot.

Do NOT just note the URL. Extract transcripts/text NOW. A 20-minute YouTube video reveals more about a person than their entire LinkedIn. Content platforms are the #1 source for psychoprofile — skipping them = shallow dossier.

OpSec-Aware Targets

If initial searches return unusually little for someone who should have a footprint:

Wayback Machine: web_fetch "https://web.archive.org/web/2024*/target-url" — deleted profiles, old bios
Google Cache: web_search "cache:domain.com/path" — recently removed pages
Yandex Cache: brightdata.sh search-yandex "Name" — Yandex indexes CIS deeper and caches longer
Username variations: try transliteration (Иванов → ivanov, ivanoff), birth year suffixes, company abbreviations
Reverse image search: if photo found, check for other profiles using same avatar
Conference archives: speaker bios often survive after profiles are deleted

Phase 3: Cross-Reference & Confidence Scoring

Step 1: Fact Table

List every claim as a row: fact | source 1 | source 2 | grade.

Show full SKILL.md (969 more words)Show less

Step 2: Cross-check key facts

For each critical fact (employer, role, location, education):

Compare LinkedIn title vs Telegram signature vs email signature vs company website
If 2+ match → Grade A
If only 1 source → Grade B
If inferred (timezone from messages, geotag) → Grade C
If single unverified mention → Grade D

Step 3: Resolve contradictions

If LinkedIn says "CEO" but company site says "Co-founder" — flag explicitly. Include both with sources. Do NOT silently pick one.

Step 4: Name collision check

If common name — verify at least 2 facts (company + city, or photo + company) link to same person. If unsure, split into separate entities.

Confidence grades:

A (confirmed): 2+ independent sources, or official/verified profile, or direct Telegram/email conversation
B (probable): 1 credible source (LinkedIn, official media, company site)
C (inferred): indirect evidence (photo geotag, timezone from message patterns, connections)
D (unverified): single mention, could be wrong

Internal intelligence (Phase 1.5) counts as an independent source.

Phase 4: Psychoprofile

Read references/psychoprofile.md ONLY at this phase.

Collect text samples: posts, bios, interviews, channel content, Telegram messages (highest signal).
Assess MBTI per dimension with cited behavioral evidence and confidence (high/medium/low).
Quantify writing style: sentence length, emoji density, self-reference rate.
Compare formal (LinkedIn/email) vs informal (Telegram/Instagram) voice — the delta reveals the real person.
Deduce values from actions, not self-reported claims.
Zodiac ONLY if DOB confirmed (Grade A or B).

Phase 5: Completeness Evaluation (Recursive)

Axis 1: Data Coverage (pass/fail per dimension)

9 mandatory checks. If any fail, flag as critical gap:

Subject correctly identified? (not a namesake)
Current role/company confirmed?
At least 2 social platforms found?
At least 1 contact method (email/phone/messenger)?
Career history has 2+ verifiable positions?
Location (current) established?
At least 1 photo found?
No unresolved contradictions between sources?
Internal intelligence checked? (Telegram/email/vault — even if empty)

Axis 2: Depth Score (8 weighted criteria)

Dimension	Weight	What to score (1-10)
Identity	0.15	Full name, DOB, location, education, photo
Career	0.20	Completeness of work history, current role clarity
Digital footprint	0.15	Number of platforms found, account activity level
Psychoprofile	0.15	MBTI confidence, writing style quantified, values deduced
Internal intel	0.10	Telegram/email history depth, vault data
Personal life	0.05	Family, hobbies, lifestyle, pets
Cross-reference	0.10	How many facts are A-grade, contradiction count
Actionability	0.10	Entry points identified, approach strategy clear

Weighted sum (1-10) = Depth Score.

Axis 3: Source Diversity

Count unique source types used (max 12): LinkedIn, Instagram, Facebook, Telegram DM, Telegram channel, VK, Twitter/X, company website, press/media articles, conference profiles, government/business registries, email correspondence.

8+ source types = Excellent
5-7 = Good
2-4 = Shallow
1 = Insufficient

Gap Analysis

Depth Score	Coverage	Diagnosis	Action
8+	All pass	Strong dossier	Proceed to Phase 6
8+	Some fail	Deep but blind spots	Target failed checks, 1 more cycle
<7	All pass	Wide but shallow	Deepen via interviews/articles/deepsearch
<7	Some fail	Restart needed	Different search angle, new tool combination

Stopping Criteria

(a) Depth Score ≥ 8.0 AND all coverage checks pass → exit to Phase 6 (b) 3 cycles completed → deliver best available with honest assessment (c) Two cycles with delta < 0.5 → plateau reached, deliver with note

Calibration Benchmarks

9-10: full career timeline, 5+ platforms, confirmed DOB, psychoprofile with high confidence, family/hobbies known, multiple entry points, Telegram history analyzed. Equivalent to a professional PI report.
7-8: career outline, 3+ platforms, most facts B-grade or above, psychoprofile with medium confidence. Solid due diligence.
5-6: basic bio, 1-2 platforms, some gaps. Quick background check level.
<5: minimal data found. Name + current role at best. Flag as insufficient.

Phase 6: Dossier Output

Read assets/dossier-template.md before rendering. Follow the template structure exactly. No markdown tables in output (Telegram cannot render). Bullet lists only. Report Depth Score, source count, source types, and total API spend.

If internal intelligence was used, add a separate "из переписки" section (marked as internal/confidential, not for sharing outside).

Budget

≤$0.50 per target: spend without asking.
$0.50: ask user before proceeding.
Track cumulative spend per research session.

Troubleshooting

All tools return empty: target has minimal digital presence. Try Bright Data Yandex search (better for CIS region), search by company + role instead of name.
Wrong person keeps appearing: add company name, city, or role to all queries. Use quotes around full name.
LinkedIn blocked: use brightdata.sh scrape as primary instead of Apify.
Apify actor dead/changed: check apify.sh store-search "linkedin scraper" for alternatives. Actors on Apify are volatile — always have a Bright Data fallback.
Depth Score stuck at 6-7: likely missing press/media articles or internal intel. Search industry publications (AdIndex, Sostav, Forbes, Kommersant for Russian market). Try jina.sh deepsearch. Check Telegram history.
No social media found: person may use pseudonyms. Search by email, phone, or company employee page. Search Apify store: bash scripts/apify.sh store-search "people search". If mcpc installed: APIFY_TOKEN=$APIFY_API_TOKEN mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call search-actors keywords:="people search" limit:=10. Check Telegram contacts by phone.
TikTok scraper fails: try clockworks/free-tiktok-scraper (free tier) as fallback. TikTok usernames often differ from other platforms — search by real name via clockworks/tiktok-user-search-scraper.
Need emails from website: use vdrmota/contact-info-scraper — it crawls the site and extracts all contact info.
Rate limited (429): back off 5s, then 15s. Switch to fallback tool. Never retry immediately.

Anti-Patterns

Never start with a single tool. Launch all available in parallel.
Never retry a failed tool more than once. Switch to fallback.
Never guess DOB, family, or zodiac.
Never attribute data without cross-referencing against namesakes.
Never include unsourced facts.
Never reveal OSINT methods in public messages.
Never exceed 3 recursive cycles. Diminishing returns.
Never rate Depth Score 9+ without justification.
Never skip psychoprofile. Without it, dossier = Wikipedia article.
Never skip Phase 1.5 (internal intel). Telegram history is often the richest source.
Never quote DMs verbatim in shareable outputs. Summarize and cite.
Never hammer APIs without rate limiting. Stagger requests.

Files

SKILL.md and 19 other files (scripts, references, assets) in osint of smixs/osint-skill.

SKILL.md
assets/dossier-template.md
references/content-extraction.md
references/platforms.md
references/psychoprofile.md
references/tools.md
scripts/apify.sh
scripts/brightdata.sh
scripts/diagnose.sh
scripts/exa.sh
scripts/first-volley.sh
scripts/jina.sh
scripts/mcp-client.py
scripts/merge-volley.sh
scripts/package.json
scripts/parallel.sh
scripts/perplexity.sh
scripts/run-actor.sh
… and 2 more

Open the folder on GitHubat commit 94f382e

Compare with similar skills

Osint next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Osint compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Osint this skillsmixs/osint-skill	141	—	~5.5k	Automated safety check: Pass	MIT	7 mo ago
Apify Multi-Platform Scraperapify/agent-skills	2.4k	2 repos	~1.4k	Automated safety check: Notes	None	today
Apify Buying Signal Detectionapify/awesome-skills	265	—	~5.1k	Automated safety check: Notes	Apache-2.0	16 days ago
Apify Google Maps Leadsapify/awesome-skills	265	—	~3.8k	Automated safety check: Pass	Apache-2.0	16 days ago
Luma Event Attendeesgooseworks-ai/goose-skills	1.2k	1 repos	~1.7k	Automated safety check: Notes	MIT	today
Apify Ads Intelligenceapify/awesome-skills	265	—	~4.2k	Automated safety check: Notes	Apache-2.0	16 days ago

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Data & AnalyticsAuto-check: notes
Apify Buying Signal Detection
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Works with

Questions about Osint

What does Osint do?

Conduct deep OSINT research on individuals. An agent skill from smixs/osint-skill. Osint is an agent skill from smixs/osint-skill. Conduct deep OSINT research on individuals.

When should I use Osint?

Osint fits situations like: research person; find everything about; background check; digital footprint.

How do I install Osint in Claude Code?

Run `npx skills add smixs/osint-skill --skill osint -a claude-code`. Or copy the skill folder (osint in smixs/osint-skill) into .claude/skills/osint in your project. Claude Code loads it when a task matches its description.

How do I install Osint in Codex?

Run `npx skills add smixs/osint-skill --skill osint -a codex`. Or copy the skill folder (osint in smixs/osint-skill) into .agents/skills/osint in your project. Codex loads it when a task matches its description.

Can I use Osint in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add smixs/osint-skill --skill osint -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/osint, .gemini/skills/osint, .github/skills/osint and .opencode/skills/osint in your project.

What does Osint need to run?

Going by SKILL.md and its folder, Osint needs a shell and Python for the scripts in its folder, the command-line tools its instructions call (bash and python3) and credentials named APIFY_API_TOKEN, APIFY_TOKEN, PERPLEXITY_API_KEY and EXA_API_KEY. Our summary lists: Python 3; A Bash shell; A credential in PERPLEXITY_API_KEY; A credential in EXA_API_KEY.

Does Osint access the network?

SKILL.md names 2 domains. In commands or code: web.archive.org; the agent is likely to contact it when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is Osint safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Osint use?

Osint is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Osint use?

About 5.5k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6k tokens, read only when the agent opens those files.

What are the alternatives to Osint?

Skills that share tags, products or a category with Osint: Apify Multi-Platform Scraper (apify/agent-skills, 2.4k stars), Apify Buying Signal Detection (apify/awesome-skills, 265 stars), Apify Google Maps Leads (apify/awesome-skills, 265 stars) and Luma Event Attendees (gooseworks-ai/goose-skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Osint?

smixs (a GitHub user) maintains it in smixs/osint-skill, which has 141 GitHub stars. The repository was last updated on March 10, 2026.

Source: smixs/osint-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Osint

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

OSINT Skill v3.2

Phase Router

Environment

Scripts

Research Escalation Flow

Level 1: Quick Answers (секунды, ~$0.00)

Level 2: Source Verification (секунды-минуты, ~$0.01)

Level 3: Social Media Deep Dive (~$0.01-0.10)

Level 4: Deep Research (~$0.05-0.50)

Swarm Mode (DEFAULT)

How it works:

Task split pattern:

Rules:

Why swarm:

Phase 0: Tooling Self-Check

Phase 1: Seed Collection

Phase 1.5: Internal Intelligence

Telegram History

Email History

Vault / CRM Check

Node Camera/Location (if paired device available)

Internal Intelligence Summary

Phase 2: Platform Extraction

⚠️ Content Platform Rule (CRITICAL)

OpSec-Aware Targets

Phase 3: Cross-Reference & Confidence Scoring

Step 1: Fact Table

Step 2: Cross-check key facts

Step 3: Resolve contradictions

Step 4: Name collision check

Confidence grades:

Phase 4: Psychoprofile

Phase 5: Completeness Evaluation (Recursive)

Axis 1: Data Coverage (pass/fail per dimension)

Axis 2: Depth Score (8 weighted criteria)

Axis 3: Source Diversity

Gap Analysis

Stopping Criteria

Calibration Benchmarks

Phase 6: Dossier Output

Budget

Troubleshooting

Anti-Patterns

Files

Compare with similar skills

Similar skills

Apify Multi-Platform Scraper

Apify Buying Signal Detection

Apify Google Maps Leads

Luma Event Attendees

Apify Ads Intelligence

Bright Data MCP

Related

Questions about Osint