Social Engineering Recon logo

Social Engineering Recon

Organization
zero-lab-ai
social-engineering-recon

Maps human-layer exposure from client-supplied and passive public evidence at cohort, role, process, channel, and relationship level. Use when preparing or defending an authorized social-engineering assessment without contacting people, building personal dossiers, ranking targets, or generating pretexts.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namesocial-engineering-recon
Stars
232
Forks
14
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Social Engineering Recon AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zero-lab-ai/skills/tree/main/plugins/recon-operations/skills/social-engineering-recon
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/recon-operations/skills/social-engineering-recon .claude/skills/social-engineering-recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Social Engineering Recon in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Social Engineering Recon on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Social Engineering Recon is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Social-engineering recon

Public information can expose organizational roles, processes, channels, relationships, and control assumptions. Defensive assessment measures those patterns without turning people into targets.

This skill analyzes already supplied evidence. It does not discover sources, contact a target, build personal profiles, rank people, or generate personas, pretexts, messages, lures, payloads, or execution instructions.

When to Use

  • Preparing or defending an authorized social-engineering assessment without contacting people, building personal dossiers, ranking targets, or generating pretexts.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Workflow

  1. Record digest-addressed written-authority evidence, its approving opaque role and validity window, an exact assessment time, passive-only mode, opaque identity handling, permitted source classes, bounded retention, and limitations.
  2. Import digest-addressed acquisition receipts from osint-scanner or the supplying system. Preserve source class, opaque origin and its evidence, contact classification, retrieval time, validity evidence, disposition, and opaque source reference. Do not recreate a tool or query catalogue here.
  3. Exclude out-of-scope, sensitive, unverifiable, and expired material explicitly. Public availability does not erase privacy or scope.
  4. Represent only opaque organizational cohorts, roles, processes, channels, and relationships. Never store names, addresses, handles, personal profiles, routines, protected traits, health data, or susceptibility scores.
  5. Record one direct fact per observation. Bind it to one source, one opaque claim, one or more organizational units, a controlled fact class, and a support or contradiction stance. Bind counterevidence directly to the supporting observation it counters. Store no free-form narrative.
  6. Build assessments from observation IDs. Two result pages copied from one publication share one origin_id; repetition is not independent corroboration. Unknown or expired validity cannot establish current support. Accepted counterevidence forces disputed.
  7. Keep conclusions organizational. supported, limited, disputed, and not-supported describe evidence strength, not exploitability, susceptibility, compromise, campaign readiness, or permission to contact anyone.
  8. Create only defensive handoffs: control review, remediation, engagement planning, or a separate capability. Pass opaque assessment references, not target lists or tactics.
  9. Cover authority, provenance, privacy, role, process, relationship, channel, timing, control assumptions, freshness and contradiction, and defensive handoff for every scope unit. Proof must match the coverage class.

Boundary with adjacent skills

  • Use osint-scanner to plan acquisition and classify whether a tool or mode contacts the target. This skill consumes its receipt and analyzes supplied evidence.
  • Use social-engineering-engagement-plan for cohorts, approved pretexts, participant safety, infrastructure readiness, measurement, and debrief before any interaction.
  • Use opsec-planning for action-level artifacts, detection risk, and abort decisions.
  • Use findings-report only after a demonstrated issue is ready for formal reporting.

Rationalizations to reject

  • "It is public, so it is fair game." Public availability does not remove scope, privacy, retention, or harm constraints.
  • "Names make the assessment actionable." Organizational controls can be assessed with opaque roles and cohorts; names turn analysis into dossiering.
  • "It is only recon." Collection and synthesis can harm people before contact.
  • "Two search results corroborate it." Copies of one origin are one origin.
  • "The old page is probably current." Unknown freshness limits the claim.
  • "No control was visible, so none exists." Missing evidence is not absence.
  • "Sponsor approval lets us verify live." Passive authority forbids target contact.
  • "Ranking likely targets is defensive prioritization." Prioritize controls and processes, never people.
  • "A sample pretext explains the risk." It crosses into deceptive-content creation.
  • "A complete record means launch." It validates the recon record only; the engagement planner still governs interaction.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Social Engineering Recon AI skill do?

Maps human-layer exposure from client-supplied and passive public evidence at cohort, role, process, channel, and relationship level. Use when preparing or defending an authorized social-engineering assessment without contacting people, building personal dossiers, ranking targets, or generating pretexts.

Why use Social Engineering Recon on TypingMind?

Because you install it once and use it with any model. Social Engineering Recon is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Social Engineering Recon in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/recon-operations/skills/social-engineering-recon. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Social Engineering Recon?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Social Engineering Recon?

As many as you like. As long as a model supports skills, you can use Social Engineering Recon with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Social Engineering Recon AI skill free?

Yes. It is published on GitHub by zero-lab-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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