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.
git clone --depth 1 https://github.com/HermeticOrmus/LibreSecOps-Claude-CodeWrote 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/commands/hermeticormus/libresecops-claude-code/social-eng-assess)<a href="https://agentmods.dev/commands/hermeticormus/libresecops-claude-code/social-eng-assess"><img src="https://agentmods.dev/badge/commands/hermeticormus/libresecops-claude-code/social-eng-assess/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.
<a href="https://agentmods.dev/commands/hermeticormus/libresecops-claude-code/social-eng-assess"><img src="https://agentmods.dev/badge/commands/hermeticormus/libresecops-claude-code/social-eng-assess.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00027 | $0.01244 |
| Opus 5.5 | $0.00011 | $0.00498 |
| Sonnet 5.5 | $0.00005 | $0.00249 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
social-eng-assess 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 12d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/social-eng-assess
Assess an organization's vulnerability to social engineering attacks, identifying risk factors, current defense gaps, and prioritized recommendations.
Trigger
Use when evaluating an organization's resilience to social engineering. Appropriate for:
- Baseline assessment before launching an awareness program
- Post-incident assessment after a social engineering attack
- Regulatory compliance assessment (security controls evaluation)
- Annual security program review
- Pre-acquisition due diligence (target company security culture)
Input
- Organization profile: Industry, size, geographic distribution, remote vs in-office
- Communication patterns: Email systems, collaboration tools, phone systems, external-facing staff
- Current defenses: Email security (SPF/DKIM/DMARC), MFA status, awareness training history, phishing simulation results
- Incident history: Past social engineering incidents (phishing, BEC, vishing, physical)
- Public exposure: Website, social media presence, employee information publicly available
- Regulatory environment: Applicable compliance requirements
- Specific concerns: Any known areas of weakness or recent threats
Process
-
Threat landscape assessment -- Identify the most likely social engineering threats based on industry and organization profile:
- Financial sector: BEC, wire fraud, account takeover
- Healthcare: Patient data phishing, ransomware via phishing, credential theft
- Technology: Supply chain phishing, developer targeting, source code theft
- Government: Espionage-motivated spear phishing, insider recruitment
- Retail: Payment fraud, POS compromise via phishing, customer data theft
-
Attack surface analysis -- Map the human attack surface:
- Email exposure (how many addresses are publicly discoverable)
- Phone exposure (direct dial numbers, receptionist information)
- Physical exposure (public lobbies, delivery areas, parking)
- Social media exposure (LinkedIn profiles, company information)
- OSINT availability (organizational structure, technology stack, vendor relationships)
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.
- 12d ago Changed · +5 lines · +27 tokens per session 5c065b4dbce5
- 1mo ago First seen · 117 lines · 0 tokens per session scan A 55c40717d3c8
social-eng-assess is a command published in the GitHub repository HermeticOrmus/LibreSecOps-Claude-Code (4 stars, last pushed 9d ago), licensed MIT. It adds 27 tokens to every session and 1,244 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
Other commands, from other repositories
verify
An independent recheck of a security assessment report that derives conclusions from its own evidence instead of accepting the original findings.
corporate-launcher
Build a secure, branded, organization-specific launcher around an AI coding CLI (Claude Code, Codex CLI, Gemini CLI, Cursor, or Cline) so the user can.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.