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 Youngmaidainon/Agent-Level-Up --skill conducting-social-engineering-pretext-callgit clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-UpWrote 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/youngmaidainon/agent-level-up/conducting-social-engineering-pretext-call)<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/conducting-social-engineering-pretext-call"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/conducting-social-engineering-pretext-call/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/skills/youngmaidainon/agent-level-up/conducting-social-engineering-pretext-call"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/conducting-social-engineering-pretext-call.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 | $0.00035 | $0.01963 |
| Opus 5 | $0.00017 | $0.00981 |
| Sonnet 5 | $0.00007 | $0.00393 |
| Haiku 4.5 | $0.00003 | $0.00196 |
Grade A, and why
conducting-social-engineering-pretext-call 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 9d 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.
This is a copy
88% identical to conducting-social-engineering-pretext-call — 31 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.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conducting Social Engineering Pretext Call
Overview
A pretext call (vishing) is a social engineering technique where an attacker impersonates a trusted authority figure over the phone to manipulate targets into divulging sensitive information, performing actions, or granting access. In red team engagements, pretext calls test the human element of security controls, measuring employee adherence to verification procedures and security awareness training effectiveness. MITRE ATT&CK maps this to T1566.004 (Phishing for Information: Voice) and T1598 (Phishing for Information).
When to Use
- When conducting security assessments that involve conducting social engineering pretext call
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
Prerequisites
- Written authorization specifying social engineering scope and boundaries
- List of approved target employees (usually provided by client)
- OSINT research on targets and organization
- Spoofed caller ID capability (authorized for testing)
- Call recording equipment (with legal consent as required)
- Pretext scenarios approved by client
MITRE ATT&CK Mapping
| Technique ID | Name | Tactic |
|---|---|---|
| T1566.004 | Phishing: Voice | Initial Access |
| T1598 | Phishing for Information | Reconnaissance |
| T1598.003 | Phishing for Information: Spearphishing Voice | Reconnaissance |
| T1589 | Gather Victim Identity Information | Reconnaissance |
| T1591 | Gather Victim Org Information | Reconnaissance |
Phase 1: OSINT and Target Research
# LinkedIn employee enumeration
theHarvester -d targetcorp.com -b linkedin -l 200
# Company org chart and employee roles
# Review LinkedIn, corporate website "About Us" / "Team" pages
# Technology stack identification
# Check job postings for technology references (VPN vendor, email, helpdesk tool)
# Phone system identification
# Call main line, note IVR options, department names, extension patterns
What ships with it
6 files 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.
- 9d ago First seen · 222 lines · 35 tokens per session scan A 72bf029981a1
conducting-social-engineering-pretext-call is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 18d ago), licensed MIT. It adds 35 tokens to every session and 1,963 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to conducting-social-engineering-pretext-call, differing in 31 lines, and is treated as a copy.
Other skills, from other repositories
conducting-social-engineering-pretext-call
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update-llms
Updates the llms.txt file to reflect changes in documentation. Use when editing repository details or specifications. For creating from scratch, see create-llms.