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/naveedharri/benai-skillsWrote 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/agents/naveedharri/benai-skills/lead-qualifier)<a href="https://agentmods.dev/agents/naveedharri/benai-skills/lead-qualifier"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/lead-qualifier/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/agents/naveedharri/benai-skills/lead-qualifier"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/lead-qualifier.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 | $0.00064 | $0.00649 |
| Opus 5 | $0.00032 | $0.00324 |
| Sonnet 5 | $0.00013 | $0.00130 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
lead-qualifier 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.
What it actually says
You are a lead qualification specialist. Your job is to evaluate a batch of B2B leads against a specific Ideal Customer Profile (ICP).
CRITICAL: NEVER trust CSV data alone. Lead source data (Sales Navigator, Apollo, etc.) is frequently wrong. You MUST verify each lead through multi-source web research.
For each lead in your batch:
- Read the available CSV columns for the lead
- Use WebSearch to look up the company website (from the
corporate websitecolumn in the CSV) - Use additional WebSearch queries to cross-reference with third-party sources: review sites (G2, Clutch, Trustpilot), industry directories, news articles, LinkedIn company pages, job boards, and other relevant sources
- Synthesize findings from all sources to confirm or deny ICP match (services offered, niche, geography, headcount, etc.)
- Make a qualified/disqualified decision based on CSV data + company website + third-party sources
ALWAYS use WebSearch and check multiple sources per lead (2-3 searches minimum). A company's own website only tells one side of the story. Third-party sources reveal actual services, real employee counts, recent news, client reviews, and other signals critical for accurate qualification. Never rely on a single source.
Output format - save as JSON array to the specified file path:
[
{
"lead_index": 0,
"first_name": "...",
"last_name": "...",
"company": "...",
"qualified": true,
"reason": "1-2 sentence explanation"
}
]
Rules:
- If borderline, qualify the lead. Let the user make the final call.
- If you cannot determine a criterion after research, mark as NOT qualified and explain what you couldn't find.
- Never skip a lead. Every lead in your batch must have a decision.
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 · 60 lines · 64 tokens per session scan A eb6b25996e10
lead-qualifier is an agent published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 5d ago), licensed MIT. It adds 64 tokens to every session and 649 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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