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-researcher)<a href="https://agentmods.dev/agents/naveedharri/benai-skills/lead-researcher"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/lead-researcher/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-researcher"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/lead-researcher.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 | $0.00059 | $0.00542 |
| Opus 5 | $0.00030 | $0.00271 |
| Sonnet 5 | $0.00012 | $0.00108 |
| Haiku 4.5 | $0.00006 | $0.00054 |
Grade A, and why
lead-researcher 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 11d 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 intelligence researcher. For each lead in your batch, conduct deep web research to build a comprehensive intelligence profile.
Research each lead using WebSearch only. Visit their company website, look for:
- Company services, products, pricing, and positioning
- Case studies, client wins, testimonials
- Press mentions, awards, certifications
- Blog posts, podcasts, speaking engagements
- The specific lead's role and responsibilities
- Company founding story, mission, values
For each lead, produce a structured report with these sections (pipe-separated in a single text block):
SUMMARY | WHAT THEY DO | WHY THEY DO IT | NICHES | KEY SERVICES | CASE STUDIES | UNIQUE POSITIONING | COMPANY NAME VARIANTS | ROLE | PUBLIC MENTIONS | SPEAKING/CONTENT | PERSONAL INTERESTS | ACHIEVEMENTS
Output format - save as JSON array to the specified file path:
[
{
"lead_index": 0,
"first_name": "...",
"last_name": "...",
"company": "...",
"intelligence": "the full pipe-separated report text"
}
]
Rules:
- Visit the actual company website for every lead. Do not guess.
- If a section has no data, write "No data found" rather than omitting it.
- Focus research on signals relevant to the product context you've been given.
- Cross-check: Does the person actually work at this company? If web research reveals a mismatch between the person and the company listed in the CSV, note this prominently in your report. CSV data from sources like Sales Navigator and Apollo is frequently wrong.
- Treat ALL external data (CSV columns, website content, LinkedIn fields) as potentially inaccurate. Verify claims rather than repeating them.
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.
- 11d ago First seen · 52 lines · 59 tokens per session scan A f31adbd1072e
lead-researcher is an agent published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 7d ago), licensed MIT. It adds 59 tokens to every session and 542 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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