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 naveedharri/benai-skills --skill lead-intelligencegit 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/skills/naveedharri/benai-skills/lead-intelligence)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/lead-intelligence"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/lead-intelligence/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/naveedharri/benai-skills/lead-intelligence"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/lead-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 261 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00024 | $0.03387 |
| Opus 5 | $0.00012 | $0.01693 |
| Sonnet 5 | $0.00005 | $0.00677 |
| Haiku 4.5 | $0.00002 | $0.00339 |
Grade A, and why
lead-intelligence 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 5d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Intelligence
Gather deep intelligence on a list of qualified B2B leads. This involves two layers of research that feed into a single enriched CSV.
Before You Start
Collect from the user:
- A qualified lead list with at minimum: name, company, website, LinkedIn URL
- Context on what they're selling so research focuses on relevant signals
LinkedIn Scraping Path: Apify
Use the Apify MCP connector directly (call-actor, get-dataset-items, etc.). This is the only supported path.
If LinkedIn URLs aren't available, skip Layer 2 and run only Layer 1 (web research).
Critical Rule: Parallel Execution of Both Layers
Layer 1 and Layer 2 MUST run in parallel, not sequentially.
When both layers are being used, spawn everything at the same time in a single message:
- Layer 1 (General Lead Intelligence): Multiple
lead-researchersub-agents (one per batch of 5 leads), each doing web research. - Layer 2 (LinkedIn Lead Intelligence): ONE
linkedin-scrapersub-agent handling the entire LinkedIn scraping pipeline (BOTH actors: profiles AND posts).
In practice: N+1 sub-agents spawned in a single message:
- N
lead-researchersub-agents for Layer 1 (N = ceil(total_leads / 5)) - 1
linkedin-scrapersub-agent for Layer 2 (handles BOTH Apify actors: profiles AND posts)
All spawn simultaneously. Do NOT wait for one layer to finish before starting the other.
Critical: Spawn ALL N+1 sub-agents in a single message. If there are 40 leads, that's 8 lead-researcher + 1 linkedin-scraper = 9 sub-agents spawned simultaneously. For 200 leads, that's 41 sub-agents in one shot. Every sub-agent launches at once.
After ALL sub-agents complete, run the merge script (see "Data Persistence and Merge" below) to combine results into the CSV.
Layer 1: General Lead Intelligence (Web Research)
Each lead-researcher sub-agent handles 5 leads and produces a structured intelligence report covering:
- SUMMARY: 2-3 sentence overview
- WHAT THEY DO: Services/products, revenue model
- WHY THEY DO IT: Founding story, mission
- NICHES: Industries/verticals served
- KEY SERVICES: Exhaustive list from their website
- CASE STUDIES: Published wins with specifics
- UNIQUE POSITIONING: Competitive differentiation
- COMPANY NAME VARIANTS: Abbreviations, legal names
- ROLE: The lead's specific role and responsibilities
- PUBLIC MENTIONS: Awards, press, directories, podcasts
- SPEAKING/CONTENT: Talks, blog posts, published content
- PERSONAL INTERESTS: Publicly available personal info
- ACHIEVEMENTS: Awards, certifications, milestones
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.
- 5d ago First seen · 264 lines · 0 tokens per session scan A 83c5a66d0f6a
lead-intelligence is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 7d ago), licensed MIT. It adds 24 tokens to every session and 3,387 once invoked, about $0.0001 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 2026-09-05.
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