lead-intelligence skillB
lead-intelligence is agent-read markdown (skill) from naveedharri/benai-skills: Deep-research lead intelligence gathering for B2B qualified leads. This skill runs in two layers: (1) General Lead Intelligence via web research using parallel sub-agents, and (2) LinkedIn Lead Intelligence via Apify actors for profile and post scraping. Use this skill whenever the user says "research these leads", "get intel on my leads", "lead intelligence", "lead enrichment", "enrich my leads", "deep research leads", "find out about these companies", "LinkedIn scraping", "scrape LinkedIn prof.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# 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: 1. **A qualified lead list** with at minimum: name, company, website, LinkedIn URL 2. **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-researcher` sub-agents (one per batch of 5 leads), each doing web research. - **Layer 2 (LinkedIn Lead Intelligence)**: ONE `linkedin-scraper` sub-agent handling the entire LinkedIn scraping pipeline (BOTH actors: profiles AND posts). **In practice: N+1 sub-agents spawned in a single message:** …
Read the whole file at its exact version.
How to install
mdr add naveedharri/benai-skills/lead-intelligence@git:20260916.1a4a64amdr add naveedharri/benai-skills/lead-intelligence@sha256:75892c1a72323fecPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_tr2kdtuple27qta6)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260916.1a4a64a latest | 2026-09-16 | 1a4a64a | 13,848 B | B | view · diff |
| git:20260723.0af5e11 | 2026-07-23 | 0af5e11 | 13,840 B | B | view |
Audit of the latest version
- fail: No prompt-injection phrasing (matched: disregard all prior prompts)
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (13848 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
naveedharri/benai-skills · 65 stars · license MIT · pushed 2026-09-16 · branch develop
API
GET https://markdownregistry.com/api/v1/artifacts/art_tr2kdtuple27qta6 GET https://markdownregistry.com/api/v1/resolve?ref=naveedharri/benai-skills/lead-intelligence GET https://markdownregistry.com/api/v1/blob/75892c1a72323fec25245ffc7ad5bc58ccc75bd848171c4f6779f5dc29e069d6
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