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 iPythoning/b2b-sdr-agent-template --skill lead-discoverygit clone --depth 1 https://github.com/iPythoning/b2b-sdr-agent-templateWrote 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/ipythoning/b2b-sdr-agent-template/lead-discovery)<a href="https://agentmods.dev/skills/ipythoning/b2b-sdr-agent-template/lead-discovery"><img src="https://agentmods.dev/badge/skills/ipythoning/b2b-sdr-agent-template/lead-discovery/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/ipythoning/b2b-sdr-agent-template/lead-discovery"><img src="https://agentmods.dev/badge/skills/ipythoning/b2b-sdr-agent-template/lead-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00943 |
| Opus 5.5 | $0.00013 | $0.00377 |
| Sonnet 5.5 | $0.00007 | $0.00189 |
| Haiku 4.5 | $0.00003 | $0.00094 |
Grade A, and why
lead-discovery scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s 'https://s.jina.ai/QUERY_URL_ENCODED' \ How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Discovery — AI-Powered Prospecting
Automatically search, filter, and evaluate potential buyers based on your ICP profile.
Triggers
- Cron scheduled execution (Daily 10:00)
- Manual command from owner: "Search for leads in [market/industry]"
Search Strategy
Search Dimensions (rotate daily, pick 1-2)
-
Target Market Procurement
- "{{product}} buyers [target country] 2026"
- "[target country] fleet expansion logistics company"
- "[target country] construction equipment procurement"
-
Trade Shows & Procurement Signals
- "{{product}} buyers exhibition Africa Middle East 2026"
- "transport logistics tender [region]"
-
Company Research (read website)
- After discovering a target company, read their website for detailed info
-
Customs / Trade Data
- "[target country] {{product}} import statistics"
- "{{product}} import demand [region] 2026"
Search Execution
Jina Search (find potential buyers)
curl -s 'https://s.jina.ai/QUERY_URL_ENCODED' \
-H 'Authorization: Bearer $JINA_API_KEY' \
-H 'Accept: application/json'
Jina Reader (read company website)
curl -s 'https://r.jina.ai/https://target-company.com' \
-H 'Authorization: Bearer $JINA_API_KEY' \
-H 'Accept: application/json'
JINA_API_KEY in .secrets/env. Get one free at https://jina.ai/
3-Layer Enrichment Pipeline
Layer 1: Website Extraction
Read company website via Jina Reader → extract:
- Company size, employee count
- Product lines, services
- Certifications (ISO, etc.)
- Contact info (email, phone, WhatsApp)
- Office/warehouse locations
Layer 2: Purchase Signal Search
Jina Search for:
- "[company name] procurement tender"
- "[company name] fleet expansion"
- "[company name] import export"
Layer 3: Information Integration
- Combine all findings into enrichment profile
- Calculate ICP score based on USER.md criteria
- Store research notes in Supermemory with tag "customer_research"
Evaluation Flow
For each discovered prospect:
- Extract: company name, country, industry, size, contact info (email/WhatsApp/phone)
- Read company website via Jina Reader for deep understanding
- Score per USER.md ICP criteria (1-10)
- ICP >= 5: Write to CRM (source=
web_discovery, status=new) - ICP >= 7: Also mark as hot_lead, create research note
- Email found: Mark next_action=
email_outreach - WhatsApp found: Mark next_action=
whatsapp_outreach
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.
- 2mo ago First seen · 121 lines · 33 tokens per session scan A a81da024dfea
lead-discovery is a skill published in the GitHub repository iPythoning/b2b-sdr-agent-template (191 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 943 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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