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 Maudeunfledged834/startup-founder-skills --skill lead-scoringgit clone --depth 1 https://github.com/Maudeunfledged834/startup-founder-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/maudeunfledged834/startup-founder-skills/lead-scoring)<a href="https://agentmods.dev/skills/maudeunfledged834/startup-founder-skills/lead-scoring"><img src="https://agentmods.dev/badge/skills/maudeunfledged834/startup-founder-skills/lead-scoring/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/maudeunfledged834/startup-founder-skills/lead-scoring"><img src="https://agentmods.dev/badge/skills/maudeunfledged834/startup-founder-skills/lead-scoring.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 | $0.00060 | $0.01752 |
| Opus 5 | $0.00030 | $0.00876 |
| Sonnet 5 | $0.00012 | $0.00350 |
| Haiku 4.5 | $0.00006 | $0.00175 |
Grade A, and why
lead-scoring 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 12d 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.
This is a copy
100% identical to lead-scoring — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Scoring
When to Use
Activate when a founder needs to evaluate inbound prospects against ICP criteria, build a systematic qualification workflow, score and route leads, establish MQL/SQL definitions, or design pipeline stages. Also use when the user says "which leads should I focus on," "how do I qualify inbound leads," "define my ICP," "set up lead scoring," or "how do I route leads to the right person."
Context Required
From startup-context or the user:
- ICP definition — Who is the ideal customer (company size, industry, stage, geography, use case)
- Lead sources — Where inbound leads come from (website, events, content, referrals)
- CRM and tooling — Current stack for managing leads and deals
- Current customers — Who are the best existing customers and why
- Pipeline data — Existing deals, active customers, prior contacts
- Sales capacity — Who handles leads and what is their bandwidth
Work with whatever the user provides. If they have a clear problem area, start there. Do not block on missing inputs.
Workflow
- Load ICP and configuration — Read startup-context if available. Establish the qualification criteria across company attributes, person attributes, and use case fit.
- Parse the lead data — Accept leads in any format (CSV, list, CRM export, single name). Identify data gaps and flag what needs enrichment.
- Check pipeline overlap — Before scoring, check for existing customers (route to upsell), active deals (flag for sales coordination), and prior contacts (note history). Pipeline overlaps are routing flags, not disqualifiers.
- Score company fit — Evaluate against company size, industry, stage, geography, and use case alignment. Weight each dimension based on what predicts closed-won deals.
- Score person fit — Evaluate title, seniority, department, and decision-making authority. A perfect company with the wrong contact still needs routing, not rejection.
- Score use case alignment — Connect the lead's inferred intent to specific product capabilities. Inbound signals (demo requests, pricing page visits) tip borderline cases toward qualification.
- Generate composite score and verdict — Produce a 0-100 composite score and assign a routing recommendation.
- Export structured output — Deliver results in a table or CSV with all qualification data, scores, and routing.
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.
- 12d ago First seen · 111 lines · 60 tokens per session scan A cb6d88ce67dd
lead-scoring is a skill published in the GitHub repository Maudeunfledged834/startup-founder-skills (6 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,752 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to lead-scoring, differing in 0 lines, and is treated as a copy.
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