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 seb1n/awesome-ai-agent-skills --skill lead-scoringgit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/lead-scoring)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/lead-scoring"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/lead-scoring"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/lead-scoring.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 | $0.00041 | $0.01740 |
| Opus 5 | $0.00020 | $0.00870 |
| Sonnet 5 | $0.00008 | $0.00348 |
| Haiku 4.5 | $0.00004 | $0.00174 |
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 9d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Scoring
Score and prioritize inbound and outbound leads by combining firmographic fit (how closely a lead matches your ideal customer profile) with behavioral engagement signals (actions that indicate purchase intent). This skill builds scoring rubrics, assigns weighted points, calculates composite scores, and segments leads into actionable tiers — Hot, Warm, and Cold — so sales teams focus time on the highest-converting opportunities.
Workflow
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Define ICP Criteria — Establish the firmographic attributes of your ideal customer: target industries, company size ranges, revenue bands, geographic regions, and technology stack indicators. Each attribute gets a weight reflecting its predictive importance based on historical conversion data.
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Assign Fit Scores — Score each lead's company against ICP criteria. A perfect-fit lead earns maximum fit points; partial matches earn proportional scores. Negative scoring applies for explicit disqualifiers (e.g., company size below minimum threshold, industries you don't serve, students or competitors).
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Track Engagement Signals — Capture behavioral signals from marketing automation, CRM, and product analytics: email opens/clicks, website page visits (especially pricing and case study pages), content downloads, webinar attendance, demo requests, free trial signups, and reply sentiment. Weight each signal by its correlation to closed-won deals.
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Calculate Composite Score — Combine fit score (typically 0–50 points) and engagement score (typically 0–50 points) into a composite score (0–100). Apply decay to engagement signals older than 30 days to ensure the score reflects current intent, not stale activity.
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Rank and Segment into Tiers — Sort leads by composite score and assign tiers: Hot (75–100), Warm (40–74), Cold (0–39). Route Hot leads to SDRs for immediate outreach, Warm leads to nurture sequences, and Cold leads to low-touch automated campaigns. Review tier thresholds quarterly against actual conversion rates and adjust.
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.
- 9d ago First seen · 102 lines · 41 tokens per session scan A 68c3fe3ae05b
lead-scoring is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 1,740 once invoked, about $0.0002 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 2026-09-03.
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