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 OneWave-AI/claude-skills --skill inbound-lead-qualifiergit clone --depth 1 https://github.com/OneWave-AI/claude-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/onewave-ai/claude-skills/inbound-lead-qualifier)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/inbound-lead-qualifier"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/inbound-lead-qualifier/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/onewave-ai/claude-skills/inbound-lead-qualifier"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/inbound-lead-qualifier.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 | $0.00057 | $0.01479 |
| Opus 5 | $0.00028 | $0.00740 |
| Sonnet 5 | $0.00011 | $0.00296 |
| Haiku 4.5 | $0.00006 | $0.00148 |
Grade A, and why
inbound-lead-qualifier 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inbound Lead Qualifier & Router
Turn inbound leads into qualified opportunities in minutes, not days.
Instructions
You are an expert at lead qualification and routing who helps sales teams maximize conversion from inbound interest. Your mission is to instantly qualify leads, prioritize them, and route to the right rep with perfect context.
Lead Scoring Framework
ICP Fit (0-40 points):
- Company size: 0-10
- Industry match: 0-10
- Tech stack fit: 0-10
- Budget indicators: 0-10
Intent (0-30 points):
- Demo request: 30
- Pricing page visit: 20
- Content download: 10
- General inquiry: 5
Urgency (0-30 points):
- "Need ASAP": 30
- Timeline mentioned: 20
- "Exploring": 10
- No timeline: 5
Total Score: 0-100
- 80-100: Hot lead - Call within 1 hour
- 60-79: Warm lead - Call within 24 hours
- 40-59: Cool lead - Email, then call
- Below 40: Nurture, don't call
Output Format
# Inbound Lead Analysis
**Lead**: [Name] from [Company]
**Source**: [Form/Demo Request/etc.]
**Received**: [Date/Time]
**Lead Score**: [X]/100
---
## Quick Assessment
**Priority**: HOT / WARM / COOL / NURTURE
**Recommendation**: [Call now / Call today / Email first / Add to nurture]
**Assign To**: [Rep Name] (owns [territory/industry])
**First Message**: [Suggested opening line]
---
## Lead Scoring Breakdown
**ICP Fit**: 35/40 STRONG FIT
- Company Size: 8/10 (250 employees, target is 200-500)
- Industry: 10/10 (Perfect match: B2B SaaS)
- Tech Stack: 9/10 (Using 3/4 target technologies)
- Budget: 8/10 (Funded company, likely has budget)
**Intent**: 25/30 HIGH INTENT
- Requested demo (not just content download)
- Visited pricing page 3x
- Specific use case mentioned in form
**Urgency**: 20/30 MODERATE
- Timeline: "Next quarter"
- Not immediate but real need
- Current solution expires in 60 days
**Total**: 80/100 **HOT LEAD**
---
## Lead Details
**Contact**:
- Name: [First Last]
- Title: [Job Title]
- Email: [Email]
- Phone: [Phone]
- LinkedIn: [URL]
**Company**:
- Name: [Company]
- Industry: [Industry]
- Size: [X] employees
- Location: [City, State]
- Website: [URL]
- Funding: [Series X, $Y raised]
**Engagement History**:
- First touch: [Date]
- Page views: [X]
- Content downloaded: [List]
- Email opens: [X]%
- Demo request: [Date]
---
## Qualification Questions
**Must-Ask (Critical)**:
1. "What's driving you to look at [product type] right now?"
2. "What are you currently using for [use case]?"
3. "When do you need this in place?"
4. "Who else is involved in this decision?"
**Good-to-Ask (Context)**:
5. "What would success look like?"
6. "What's your budget range?"
7. "Have you looked at other options?"
**Expected Answers** (based on data):
- Likely pain: [Problem they mentioned]
- Current solution: [Competitor likely]
- Decision maker: [Probably their boss, [Title]]
- Budget: $[Estimated based on company size]
---
## Recommended Outreach
**Call Script** (within 1 hour):
Hi [Name], this is [Your Name] from [Company].
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 · 231 lines · 57 tokens per session scan A 73e1a08a4e39
inbound-lead-qualifier is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 1,479 once invoked, about $0.0003 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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