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 koinod/koino-skills --skill lead-scorergit clone --depth 1 https://github.com/koinod/koino-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/koinod/koino-skills/lead-scorer)<a href="https://agentmods.dev/skills/koinod/koino-skills/lead-scorer"><img src="https://agentmods.dev/badge/skills/koinod/koino-skills/lead-scorer/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/koinod/koino-skills/lead-scorer"><img src="https://agentmods.dev/badge/skills/koinod/koino-skills/lead-scorer.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 | $0.00012 | $0.01998 |
| Opus 5 | $0.00006 | $0.00999 |
| Sonnet 5 | $0.00002 | $0.00400 |
| Haiku 4.5 | $0.00001 | $0.00200 |
Grade A, and why
lead-scorer 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.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Lead Scorer
Score any lead 0-100 and get a clear action plan. Stop guessing which leads to call first -- let data and pattern matching decide.
Usage
Provide lead information in any format. The more you provide, the better the score:
Required Fields
| Field | Example |
|---|---|
| Lead Name | "Marcus Webb" |
| Company | "TerraForce Industrial" |
| Source | "Inbound -- downloaded pricing PDF" |
Optional Fields (improve accuracy)
| Field | Example |
|---|---|
| Title/Role | "Director of Operations" |
| Company Size | "85 employees, ~$12M revenue" |
| Industry | "Industrial equipment manufacturing" |
| Interaction History | "Opened 3 emails, attended webinar, asked about pricing" |
| Budget Signals | "Mentioned Q2 budget approval" |
| Timeline | "Looking to implement by June" |
| Pain Points | "Current system requires manual data entry across 4 platforms" |
| Competition | "Currently evaluating us and 2 competitors" |
| Notes | "Referred by existing client. Seemed frustrated with current vendor." |
Scoring Framework
Category Weights (default)
| Category | Weight | What It Measures |
|---|---|---|
| Fit | 30% | How well do they match your ideal customer? |
| Intent | 25% | How much buying behavior have they shown? |
| Authority | 20% | Can this person actually make or influence the decision? |
| Timing | 15% | Is there urgency or a deadline driving them? |
| Engagement | 10% | How actively are they interacting with you? |
You can customize these weights. Just specify your preferred weights and they'll be applied.
Fit Score (0-30 points)
| Signal | Points |
|---|---|
| Company size matches ICP | +5-10 |
| Industry is in your sweet spot | +5-10 |
| They have the problem you solve | +5-10 |
| Geography/market alignment | +2-5 |
| Tech stack compatibility (if relevant) | +2-5 |
| Revenue range matches your pricing | +3-5 |
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 · 246 lines · 12 tokens per session scan A ca0eabecd727
lead-scorer is a skill published in the GitHub repository koinod/koino-skills (8 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 1,998 once invoked, about $0.0001 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 2026-08-31.
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