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 NimbleBrainInc/upjack --skill lead-qualificationgit clone --depth 1 https://github.com/NimbleBrainInc/upjackWrote 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/nimblebraininc/upjack/lead-qualification)<a href="https://agentmods.dev/skills/nimblebraininc/upjack/lead-qualification"><img src="https://agentmods.dev/badge/skills/nimblebraininc/upjack/lead-qualification/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/nimblebraininc/upjack/lead-qualification"><img src="https://agentmods.dev/badge/skills/nimblebraininc/upjack/lead-qualification.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 | $0.00000 | $0.00400 |
| Opus 5.5 | $0.00000 | $0.00160 |
| Sonnet 5.5 | $0.00000 | $0.00080 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
lead-qualification 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 1mo 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.
What it actually says
Lead Qualification
Score and qualify new contacts based on their profile and engagement signals.
When to Use
- A new contact is created (triggered automatically via hook)
- Nightly lead scoring refresh (triggered via schedule)
- User asks to evaluate or score a contact
Process
-
Gather signals from the contact record:
- Job title and seniority (C-suite, VP, Director = high signal)
- Company size and industry fit
- Email domain (corporate vs. personal)
- LinkedIn presence
- Existing relationships to other entities
-
Score the contact (0-100):
- 80-100: Hot lead — immediate follow-up needed
- 60-79: Warm lead — nurture with targeted content
- 40-59: Neutral — monitor for engagement
- 0-39: Cold — low priority
-
Update the contact with:
lead_score: Numeric scorelifecycle_stage: Based on score (lead, mql, sql)tags: Add relevant tags (e.g., "hot-lead", "decision-maker")
-
Log an activity explaining the scoring rationale
Scoring Rubric
| Signal | Points |
|---|---|
| C-suite title | +25 |
| VP/Director title | +15 |
| Manager title | +10 |
| Company size > 200 | +15 |
| Company size 50-200 | +10 |
| Corporate email domain | +10 |
| LinkedIn profile present | +5 |
| Multiple relationships | +10 |
| Recent activity (7 days) | +10 |
Rules
- Never decrease a lead score by more than 20 points in a single update
- Always explain score changes in an activity note
- If enrichment bundle is available, use
enrich_personbefore scoring
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.
- 1mo ago First seen · 52 lines · 0 tokens per session scan A 1d6bfb360fea
lead-qualification is a skill published in the GitHub repository NimbleBrainInc/upjack (11 stars, last pushed 6mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 400 tokens. 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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