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 LensmorOfficial/trade-show-skills --skill badge-qualifiergit clone --depth 1 https://github.com/LensmorOfficial/trade-show-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/lensmorofficial/trade-show-skills/badge-qualifier)<a href="https://agentmods.dev/skills/lensmorofficial/trade-show-skills/badge-qualifier"><img src="https://agentmods.dev/badge/skills/lensmorofficial/trade-show-skills/badge-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/lensmorofficial/trade-show-skills/badge-qualifier"><img src="https://agentmods.dev/badge/skills/lensmorofficial/trade-show-skills/badge-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 | $0.00095 | $0.01614 |
| Opus 5 | $0.00048 | $0.00807 |
| Sonnet 5 | $0.00019 | $0.00323 |
| Haiku 4.5 | $0.00010 | $0.00161 |
Grade A, and why
badge-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 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Badge Qualifier
Transform raw booth conversation notes into a structured lead record — including tier, authority, fit, and next step — without inflating signals that aren't there.
When this skill triggers:
- Use it during the show or immediately after to triage leads while the conversation is still fresh
- Use it for live single-lead decisions or end-of-day batch qualification
- Do not use it to write the outbound sequence itself; hand the result to
post-show-followup
Workflow
Step 1: Normalize Raw Input
Accept any of these input formats:
- Typed booth notes ("Spoke with Sarah at Acme, she asked about pricing for 5 lines")
- Badge or business card OCR text (name, title, company, contact details)
- Voice transcript or dictated summary
- A mix of all three
If the user pastes badge text only, treat it as contact-only — do not infer conversation depth that wasn't described.
Extract and confirm these fields before proceeding:
- Contact name (badge or notes; unknown if absent)
- Job title (badge; unknown if absent)
- Company (badge; unknown if absent)
- How contact was made (scanned badge / brief chat / product demo / pricing discussion)
If critical fields are missing and the user is in a live session, ask a single clarifying question. If processing in bulk, mark as unknown and continue.
Step 2: Extract Structured Lead Facts
From the normalized input, pull explicit facts — not inferences:
| Field | Source | Rule |
|---|---|---|
| Name / Title / Company | Badge or notes | Transcribe exactly; mark as unknown if absent |
| Email / Phone | Badge | Transcribe only if present; never fabricate |
| Need | Conversation notes | Only quote if explicitly stated; otherwise unknown |
| Urgency | Notes ("needs by Q3", "replacing system now") | Only when a timeline is given |
| Authority | Title + explicit role clues | Infer conservatively (see tier rules below) |
| Budget signal | Notes only | Only if the contact or rep mentioned it |
| ICP fit | Compare to ICP criteria if provided | Low / Medium / High; explain why |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 153 lines · 95 tokens per session scan A 8f57a5374822
badge-qualifier is a skill published in the GitHub repository LensmorOfficial/trade-show-skills (48 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 1,614 once invoked, about $0.0005 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 2026-08-30.
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