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 | $0.00024 | $0.01615 |
| Opus 5.5 | $0.00010 | $0.00646 |
| Sonnet 5.5 | $0.00005 | $0.00323 |
| Haiku 4.5 | $0.00002 | $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 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.
How it starts
The opening of the file, as written. The whole thing — 160 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
Example Requests
- Score my booth leads
- 给展会线索打分
- Leads qualifizieren
- リードを評価する
- calificar leads de feria
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.
Use supplied contact information only in the requested lead records. Do not upload badge images, notes, emails, or phone numbers to enrichment services or a CRM unless the user has authorized that destination. If sharing a worked example or public report, replace personal contact details with placeholders.
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 | Explicit role clues in notes | A title alone does not establish buying involvement |
| 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.
- 1mo ago Changed · +7 lines · -71 tokens per session c70f67ce48da
- 2mo 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 (49 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 1,615 once invoked, about $0.0001 per session on Opus 5.5. 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.
badge-qualifier compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| badge-qualifierLensmorOfficial/trade-show-skills | Skill | 49 | 1mo ago | MIT | A | 24 | Claude Code, Codex |
| mcp-host-styling-integrationa5c-ai/babysitter | Skill | 1.9k | 5d ago | MIT | A | 44 | Claude Code |
| cog-knowledge-consolidationa5c-ai/babysitter | Skill | 1.9k | 5d ago | MIT | A | 19 | Claude Code |
| formula-authoringa5c-ai/babysitter | Skill | 1.9k | 5d ago | MIT | A | 28 | Claude Code |
| quality-hooksa5c-ai/babysitter | Skill | 1.9k | 5d ago | MIT | A | 53 | Claude Code |
| ops-arLifecycle-Innovations-Limited/claude-ops | Skill | 548 | 4d ago | MIT | A | 29 | Claude Code |
| ops-marketingLifecycle-Innovations-Limited/claude-ops | Skill | 548 | 4d ago | MIT | A | 29 | Claude Code |