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 post-show-followupgit 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/post-show-followup)<a href="https://agentmods.dev/skills/lensmorofficial/trade-show-skills/post-show-followup"><img src="https://agentmods.dev/badge/skills/lensmorofficial/trade-show-skills/post-show-followup/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/post-show-followup"><img src="https://agentmods.dev/badge/skills/lensmorofficial/trade-show-skills/post-show-followup.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 | $0.00019 | $0.02063 |
| Opus 5.5 | $0.00008 | $0.00825 |
| Sonnet 5.5 | $0.00004 | $0.00413 |
| Haiku 4.5 | $0.00002 | $0.00206 |
Grade A, and why
post-show-followup 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-Show Follow-up
Generate tiered follow-up email sequences that convert trade show conversations into pipeline — sent within the critical 48-hour window when you're still fresh in their memory.
Why This Matters
Many trade show leads never receive timely, relevant follow-up. Of those that do, many get a generic "Great meeting you!" email that goes nowhere. This skill creates targeted sequences based on how warm the lead actually is.
When this skill triggers:
- Use it in the 24-48 hour window after the show, once leads are tiered or at least roughly segmented
- Use it after
badge-qualifierif you want tier logic grounded in actual booth notes - Do not use it to qualify raw leads from scratch; do that first in
badge-qualifier
Example Requests
- Write follow-up emails for my trade show leads
- 帮我写展后跟进邮件
- Messe-Nachfassung schreiben
- 展示会後フォローアップメール
- emails de seguimiento post-feria
Workflow
Step 1: Understand the Context
Extract from the user's request:
Required:
- Show name (just completed or about to end)
- What they were showcasing / selling
Helpful:
- Lead tiers — does the user already have a system? (e.g., hot/warm/cold, or A/B/C)
- Typical deal cycle — quick transactional vs. 6-month enterprise
- CRM they use (affects formatting and merge tags)
- Any specific conversations they want to reference
If the user just says "help me follow up after MEDICA", generate three sequence templates with visible placeholders for missing facts. Do not invent lead counts, conversation details, or qualified tiers.
Step 2: Define Lead Tiers
Preserve supplied qualified tiers. For raw notes, count confirmed buying involvement, explicit need, and a stated timeline: three signals = Hot, two = Warm, zero or one = Cold. A title or resource request alone does not establish buying involvement. Use badge-qualifier when available for a fuller lead card; this skill can apply the same rules without requiring another installed skill.
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 · +2 lines · -86 tokens per session cfa15fd095bb
- 2mo ago First seen · 190 lines · 105 tokens per session scan A 833c1e6a6bdf
post-show-followup is a skill published in the GitHub repository LensmorOfficial/trade-show-skills (49 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 2,063 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.
post-show-followup compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| post-show-followupLensmorOfficial/trade-show-skills | Skill | 49 | 1mo ago | MIT | A | 19 | 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 |