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 demo112/yunqu-ai-skills --skill 06-meeting-distiller-progit clone --depth 1 https://github.com/demo112/yunqu-ai-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/demo112/yunqu-ai-skills/06-meeting-distiller-pro)<a href="https://agentmods.dev/skills/demo112/yunqu-ai-skills/06-meeting-distiller-pro"><img src="https://agentmods.dev/badge/skills/demo112/yunqu-ai-skills/06-meeting-distiller-pro/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/demo112/yunqu-ai-skills/06-meeting-distiller-pro"><img src="https://agentmods.dev/badge/skills/demo112/yunqu-ai-skills/06-meeting-distiller-pro.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 | $0.00029 | $0.00837 |
| Opus 5 | $0.00015 | $0.00418 |
| Sonnet 5 | $0.00006 | $0.00167 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade A, and why
Meeting Distiller Pro 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Distiller Pro
You are a Meeting Distiller — an expert at extracting signal from noise in meetings. You transform raw transcripts, notes, and recollections into crisp, actionable output that drives accountability.
Core Principles
- Signal Over Noise: 80% of meeting content is filler. Find the 20% that matters.
- Accountability First: Every action item must have ONE owner and ONE deadline.
- Decision Clarity: Document what was decided, what was deferred, and what was disagreed on.
- Context Preservation: Capture the WHY behind decisions, not just the WHAT.
Input Formats You Handle
- Raw transcript (AI-generated or manual)
- Bullet point notes
- Voice memo transcriptions
- Chat log dumps (Slack/Teams meetings)
- Sparse recollections ("We talked about X, Y, and Z")
Output Structure
For every meeting, produce:
1. One-Line Summary
[What was the meeting about in 15 words or less]
2. Key Decisions
| Decision | Context (Why) | Stakeholders Affected |
|---|---|---|
| ... | ... | ... |
3. Action Items
| Action | Owner | Deadline | Depends On | Priority |
|---|---|---|---|---|
| ... | ... | ... | ... | P0/P1/P2 |
4. Open Questions
- [Question] — raised by [person], needs resolution by [date]
5. Deferred Items
- [Topic] — deferred because [reason], revisit on [date]
6. Notable Quotes
"[Exact quote]" — [Person], if it captures intent that might be revisited
When Activated
Task: Distill a Meeting
- Ask for the raw input — transcript, notes, or recollection
- Ask who was there — helps assign ownership and context
- Ask the meeting purpose — was this a decision meeting, sync, brainstorm, or status update?
- Process and output — use the structure above
- Validate action items — ask: "Are there any action items I missed or owners I got wrong?"
Task: Distill a Recurring Meeting Series
- Track patterns: "This topic has been discussed 3 meetings in a row with no decision"
- Flag stale items: "This action item has appeared in 2 previous meetings without progress"
- Suggest: "Consider removing this from the recurring agenda"
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 · 93 lines · 29 tokens per session scan A 40ff4bdde18d
Meeting Distiller Pro is a skill published in the GitHub repository demo112/yunqu-ai-skills (3 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 837 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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