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 serejaris/personal-corp-os --skill meeting-copilotgit clone --depth 1 https://github.com/serejaris/personal-corp-osWrote 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/serejaris/personal-corp-os/meeting-copilot)<a href="https://agentmods.dev/skills/serejaris/personal-corp-os/meeting-copilot"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/meeting-copilot/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/serejaris/personal-corp-os/meeting-copilot"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/meeting-copilot.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 | $0.00071 | $0.01283 |
| Opus 5 | $0.00036 | $0.00642 |
| Sonnet 5 | $0.00014 | $0.00257 |
| Haiku 4.5 | $0.00007 | $0.00128 |
Grade A, and why
meeting-copilot 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Copilot
Create and maintain a local HTML dashboard for a live meeting. The dashboard gives the user a second-screen view of context, questions, topic progress, decisions, risks, and follow-ups while the call is happening.
This skill is designed for private workspaces. Do not publish raw transcripts, client names, personal notes, or generated meeting artifacts unless the user explicitly asks for a sanitized export.
Modes
Use one of three modes:
| Mode | When | Output |
|---|---|---|
| CREATE | Before the meeting | A local dashboard app with prepared context and questions |
| UPDATE | During the meeting | Updated questions, topics, decisions, risks, and follow-ups from transcript chunks |
| CLOSE | After the meeting | Final summary, action items, CRM or notes updates, and optional sanitized export |
CREATE
Inputs:
- meeting title or contact name
- meeting type, for example discovery, sales, mentoring, support, hiring, partnership
- date
- available context files, if any
- output directory
Create this structure:
YYYY-MM-DD-meeting-copilot/
app/
index.html
app.js
components.js
styles.css
tabs/
briefing.js
questions.js
topics.js
decisions.js
followups.js
state/
transcript.txt
diff.py
Dashboard tabs:
briefing.js: meeting goal, known context, participants, constraintsquestions.js: grouped live questionstopics.js: planned and discussed topicsdecisions.js: decisions, risks, blockers, open loopsfollowups.js: action items, owners, due dates, next message draft
If the app uses ES modules, serve it over HTTP:
cd YYYY-MM-DD-meeting-copilot/app
python3 -m http.server 8080
Then open http://127.0.0.1:8080.
UPDATE
Input is usually a full transcript copied from a transcription tool. Treat it as sensitive.
Use suffix diffing so the agent processes only the new part:
#!/usr/bin/env python3
import pathlib
import sys
baseline = pathlib.Path(__file__).parent / "transcript.txt"
old = baseline.read_text() if baseline.exists() else ""
new = sys.stdin.read()
old_s = old.strip()
new_s = new.strip()
if not old_s:
sys.stdout.write(new)
elif new_s.startswith(old_s):
sys.stdout.write(new_s[len(old_s):].lstrip())
else:
sys.stderr.write("[diff] baseline mismatch; using full transcript\n")
sys.stdout.write(new)
What ships with it
3 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 · 193 lines · 71 tokens per session scan A 24a7f56b1a54
meeting-copilot is a skill published in the GitHub repository serejaris/personal-corp-os (225 stars, last pushed 16d ago), licensed MIT. It adds 71 tokens to every session and 1,283 once invoked, about $0.0004 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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