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 glebis/claude-skills --skill agency-meetup-publishgit clone --depth 1 https://github.com/glebis/claude-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/glebis/claude-skills/agency-meetup-publish)<a href="https://agentmods.dev/skills/glebis/claude-skills/agency-meetup-publish"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/agency-meetup-publish/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/glebis/claude-skills/agency-meetup-publish"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/agency-meetup-publish.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Rogue Agent · line 22 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00115 | $0.02071 |
| Opus 5 | $0.00057 | $0.01035 |
| Sonnet 5 | $0.00023 | $0.00414 |
| Haiku 4.5 | $0.00012 | $0.00207 |
Grade A, and why
agency-meetup-publish scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -L -o ~/Brains/brain/YYYYMMDD-meeting-slug.mp4 "DOWNLOAD_URL" How it starts
The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENCY Meetup Publish
Publish AGENCY Community meetup recordings to YouTube with intro, thumbnail, description, and timecodes.
Prerequisites
~/ai_projects/youtube-uploader/— YouTube upload scripts with OAuth credentials~/ai_projects/my-video/out/agency-swarm-intro.mp4— AGENCY intro animation (6s, 1920x1080)- Zoom OAuth configured at
~/.zoom_credentials/ ffmpegandffprobeinstalled- Playwright or Chrome installed (for thumbnail rendering)
Pipeline Overview
1. Identify meeting → 2. Download from Zoom → 3. Add intro/outro
→ 4. Generate timecodes → 5. Write description → 6. Create thumbnail
→ 7. Upload to YouTube → 8. Set thumbnail → 9. Add to playlist
Step 1: Identify the Meeting
Ask the user for:
- Meeting name or Zoom ID — if unknown, list recent recordings:
python3 ~/.claude/skills/zoom/scripts/zoom_meetings.py recordings --start YYYY-MM-DD - Speaker name — for description and thumbnail
- Topic summary — or derive from transcript
Get recording details:
python3 ~/.claude/skills/zoom/scripts/zoom_meetings.py recording MEETING_ID
This returns MP4 download URL, duration, transcript URL, and other files.
Step 2: Download from Zoom
Download MP4 and VTT transcript in parallel:
# Video (run in background — large file)
curl -L -o ~/Brains/brain/YYYYMMDD-meeting-slug.mp4 "DOWNLOAD_URL"
# Transcript
curl -L -o ~/Brains/brain/YYYYMMDD-meeting-slug.vtt "TRANSCRIPT_URL"
Naming convention: YYYYMMDD-meeting-slug.mp4 where slug is a kebab-case topic summary.
Step 3: Add Intro (and Outro if Available)
The intro and meeting likely have different specs. Check both:
ffprobe -v quiet -print_format json -show_streams INTRO.mp4
ffprobe -v quiet -print_format json -show_streams MEETING.mp4
Key parameters to match: resolution, fps, audio sample rate, audio channels.
Re-encode intro to match meeting
ffmpeg -y -i ~/ai_projects/my-video/out/agency-swarm-intro.mp4 \
-vf "scale=WIDTH:HEIGHT:force_original_aspect_ratio=decrease,pad=WIDTH:HEIGHT:(ow-iw)/2:(oh-ih)/2:black" \
-r FPS -c:v libx264 -preset fast -crf 18 \
-ar SAMPLE_RATE -ac CHANNELS -c:a aac \
/tmp/intro-matched.mp4
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 · 224 lines · 115 tokens per session scan A ebdde25fb4fe
agency-meetup-publish is a skill published in the GitHub repository glebis/claude-skills (375 stars, last pushed 10d ago), licensed MIT. It adds 115 tokens to every session and 2,071 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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