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 samkawsarani/sams-product-plugins --skill analyze-meetingsgit clone --depth 1 https://github.com/samkawsarani/sams-product-pluginsWrote 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/samkawsarani/sams-product-plugins/analyze-meetings)<a href="https://agentmods.dev/skills/samkawsarani/sams-product-plugins/analyze-meetings"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-meetings/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/samkawsarani/sams-product-plugins/analyze-meetings"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-meetings.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 | $0.00147 | $0.01430 |
| Opus 5 | $0.00073 | $0.00715 |
| Sonnet 5 | $0.00029 | $0.00286 |
| Haiku 4.5 | $0.00015 | $0.00143 |
Grade A, and why
analyze-meetings 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 11d 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Arguments: $ARGUMENTS
Instructions
Step 1: Resolve the Meetings Path
- If
$ARGUMENTSis provided, use it as the meetings folder path- Expand
~if needed:bash -c 'echo <path>' - Verify the folder exists and has readable files:
ls "<resolved-path>" - If the folder doesn't exist, tell the user and stop
- Expand
- If no path was provided, ask: "Please provide the path to your meetings folder (e.g.
~/meetings/)."
Step 2: Discover Available Data
Scan the folder for transcript files (.txt, .md, .vtt, .srt, .docx):
ls "<meetings-path>"
- Confirm how many files and what date range they cover
- Check whether files contain speaker labels (e.g.
Sam:,[Sam],Speaker 1:) - Identify the user's name or label in the transcripts — ask if unclear
If no transcript files are found, tell the user what formats are supported and stop.
Step 3: Clarify Analysis Goals
If the user hasn't specified what to look for, ask what they want to learn:
- Specific behaviors: conflict avoidance, interruptions, filler words
- Communication effectiveness: clarity, directness, listening
- Meeting facilitation and leadership style
- Speaking ratios and turn-taking patterns
- Tracking improvement over time
Step 4: Read and Analyze Transcripts
Read each relevant transcript file. For each requested analysis dimension:
Conflict Avoidance
- Hedging language: "maybe", "kind of", "I think", "sort of", "potentially"
- Indirect asks instead of direct requests
- Changing subject when tension arises
- Non-committal agreement: "yeah, but...", "whatever you think"
- Not naming obvious problems when they come up
Speaking Ratios
- Estimate percentage of each meeting the user is speaking
- Count interruptions (both by and of the user)
- Note question vs. statement ratio
- Track average speaking turn length
Filler Words
- Count: "um", "uh", "like", "you know", "actually", "basically", etc.
- Frequency per speaking turn, and in which situations they spike
What ships with it
1 file 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.
- 11d ago First seen · 197 lines · 147 tokens per session scan A 920509db87f0
analyze-meetings is a skill published in the GitHub repository samkawsarani/sams-product-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 147 tokens to every session and 1,430 once invoked, about $0.0007 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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