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 aleksander-dytko/ai-pm-workspace --skill meetinggit clone --depth 1 https://github.com/aleksander-dytko/ai-pm-workspaceWrote 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/aleksander-dytko/ai-pm-workspace/meeting)<a href="https://agentmods.dev/skills/aleksander-dytko/ai-pm-workspace/meeting"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/meeting/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/aleksander-dytko/ai-pm-workspace/meeting"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/meeting.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 | $0.00015 | $0.01310 |
| Opus 5.5 | $0.00006 | $0.00524 |
| Sonnet 5.5 | $0.00003 | $0.00262 |
| Haiku 4.5 | $0.00002 | $0.00131 |
Grade A, and why
meeting 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process Meeting Notes
You are helping process raw meeting notes into a structured meeting note. Action items for the user land in Dashboard/tasks.md.
Input Modes
The skill operates in one of three modes based on $ARGUMENTS:
| Mode | Arguments | When to use |
|---|---|---|
| Browse | (none) or browse |
List recent files in Meetings/ that look unprocessed |
| Specific file | File path or partial name | Process notes from a specific file |
| Paste | Pasted text (long string) | Meeting notes pasted directly into the prompt |
Workflow
Step 0: Determine mode and get raw notes
If no arguments (Browse mode):
- Use the
Globtool with patternMeetings/*.mdto list meeting files. Do NOT useBash ls- Glob is quieter and shows results already sorted. - Read the top N candidates (up to 5) in parallel and identify files with raw/unstructured notes (no "## Decisions", no structured format).
- Show a numbered list of unprocessed files.
- Ask: "Which meeting do you want to process?"
If specific file path:
- Read the file.
- If already structured (has Decisions, Action Items sections): warn and ask to confirm before overwriting.
- If raw notes: proceed directly.
If pasted text:
- Use the pasted text as raw input.
- Ask for meeting date and title if not obvious from the text.
Step 1: Analyze raw notes
Extract:
- Meeting title (from filename or content)
- Date (from filename or content)
- Attendees (mentioned names)
- Key topics discussed
- Decisions made (explicit or implicit)
- Action items (tasks, follow-ups, commitments - with owners)
- Open questions (unresolved topics)
Step 2: Cross-reference attendees
- Read
Dashboard/people-profiles.mdto verify attendee names and roles. - Use tags (e.g.,
#FirstName) when mentioning people.
Step 3: Search for related context
- Search
Loose Notes/Work/for related decisions or notes. - Check
Dashboard/Weekly P-Tasks.mdfor related P-tasks. - Identify which existing notes this meeting relates to - used in the "Related" section.
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 First seen · 145 lines · 15 tokens per session scan A d41be1ac9dac
meeting is a skill published in the GitHub repository aleksander-dytko/ai-pm-workspace (34 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 1,310 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.
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