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 BreadchainCoop/labor.fun --skill transcript-processorgit clone --depth 1 https://github.com/BreadchainCoop/labor.funWrote 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/breadchaincoop/labor.fun/transcript-processor)<a href="https://agentmods.dev/skills/breadchaincoop/labor.fun/transcript-processor"><img src="https://agentmods.dev/badge/skills/breadchaincoop/labor.fun/transcript-processor/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/breadchaincoop/labor.fun/transcript-processor"><img src="https://agentmods.dev/badge/skills/breadchaincoop/labor.fun/transcript-processor.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 | $0.00019 | $0.02180 |
| Opus 5.5 | $0.00008 | $0.00872 |
| Sonnet 5.5 | $0.00004 | $0.00436 |
| Haiku 4.5 | $0.00002 | $0.00218 |
Grade A, and why
transcript-processor 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcript Processor Skill
Overview
When someone pastes a meeting transcript or asks you to process meeting notes, follow this workflow to extract structured data and produce an HTML slideshow summary.
Detection
Recognize transcript intake when:
- User says "transcript", "meeting notes", "process this meeting", "summarize this transcript"
- A large block of text with multiple speakers, dialogue markers (e.g., "Alice:", timestamps, "Speaker 1:")
- User says "here are the notes from..." followed by substantial text
Processing Steps
1. Parse the Transcript
Read the full text and identify:
- Who spoke: List all unique speakers/participants
- When: Date/time if mentioned
- What was discussed: Group dialogue into topic clusters
2. Extract Structured Items
For each of these categories, scan the transcript thoroughly:
Action Items
Look for commitments: "I'll...", "Let's...", "We need to...", "Can you...", "Action item:", "TODO:"
[{
"description": "What needs to be done",
"assignee": "Who is responsible (or 'unassigned')",
"due_date": "YYYY-MM-DD or null if not mentioned",
"priority": "high/medium/low (infer from urgency language)",
"status": "pending"
}]
New Events
Look for scheduled meetings, deadlines, gatherings: "next Thursday", "schedule a...", "let's meet on..."
[{
"title": "Event name",
"date": "YYYY-MM-DD or description like 'next Friday'",
"time": "HH:MM or null",
"location": "Where, or null",
"description": "Context from transcript"
}]
New People
Cross-reference names against context/people/. Anyone not already in the KB:
[{
"name": "Full name",
"role": "Role if mentioned",
"context": "How they came up in the meeting"
}]
Task Updates
References to existing work: "the website project", "TASK-042", "the thing we discussed last week"
[{
"task_id": "TASK-NNN if identifiable, null otherwise",
"title": "Task title for matching",
"description": "What was said about it",
"assignee": "New or confirmed assignee",
"priority": "Changed priority if discussed",
"status": "New status if discussed"
}]
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 · 215 lines · 19 tokens per session scan A 03bc5f366779
transcript-processor is a skill published in the GitHub repository BreadchainCoop/labor.fun (2 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 2,180 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 2026-08-31.
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