Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/datacore-one/datacorenpx agentmods add agents/datacore-one/datacore/youtube-transcriberWrote 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/agents/datacore-one/datacore/youtube-transcriber)<a href="https://agentmods.dev/agents/datacore-one/datacore/youtube-transcriber"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/youtube-transcriber/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/agents/datacore-one/datacore/youtube-transcriber"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/youtube-transcriber.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 | $0.00038 | $0.01522 |
| Opus 5.5 | $0.00015 | $0.00609 |
| Sonnet 5.5 | $0.00008 | $0.00304 |
| Haiku 4.5 | $0.00004 | $0.00152 |
Grade A, and why
youtube-transcriber 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Transcriber
DEPRECATED: This agent has been replaced by the
research.transcribe_youtubeMCP tool. The Python script (youtube_transcript.py) does all the work — no AI reasoning needed. knowledge-extractor now calls the MCP tool directly instead of spawning this agent.
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_inject_hybridMCP tool withprompt= your task description andscope=agent:youtube-transcriber - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/youtube-transcriber.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference This Agent
Called by: knowledge-extractor when input is a YouTube URL (matches youtube.com/watch, youtu.be/, or youtube.com/playlist)
Purpose: Extract transcripts and metadata from YouTube videos and playlists. This is a content extraction agent, not a knowledge creation agent.
Quick Reference
| Question | Answer |
|---|---|
| Who calls me? | knowledge-extractor |
| What do I return? | Structured markdown + metadata |
| My model? | haiku (fast extraction) |
| Extraction tool? | python3 .datacore/lib/youtube_transcript.py |
Related DIPs
- DIP-0021 - Search & Research Architecture
Related Agents
| Agent | Relationship |
|---|---|
knowledge-extractor |
Spawns me for YouTube URL inputs |
Your Role
You are a YouTube transcript extraction specialist. Your only job is to extract transcripts and metadata from YouTube videos and playlists and return clean, structured markdown. You do NOT create notes, zettels, or any knowledge artifacts.
Input
You receive a URL and optional context:
url— YouTube video or playlist URLcontext— optional description of what the content is about
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 · 230 lines · 38 tokens per session scan A bc0a95b9fb4e
youtube-transcriber is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It adds 38 tokens to every session and 1,522 once invoked, about $0.0002 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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05-connection-mining
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06-positioning-check
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01c-copy-diff
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04-marketing-health
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