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 agentmods add skills/senda-labs/dqiii8/transcript-learnnpx skills add senda-labs/DQIII8 --skill transcript-learngit clone --depth 1 https://github.com/senda-labs/DQIII8Wrote 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/senda-labs/dqiii8/transcript-learn)<a href="https://agentmods.dev/skills/senda-labs/dqiii8/transcript-learn"><img src="https://agentmods.dev/badge/skills/senda-labs/dqiii8/transcript-learn.svg" alt="Measured on agentmods" 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 | $0.00057 | $0.00838 |
| Opus 5 | $0.00028 | $0.00419 |
| Sonnet 5 | $0.00011 | $0.00168 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
transcript-learn 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 5d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/transcript-learn — Video Knowledge Ingestion
Extract structured knowledge from video transcripts and integrate into DQIII8.
Usage
/transcript-learn <youtube-url>
/transcript-learn <path-to-transcript.txt>
/transcript-learn --batch <file-with-urls.txt>
Pipeline
Step 1: Get transcript
- If YouTube URL: use youtube-transcript MCP tool
get_transcript(without timestamps for long videos) - If .txt file: read directly
- If transcript > 25000 tokens: process in a forked subagent to save context
Step 2: Analyze content
Extract from the transcript:
- Key concepts — main ideas, definitions, frameworks
- Techniques/methods — actionable processes, step-by-step workflows
- Tools/technologies — software, libraries, APIs mentioned
- Data/metrics — numbers, benchmarks, comparisons
- Quotes — notable statements with timestamp if available
- Action items — things we could implement in DQ or projects
Step 3: Classify domain
Map content to DQ domain:
formal_sciences: math, logic, algorithms, statisticsnatural_sciences: biology, physics, chemistry, nutritionsocial_sciences: finance, marketing, law, economicshumanities_arts: writing, philosophy, history, designapplied_sciences: programming, devops, web dev, AI/ML
Step 4: Generate output
Based on content type, generate ONE OR MORE of:
A) Knowledge chunk (factual knowledge):
Save to: knowledge/{domain}/{topic_slug}.md
Format: use template .claude/skills/transcript-learn/templates/knowledge_chunk.md
B) Skill proposal (workflow/methodology):
Save to: skills-registry/custom/proposed/{skill_name}.md
Format: standard SKILL.md with frontmatter
Status: PENDING_REVIEW
C) Agent proposal (deep expertise):
Save to: .claude/agents/proposed/{agent_name}.md
Format: standard agent .md with frontmatter
Status: PENDING_REVIEW — user must approve before activation
Step 5: Index
After saving knowledge chunk:
- Add to the domain's
index.jsonif it exists - Print summary of what was generated
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.
- 5d ago First seen · 105 lines · 57 tokens per session scan A 350cde02ae1f
transcript-learn is a skill published in the GitHub repository senda-labs/DQIII8 (11 stars, last pushed 17d ago), licensed MIT. It adds 57 tokens to every session and 838 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
continuous-learning
Pattern extraction, confidence-scored evaluation, skill creation, organization, versioning, and cross-project export pipeline.
continuum-quickstart
Get a Continuum agent up and running — Python 3.13 venv, infra via continuum up, smallest possible BaseAgent + AgentRunner example. Invoke when the user asks "how do I start", "set up Continuum", "run my first agent", or is at the very beginning of a project.
skill-builder
Use when automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
ai-engineering-curriculum
Use when structured AI engineering curriculum — 382 skills + 99 prompts across 20 phases covering ML, deep learning, LLMs, agents, and production systems. Use when learning AI, building AI skills,.
explain
코드를 비유 기반으로 설명하고 Mermaid 다이어그램을 생성합니다. 파일/기능/줌아웃 3가지 모드 지원. /explain @file.ts (상세 설명), /explain "로그인 기능" (기능 단위), /explain --zoom-out @file.ts (한 단계 위 맵).
book-to-skill
Use when convert technical books and documents (PDF, EPUB, DOCX, HTML, Markdown, RTF, MOBI) into structured agent skills with frameworks, mental models, chapter references, and decision rules. Includes full extraction pipeline. Use when the user wants to turn a book or document collection into a reusable agent skill…