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/endogenai/dogma/transcript-extractionnpx skills add EndogenAI/dogma --skill transcript-extractiongit clone --depth 1 https://github.com/EndogenAI/dogmaWhat 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 | $0.00095 | $0.00696 |
| Opus 5 | $0.00048 | $0.00348 |
| Sonnet 5 | $0.00019 | $0.00139 |
| Haiku 4.5 | $0.00010 | $0.00070 |
Grade A, and why
transcript-extraction 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 yesterday.
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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: Transcript Extraction
This skill governs the extraction and cleaning of transcripts from video sources to support the Endogenous-First axiom (MANIFESTO.md § 1) by ensuring all source signals—even non-textual ones—are distilled into searchable, readable Markdown/text artifacts. It operates under the global constraints of AGENTS.md regarding file-writing and local compute.
Beliefs & Context
- Information Density: Video sources often contain unique practitioner insights (demos, conference talks) not found in written docs. Distilling them into text allows for local semantic search and cross-referencing.
- LCF Adherence: By caching transcripts in
.cache/transcripts/, we avoid repeated network hits and high-token external model reads of video URLs. - Artifact Integrity: Transcripts must be cleaned of redundant timestamps and repetitive filler to preserve context window space.
Workflow & Intentions
1. Identify Video Source
Extract the Video ID from the URL (e.g., J5KTpq7hVn4 from https://youtu.be/J5KTpq7hVn4).
2. Verify Cache First
Check if the transcript already exists in the local cache before attempting extraction.
ls .cache/transcripts/video_<VIDEO_ID>.txt
3. Extraction Protocol
Use the designated internal script to pull the transcript. Prefer manually created English transcripts over auto-generated versions when available.
uv run python scripts/pull_yt_transcript.py <VIDEO_ID> .cache/transcripts/video_<VIDEO_ID>.txt
4. Verification Check
Confirm the file is non-empty and contains readable text.
test -s .cache/transcripts/video_<VIDEO_ID>.txt && head -n 5 .cache/transcripts/video_<VIDEO_ID>.txt
Constraints
- UTF-8 Only: All extracted transcripts must be stored as UTF-8 encoded plain text.
- Privacy: Only pull transcripts for public videos.
- Minimal Metadata: Focus on the spoke word; omit frame data or visual descriptions unless critical to the research question.
- Pathing: Always store the output in
.cache/transcripts/to ensure it is gitignored but available for the current session.
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.
- yesterday First seen · 58 lines · 0 tokens per session scan A 96b07a779236
transcript-extraction is a skill published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 8d ago), licensed Apache-2.0. It adds 95 tokens to every session and 696 once invoked, about $0.0005 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.
Other skills, from other repositories
red-team-adversarial
Adversarial security and resilience analysis — auto-triggered during /review and /test based on task classification. Provides attack surface analysis, boundary testing, auth bypass attempts, dependency chain attacks, and Beast Mode stress testing.
auth-security
Secure authentication and authorization when credentials, sessions, roles, or permissions change.
karpathy-principles
Behavioral guidelines to reduce common LLM coding mistakes — Think Before Coding, Simplicity First, Surgical Changes, Goal-Driven Execution.
orloj-generator
Interactive scaffold generator for Orloj multi-agent systems. Use this skill whenever someone wants to create, set up, scaffold, bootstrap, or generate an Orloj agent system, pipeline, swarm, or hierarchy. Also trigger when users mention "orlojctl init", ask how to get started with Orloj, want to build a multi-agent…
api-design
Design or review REST/GraphQL APIs when endpoints or contracts change.
database-design
Design schemas and migrations when persistent data structures change.