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 archetypeai/agent-skills --skill atai-manual-generation-agentgit clone --depth 1 https://github.com/archetypeai/agent-skillsWrote 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/archetypeai/agent-skills/atai-manual-generation-agent)<a href="https://agentmods.dev/skills/archetypeai/agent-skills/atai-manual-generation-agent"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-manual-generation-agent/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/archetypeai/agent-skills/atai-manual-generation-agent"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-manual-generation-agent.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 | $0.00221 | $0.07500 |
| Opus 5 | $0.00111 | $0.03750 |
| Sonnet 5 | $0.00044 | $0.01500 |
| Haiku 4.5 | $0.00022 | $0.00750 |
Grade A, and why
atai-manual-generation-agent 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 12d 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 — 478 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MGA Agent — Managed Manual Generation via the Agents API
The MGA agent turns a procedure video into an ordered manual with timestamps. You hand the platform an .mp4; it samples frames, transcribes the audio, fuses both in one pass, and returns steps you can trace back to the recording:
video ─► sample frames ─┐
├─► newton-fusion f1-0 ─► parse ─► steps + timestamps
whisper ASR ────┘
MGA V1 is zero-shot. The mga blueprint pins its own models (newton-fusion:1.0 and whisper:large-v3), so unlike osm/red there is no classifier to fit and no artifacts map to pass. A run is upload → bundle → run → download.
Availability. The canonical
mgablueprint resolves by key on the production deployment (https://api.u1.archetypeai.app) — setATAI_API_ENDPOINTto it and the full upload → bundle → run → score cycle works as documented here. The same 173 s video reproduces an identical manual (18/18 instruction texts and timestamps) run to run, at the same ~15 min runtime. A verified run: job time 879 s — 13 s queued, whisper 31.9 s download + 3.2 s load, newton-fusion 4min55s download + 1min39s load, then 444 s to process a 173 s video (~2.6× realtime). If the blueprint key doesn't resolve, contact [email protected].
When to Apply
Use when the user wants a written procedure extracted from a recording — a maintenance manual from a repair video, an SOP from a screen capture, work instructions from an assembly clip — and wants each step tied to a time range in the source.
Do NOT use when:
| Need | Use instead |
|---|---|
| Was this task done correctly, against a known procedure? | the tva (Task Verification) blueprint |
| One question about a clip, stateless | atai-newton-fusion-model (/query) |
| Classify sensor windows into operational states | atai-operational-state-monitoring-agent |
| The video is longer than ~5 minutes | chunk it first — see Step 1 |
What ships with it
11 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.
- references/.env.example 542 B
- references/requirements.txt 91 B
- references/run_mga_agent.py 20 KB runs code
- references/sample_data/40567_i2JWkDyg26A_reference_steps.csv 414 B
- references/sample_data/mga-output-current-16384.json 3.3 KB
- references/sample_data/mga-output-current-4096-EMPTY.json 38 B
- references/sample_data/mga-output-max_new_tokens2048-coverage-prompt.json 3.2 KB
- references/sample_data/mga-output-max_new_tokens2048.json 1.9 KB
- references/sample_data/mga-output-truncated-active-blueprint.json 1.1 KB
- references/sample_data/README.md 3.5 KB
- tests/test_references.py 17 KB runs code
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.
- 12d ago First seen · 478 lines · 221 tokens per session scan A a6e8921fcd50
atai-manual-generation-agent is a skill published in the GitHub repository archetypeai/agent-skills (5 stars, last pushed today), licensed Apache-2.0. It adds 221 tokens to every session and 7,500 once invoked, about $0.0011 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.
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