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 dagthomas/comfyui_dagthomas --skill h3-base-formatgit clone --depth 1 https://github.com/dagthomas/comfyui_dagthomasWrote 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/dagthomas/comfyui_dagthomas/h3-base-format)<a href="https://agentmods.dev/skills/dagthomas/comfyui_dagthomas/h3-base-format"><img src="https://agentmods.dev/badge/skills/dagthomas/comfyui_dagthomas/h3-base-format/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/dagthomas/comfyui_dagthomas/h3-base-format"><img src="https://agentmods.dev/badge/skills/dagthomas/comfyui_dagthomas/h3-base-format.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00115 | $0.00882 |
| Opus 5.5 | $0.00046 | $0.00353 |
| Sonnet 5.5 | $0.00023 | $0.00176 |
| Haiku 4.5 | $0.00012 | $0.00088 |
Grade A, and why
h3-base-format 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
H3 base format (T2VA / I2VA / FL2VA / L2VA)
This is what the APNext H3 Prompt Writer and APNext H3 Claude Code Writer nodes
expect back. The node splits the text on these three labels, so they must appear exactly
once, in this order, each followed by one blank line:
integrated_multimodal_description: [Shot 1] <style>, <one continuous timeline> ...
overall_soundscape: ...
non_diegetic_music: ...
The stated visual style opens [Shot 1] (the node names it, or asks you to choose one).
Alignment lines
T2VA begins directly with integrated_multimodal_description:. The other modes put one
alignment line first, then exactly one blank line, then the three fields. Copy the wording
character for character; only N (the number of the final shot) and S.SS (the duration,
two decimals) change.
I2VA:
For the target video, at 0.00 seconds into the target video, <Picture 1> (from [Shot 1]) is fully referenced.
FL2VA:
How the reference pictures align with the target video — Picture 1 (from Shot 1) aligns with the 0.00-second mark of the target video; Picture 2 (from Shot N) aligns with the S.SS-second mark of the target video.
L2VA:
How the reference pictures align with the target video — <Picture 1> (from [Shot N]) aligns with the S.SS-second mark of the target video.
The node's own directive repeats the right line for the chosen task type; if it and this file ever differ, follow the node.
How the node's images map
The node sends whatever was connected to its image socket as an ordered batch and hands
the same frames back out as first_frame and last_frame for the video node:
- I2VA - the image is
<Picture 1>, the exact opening frame. Preserve its identity, clothing, colours, key objects and geography, then develop forward from it. - L2VA - the image is
<Picture 1>, the exact final frame, and belongs to the last shot. Infer a plausible earlier state, then a transition path, then a gradual convergence, then land on the image. - FL2VA - frame 0 of the batch is
<Picture 1>(opening), the last frame is<Picture 2>(ending). Describe the change between them and land exactly on the ending. - T2VA with an image attached - the image is visual context to describe from, not a
keyframe. No alignment line, no
<Picture N>labels.
What ships with it
3 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.
- 1mo ago First seen · 67 lines · 115 tokens per session scan A 3c93ca9e99fa
h3-base-format is a skill published in the GitHub repository dagthomas/comfyui_dagthomas (291 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 882 once invoked, about $0.0005 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 2026-08-30.
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