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/OSideMedia/higgsfield-ai-prompt-skillnpx agentmods add skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-shotlist-directorWrote 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/osidemedia/higgsfield-ai-prompt-skill/higgsfield-shotlist-director)<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-shotlist-director"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-shotlist-director/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/osidemedia/higgsfield-ai-prompt-skill/higgsfield-shotlist-director"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-shotlist-director.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 | $0.00201 | $0.06008 |
| Opus 5 | $0.00101 | $0.03004 |
| Sonnet 5 | $0.00040 | $0.01202 |
| Haiku 4.5 | $0.00020 | $0.00601 |
Grade A, and why
higgsfield-shotlist-director 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 — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Shotlist Director
Turn a brief into one connected shotlist — not a pile of separate prompts. The deliverable is a single editable HTML artifact the user opens in a browser, ticks scenes off as they shoot, and comes back to you to revise. This is the artifact-shaped workflow a fully-AI commercial actually runs on: lock a global look once, declare the cast/props/locations once, then emit named per-scene prompts that all inherit both.
This skill is the connected layer on top of
higgsfield-seedance. It does not reinvent the prompt grammar — every per-scene prompt obeys the six-slot formula and the Prompt-Craft Laws in../higgsfield-seedance/SKILL.md, and every prompt is preflight-linted before delivery. What this skill adds is the document: the global Style Prefix, the@-glossary, the per-scene numbering, and the edit-once / per-scene-override semantics that keep 25 prompts in sync.
QUICK FACTS
- Output = one self-contained HTML file (inline CSS/JS, no deps), not loose prompts →
- Three structural layers, top to bottom: Global Style Prefix →
@-asset glossary → named per-scene prompts → - Per-scene prompt law:
Style → Characters → Scene → CUT 1..N; each prompt targets 15s; split long scenes as3a/3b/3c→ - [OFFICIAL] Density heuristic: group rows when ALL of {same cast, same location, one emotional unit, ≤15s, inside length limits}; split on ANY of {location cut, cast change, setup change, performance arc, insert} — don't fragment grief; complexity budget + auto-enrichment defaults for thin briefs →
- Whole-sequence checks before delivery: tempo budget (cut durations sum exactly to runtime; one 6–8s hero hold) + monotony audit (no 3 consecutive cuts sharing shot size AND camera move) →
- Continuity carries exits too: an Off-screen line (exit side + last state) per just-departed character keeps re-entry direction legal →
- Edit-once-propagates: change the prefix once → it changes in every prompt; per-scene override lets one scene break the global look →
- Differentiators over a bare shotlist generator: preflight linter, reference-role lanes, Elements
@-auto-attach, failure-mode awareness, acceptance-rate logging → - English prompt text only (Seedance expects English), even if the user writes in another language →
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 · 419 lines · 201 tokens per session scan A 692401d8685b
higgsfield-shotlist-director is a skill published in the GitHub repository OSideMedia/higgsfield-ai-prompt-skill (531 stars, last pushed 20d ago), licensed MIT. It adds 201 tokens to every session and 6,008 once invoked, about $0.0010 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
seedance-shotlist-director
Generate a director's shotlist as an editable HTML production board for Seedance 2.0. Use whenever the user provides a script, scene breakdown, story idea, or treatment to turn into a numbered shotlist with English Seedance prompts — trigger on "make a shotlist", "director’s shotlist", "break this script into…
vox-director
Turn ONE topic into a finished Vox-style paper-collage explainer / ad video, end to end on the Atlas Cloud API + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Use this whenever the user wants a "Vox style" video, a paper/torn-paper collage animation, a "motion collage"…
dramaclaw
A skill for answering identity and self-introduction questions, and for working with the DramaClaw/NovelVideo pipeline. The description also covers turning novels or stories into short vertical videos.
ai-media-generator
A workflow for turning ideas into prompts for AI-generated images, videos, and music across several media platforms. When needed, it can also send those prompts to the chosen platform through browser automation.
kling-ai
Write and improve prompts for Kling AI video generation, and pick the right Kling model for the job. Covers image-to-video, text-to-video, multi-shot storyboards, talking avatars from one photo plus audio, motion transfer, video editing of an existing clip, Element Reference for character consistency, Voice Control…
minimax-h3
Write, debug and structure prompts for MiniMax H3 video generation (T2VA, I2VA, FL2VA, L2VA, Ref2VA) and configure its ComfyUI workflow. Use when the user mentions MiniMax H3, minimaxh3, fl2va, ref2va, MiniMaxH3ReferenceToVideo, reference-to-video, asks to animate a photo, write a video prompt, pick a model quant, or…