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 | $0.06211 |
| Opus 5.5 | $0.00080 | $0.02484 |
| Sonnet 5.5 | $0.00040 | $0.01242 |
| Haiku 4.5 | $0.00020 | $0.00621 |
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 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 — 431 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.
- 1mo ago Changed · +12 lines e606ef844229
- 1mo 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 (800 stars, last pushed 2d ago), licensed MIT. It adds 201 tokens to every session and 6,211 once invoked, about $0.0008 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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