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 1370998960-del/https-github.com-Emily2040-seedance-2.0 --skill seedance-antislopgit clone --depth 1 https://github.com/1370998960-del/https-github.com-Emily2040-seedance-2.0Wrote 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/1370998960-del/https-github.com-emily2040-seedance-2.0/seedance-antislop)<a href="https://agentmods.dev/skills/1370998960-del/https-github.com-emily2040-seedance-2.0/seedance-antislop"><img src="https://agentmods.dev/badge/skills/1370998960-del/https-github.com-emily2040-seedance-2.0/seedance-antislop.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.00998 |
| Opus 5 | $0.00024 | $0.00499 |
| Sonnet 5 | $0.00010 | $0.00200 |
| Haiku 4.5 | $0.00005 | $0.00100 |
Grade A, and why
seedance-antislop 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 8d 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.
This is a copy
88% identical to seedance-antislop — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
seedance-antislop
Remove filler that hides missing visual decisions. A strong Seedance prompt uses observable nouns, verbs, camera moves, light sources, sound cues, and constraints. A weak prompt asks for excellence without saying what excellence looks or sounds like.
Intent
Users reach for giant empty words precisely because they care intensely and don't know where to put it. The soul of de-slopping is conservation: every deleted "epic" must come back as a visible choice that holds the same caring. Strip a prompt without honoring the feeling that bloated it, and the user hears that their excitement was wrong.
Visibility Test
Every major phrase should be visible to a camera, measurable by a light meter, audible in the mix, or observable as motion. If a phrase cannot pass that test, replace it with production language.
| Filler | Ask what it means | Strong replacement pattern |
|---|---|---|
| cinematic | What camera and light make it cinematic? | locked close-up, warm practical key, cool rim light |
| epic | What is the scale or stake? | wide low-angle shot, tiny figure against storm wall |
| beautiful | What color, texture, or light behavior? | pearl highlights on wet ceramic, soft window bounce |
| dynamic | What moves, how fast, and where does it end? | fast lateral track ending on the hero label |
| professional | What production setup? | clean commercial tabletop, controlled reflection, no clutter |
The Six Slop Classes
Classify before rewriting - each class has a different repair:
- Empty evaluators (
cinematic, epic, stunning) - convert each to the one observable detail that earns it. - Borrowed image-model tokens (
8K, masterpiece, trending on ArtStation) - delete; quality and resolution are settings, not prose. - Tag salad (comma keyword dumps ported from image prompting) - rewrite as shooting-brief prose: one sentence per element, with an action and a time axis.
- Negation slop (
no blur, no artifacts, no extra fingers) - negation summons; describe what IS there instead, and keep negation only in the constraint slot. - Adjective stacking (three synonyms for one quality) - pick the single detail that matters.
- Feel-suffix words (
电影感, 雰囲気のある, 감성적인, atmosférico, атмосферный, vibey) - name the physical cause of the feeling; every language file inreferences/vocab/has a Slop Traps table for its own community's empty words.
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.
- 8d ago First seen · 65 lines · 48 tokens per session scan A 6a58eb6846a2
seedance-antislop is a skill published in the GitHub repository 1370998960-del/https-github.com-Emily2040-seedance-2.0 (235 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 998 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to seedance-antislop, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
seedance-antislop
This skill should be used when a Seedance 2.0 prompt contains generic AI filler, hollow superlatives, vague cinematic language, bloated adjectives, weak verbs, or needs sharper production-specific wording.
seedance-antislop
This skill should be used when a Seedance 2.0 prompt contains generic AI filler, hollow superlatives, vague cinematic language, bloated adjectives, weak verbs, or needs sharper production-specific wording.
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt is slop-heavy, generic, padded with empty quality words, tripping false-positive filters, or needs precise English production vocabulary for camera, lighting, motion, VFX, audio, and constraints.
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt is slop-heavy, generic, padded with empty quality words, tripping false-positive filters, or needs precise English production vocabulary for camera, lighting, motion, VFX, audio, and constraints.
seedance-interview
This skill should be used when the user wants creative guidance, story development, scene planning, or a director interview to turn a Seedance 2.0 idea into a production-ready prompt - adapting to users who have no idea yet, a rough concept, or precise professional direction in shots, lenses, and blocking.
seedance-prompt
This skill should be used when the user asks to write, improve, translate, compress, or debug a Seedance 2.0 video prompt; mentions T2V, I2V, V2V, R2V, camera direction, prompt quality, or provides reference assets for a production-ready prompt.