100x-visual-fission

100x-visual-fission is a skill for Claude Code from kezd088/100x-skill-tiktok. It costs 165 tokens per session (2,275 once invoked), scanned A, original, MIT.

A prompt-variation tool for turning at least two related person, scene, or product references into structured instructions for creating visual variants.

In plain words
What is it for?
Use it to create prompt sets for single images, beginning-and-ending frames, three-stage sequences, or multi-day progress scenes. It can prepare variants for image or video generation.
Why use it?
It helps preserve the shared identity of the person, setting, and product while varying the framing, camera position, lighting, colors, and sequence of images.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 100x-skill-tiktok plugin — 9 skills shipped together

Good fit Use it to create prompt sets for single images, beginning-and-ending frames, three-stage sequences, or multi-day progress scenes. It can prepare variants for image or video generation.

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Install with agentmods
npx agentmods add skills/kezd088/100x-skill-tiktok/100x-visual-fission
Install

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.

Any agent
npx skills add kezd088/100x-skill-tiktok --skill 100x-visual-fission
Clone the repo
git clone --depth 1 https://github.com/kezd088/100x-skill-tiktok

Made for: Claude Code.

Or install 100x-skill-tiktok, the plugin that ships this one along with the rest of its 9 skills.

Wrote 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.

agentmods badge for 100x-visual-fission

README.md
[![agentmods](https://agentmods.dev/badge/skills/kezd088/100x-skill-tiktok/100x-visual-fission/github.svg)](https://agentmods.dev/skills/kezd088/100x-skill-tiktok/100x-visual-fission)
Your own site
<a href="https://agentmods.dev/skills/kezd088/100x-skill-tiktok/100x-visual-fission"><img src="https://agentmods.dev/badge/skills/kezd088/100x-skill-tiktok/100x-visual-fission/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.

agentmods 80×15 button for 100x-visual-fission

Your own site · 80×15
<a href="https://agentmods.dev/skills/kezd088/100x-skill-tiktok/100x-visual-fission"><img src="https://agentmods.dev/badge/skills/kezd088/100x-skill-tiktok/100x-visual-fission.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 165 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,275 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00165 $0.02275
Opus 5 $0.00082 $0.01137
Sonnet 5 $0.00033 $0.00455
Haiku 4.5 $0.00016 $0.00228

Measured 12d ago against content hash edc684a2be7a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

100x-visual-fission 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/validate.js), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/100x-visual-fission/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

100x-visual-fission

一句话定位

输入 ≥2 条同系列人物/场景参考(反推 JSON 或文字降级描述)+ 产品文案,输出锁定人物/场景/ 产品身份的媒介裂变提示词矩阵:单帧/首尾/首中尾/数日见效四选一媒介结构 × VTP 原版 A/B/C 机位预设,附固定负面词常量。属于 100x 体系 L2 创意生成层,对应"5 视觉裂变"这一步。

何时触发

用户说:

  • "帮我裂变这条视觉参考" / "这个产品出几个变体" / "生成裂变提示词矩阵" / "媒介裂变" / "视觉裂变"
  • "fission this reference" / "generate prompt variants for this product" / "multiply this reference image into variants"
  • 或直接给一组已反推的人物/场景 JSON(或文字描述)+ 产品文案,要求出一套裂变提示词

输入

最小输入(类别 A,硬性必填,两项都要):references[]——至少 2 条同系列的人物/场景 参考材料,可以是已完成的 VTP 反推 JSON(vtp_prompts_01/02 schema),也可以是没有真实 参考图时的文字描述降级(需在产出的 source_material_note 里如实标注);product_brief—— 产品名+品类+效果叙事文本。少于 2 条参考无法做"提取共性"(分不清哪些在变),会被拒绝, 详见 workflow.md Phase 1 类别 A 校验。

软性补充(类别 B,缺失走默认值,见 workflow.md):aspect_ratios_wanted(默认 ["9:16"])、variant_count(默认 2,这是 VTP 04 步用户自由指定的参数,不是本 skill 的结构性判据)。

上游可选产出(类别 C,缺失静默跳过,不阻塞,也绝不要求用户先跑别的 skill): 100x-persona 产出的 PersonaSceneBundle(若已存在,直接复用其身份锚点,跳过反推)。

输出

结构见 schema.jsonconstants(人物/场景/产品三锚点)+ variables(VTP 03 步的 共性/变量提取)+ media_plan(四选一媒介结构 + 帧计划)+ prompt_sets(VTP 04 步生成 的 N 组提示词)+ fission_variants(VTP 06 步机位裂变,每帧至少一条)+ negative_prompt (VTP 07 步固定常量)+ meta。可选再渲染一张人类可读 Markdown 摘要(媒介结构 + 帧列表 + 每帧对应裂变条目)。

核心约束(4 条公理,详见 axioms.md

  1. 人物/场景/产品三锚点是定量,必须逐字出现在每一个裂变分支里(子串包含检查是大小写 不敏感的字面匹配,不是语义等价判断——两段用词不同但描述同一个人的文字,或恰好共享 同一句泛泛套话的两件不同产品,都可能让这条检查产生假阴性/假阳性,见 axioms.md 公理1)
  2. 媒介裂变轴(单帧/首尾/首中尾/数日见效)叠加在 VTP 原版机位/灯光/调色三档轴之上, 不是替换——这是本 skill 与 VTP 原设计的核心差异点(已知局限:媒介结构判定要求 multi_day 分支的理由必须引用一个数字,但机器验证不了这个数字是否忠实反映输入叙事, 见 axioms.md 公理 2 TODO)
  3. 每一帧计划必须真的被至少一条裂变产物渲染出来,裂变产物引用的来源必须真实存在—— 规划了却没渲染,或渲染了却指向不存在的规划,都判 FAIL
  4. 每条落地提示词固定负面词常量 + 真实感兜底后缀,不得把不可视化/夸大宣称原句抄进画面 描述(已知局限:不可视化宣称关键词表目前只在保健品类目的英语+西语真实语料上验证过, 跨更多品类大概率需要扩表,见 axioms.md 公理 4 TODO)

三阶段流程

详见 workflow.md:Phase 1 接收+校验(类别 A/B/C + 不可视化宣称预检测)→ Phase 2(VTP 七步骨架:01/02 反推或降级 → 03 提取共性 → 媒介结构判定(本 skill 新增) → 04 生成 N 组提示词 → 06 机位裂变 → 07 固定负面词 → 逐条自检)→ Phase 3 用户触发的返工(L1 单条 / L2 单帧或结构级 / L3 全体)。

Read the full file on GitHub · 112 lines

Changes

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.

  1. 12d ago First seen · 112 lines · 165 tokens per session scan A edc684a2be7a

Subscribe to this mod's changes

100x-visual-fission is a skill published in the GitHub repository kezd088/100x-skill-tiktok (8 stars, last pushed 15d ago), licensed MIT. It adds 165 tokens to every session and 2,275 once invoked, about $0.0008 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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