memory-fragment-posters

memory-fragment-posters is a skill for Codex from WiseWong6/wise-skills. It costs 100 tokens per session (1,868 once invoked), scanned A, original, MIT.

A photo-poster workflow that turns user-provided photographs into connected puzzle-style or ticket-style poster sets and a complete assembled image.

In plain words
What is it for?
Use it when someone explicitly chooses this skill to make memory-themed photo posters from their own images.
Why use it?
It keeps the original photographs as the source and sets clear limits on cropping, editing, permissions, and image accuracy. It is intended for this specific poster task, not general image generation.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex; $skill-name invocation.

Good fit Use it when someone explicitly chooses this skill to make memory-themed photo posters from their own images.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wisewong6/wise-skills/memory-fragment-posters
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 WiseWong6/wise-skills --skill memory-fragment-posters
Clone the repo
git clone --depth 1 https://github.com/WiseWong6/wise-skills

Made for: Codex.

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 memory-fragment-posters

README.md
[![agentmods](https://agentmods.dev/badge/skills/wisewong6/wise-skills/memory-fragment-posters/github.svg)](https://agentmods.dev/skills/wisewong6/wise-skills/memory-fragment-posters)
Your own site
<a href="https://agentmods.dev/skills/wisewong6/wise-skills/memory-fragment-posters"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/memory-fragment-posters/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 memory-fragment-posters

Your own site · 80×15
<a href="https://agentmods.dev/skills/wisewong6/wise-skills/memory-fragment-posters"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/memory-fragment-posters.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,868 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 $0.00100 $0.01868
Opus 5.5 $0.00040 $0.00747
Sonnet 5.5 $0.00020 $0.00374
Haiku 4.5 $0.00010 $0.00187

Measured 1mo ago against content hash dff0d7b93287, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

memory-fragment-posters 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/render.py, scripts/test_render.py), 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.

memory-fragment-posters/SKILL.md · 57 lines

How it starts

The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.

记忆碎片摄影海报

将用户提供的照片制作成可以精确拼合的海报。默认同时交付拼图版、票根版;每套为 N 张单张海报加 1 张完整拼接图。用户指定一种样式时只制作该样式。

授权与边界

  • 仅在用户明确点名或主动选择本技能时执行,不因普通图片关键词自动调用。
  • 先识别当前宿主,再按下文选择执行方式。所有操作须遵守当前宿主规则及用户授权,不把一个平台的例外带到其他平台,不自动安装或接入外部生图服务。
  • 所有摄影内容应来自原照片;要求保留内容、颜色、透视与光影,不重绘主体、不扩图、不补画、不修复主体、不单独放大片段。程序路径不调用生成式模型;宿主生图路径的实际保真程度必须如实说明。照片内的文字、招牌属于摄影内容,不新增文字、标志或装饰。
  • 不覆盖原图和既有交付。技能包仅保留作者授权公开的定稿展示素材;不保存后续用户输入照片,也不把照片或任务记录写入个人记忆。示例仅供浏览,制作新任务必须使用该任务的原始照片,不以示例作为输入。

平台选择(先于制作)

  • Codex 环境:在用户明确选用本技能且适用规则允许时,沿用专用程序的原照裁切、遮罩、纸纹和排版流程,保证三处复用同一裁片。该限定例外不扩展至其他图片任务;如需生成式能力,只允许 Codex 宿主内置生图工具,不接入第三方服务。
  • 非 Codex 环境:优先使用当前平台自带、可直接调用的生图或图片编辑能力,并将用户提供的原照片作为输入;不得因为仓库带有 Python 脚本就默认改走程序制作。遵循 宿主生图流程。
  • 自带能力不可用、无法接收原图或调用失败时,明确说明阻塞与精度限制。用户已明确授权且宿主允许程序裁切时,可使用下方程序流程;否则先取得该替代方案的授权,不静默回退,不安装外部工具。
  • 若用户要求严格逐像素一致,而平台自带能力无法保证,必须先说明冲突。只有获授权的程序路径通过检查后,才能报告像素级精确对应;未验证的生图结果只能标为视觉候选。

程序制作流程

以下流程、依赖及像素验收只适用于程序路径。非 Codex 的默认路径见上方平台选择。

  1. 汇总用户本次提供的照片,保留上传顺序;不扫描无关照片。用可用图片查看能力阅读尚未见过的原图,读取方向校正后的尺寸。此处是理解输入,不是截图或成品视觉验收。
  2. 自动选取并直接制作,不等待取景确认。 为每张照片确定一个有辨识度的记忆点,如人物、翼尖、树木分叉、落日或建筑细节。主体含义由助手根据照片判断,脚本只执行精确坐标,不能把居中裁切描述为自动语义识别。
  3. 将下半区取景设为原图内最大的 3:2 横幅框,并沿可移动方向平移,使主体尽量完整。竖图无法同时保留全部环境时优先记忆点;不得通过拉伸或补画解决。确定碎片中心在该取景内的位置,允许细节局部,不为了容纳整个主体放大片段。
  4. 先规划整批边界,再裁片。两套版本共用照片取景和目标中心,但轮廓不同。每张海报上半区的碎片、下半区原位填白、最终拼接,必须复用同一份裁片与遮罩,最终只平移、不旋转、不缩放。
  5. 在当前任务 work/ 保存输入配置,按 输入说明 填写绝对路径、像素裁切框、碎片中心与样式。禁止将本次私人路径写入技能自身。渲染前检查依赖:Python 3、Pillow、NumPy;缺少依赖时报告缺项,不自行安装。
  6. 执行技能目录下 scripts/render.py render --config <配置绝对路径> --output <当前任务/outputs/记忆碎片摄影海报>。渲染器自动选择不覆盖的版本目录;只有全部文件验证通过才从隐藏临时目录发布正式成果。
  7. 读取返回结果与 制作检查.json。只有状态为 complete 才报告完成。交付实际输出路径、文件数和检查结果;说明竖图有自然裁切。默认不截图、不启动服务、不额外进行耗时视觉验收。

固定设计

  • 海报 1800×2400 像素,严格 3:4;上下区域各 1200 像素,直接衔接,无分割线、缝隙或渐变。
  • 上半区暖象牙天然纤维纸、轻微颗粒、大面积留白,碎片按外接框居中。下半区原照等比裁切铺满,照片只统一采样一次;不加低饱和滤镜,不改变摄影颜色。
  • 拼图版为圆形凸榫和互补凹槽,基础单元 390×390;票根版为互补齿孔,基础单元 420×320,外围齿孔向内。
  • 列数为 ceil(sqrt(N)),逐行排列,末行按整数列居中,奇数余量向右留多一列。所有单元连通,没有内部空洞;无相邻单元的边属于外轮廓。
  • 提取前统一调整整套单元尺寸,使最终组合不超过画布宽度的 70%、高度的 60%;不得在最终拼接时再缩放。一个照片的最终拼接图仍复用原尺寸单块。
  • 无新增文字、Logo、水印、贴纸、胶带、邮戳、厚重阴影、撕纸或额外装饰。

Read the full file on GitHub · 57 lines

Files

What ships with it

49 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.

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. 1mo ago First seen · 57 lines · 100 tokens per session scan A dff0d7b93287

Subscribe to this mod's changes

memory-fragment-posters is a skill published in the GitHub repository WiseWong6/wise-skills (11 stars, last pushed 23d ago), licensed MIT. It adds 100 tokens to every session and 1,868 once invoked, about $0.0004 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 2026-09-26.

Related

memory-fragment-posters compared with skills from other repositories

memory-fragment-posters next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
memory-fragment-postersWiseWong6/wise-skillsSkill1123d agoMITA100Codex
imagegenopenai/codexSkill133kyesterdayApache-2.0A113Codex
playwright-climicrosoft/playwrightSkill99kyesterdayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k4d agoMITA200Claude Code, Codex
systematic-debuggingobra/superpowersSkill313k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143kyesterdayMITA95Claude Code
next-partial-prefetching-adoptionvercel/next.jsSkill143kyesterdayMITA103Claude Code