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laniameda-storage拉尼亚梅达存储

Agent Skill

laniameda-storage 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

220

周安装

9

GitHub Stars

公开资料未说明

下载量

71
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:laniameda-storage(拉尼亚梅达存储)
来源仓库:https://github.com/michailbul/laniameda-skills
仓库路径:skills/laniameda-storage
安装命令:
npx skills add https://github.com/michailbul/laniameda-skills --skill laniameda-storage
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/michailbul/laniameda-skills --skill laniameda-storage

简介

laniameda-storage 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词或任务场景快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限和维护状态。
  • 使用前建议核实是否会触发联网、命令执行或文件读写等操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

laniameda-storage

Save content to the Convex prompt-storager KB via the HTTP API.

Script

bun run ~/.agents/skills/laniameda-storage/scripts/ingest.ts '<json>'

Key fields

  • ownerUserIdread from KB_OWNER_USER_ID env var automatically, never pass it in JSON
  • promptText — text/prompt content
  • tagNames — array of tags (see tagging below)
  • imagePath — local path for inbound media (preferred over base64)
  • imageUrl — remote URL to fetch and store
  • ingestKey — auto-generated if omitted (dedup protection)
  • modelName — AI model that generated this (e.g. "Midjourney", "FLUX", "Nano Banana Pro", "Runway", "Kling", "Sora", "CDANCe")
  • generationType"image_gen" | "video_gen" | "ui_design" | "other"
  • promptType"image_gen" | "video_gen" | "ui_design" | "cinematic" | "ugc_ad" | "other"
  • domain — freeform category: "product photography", "portrait", "architecture", "fashion", etc.

Single prompt + image with model tag:

bun run ~/.agents/skills/laniameda-storage/scripts/ingest.ts '{
  "promptText": "Cinematic close-up, golden hour, 35mm film grain...",
  "imagePath": "/Users/michael/.openclaw/media/inbound/file_xxx.jpg",
  "modelName": "Midjourney",
  "generationType": "image_gen",
  "promptType": "cinematic",
  "domain": "portrait",
  "tagNames": ["prompts", "cinematic", "midjourney"]
}'

Tagging

Always include a category tag: prompts, tutorials, resources, or ideas.

Keep tags digestible (default): 3–5 tags total.

  • Don’t dump long keyword lists.
  • Don’t include generation parameters as tags (e.g., Midjourney flags like --ar, --raw, etc.).

If the user explicitly asks for creative tags, include the literal tag creative (and still keep the total to ~3–5).

Add relevant tool/style tags as needed: midjourney, flux, cinematic, portrait, character, etc.

Common patterns

Single prompt:

bun run ~/.agents/skills/laniameda-storage/scripts/ingest.ts '{
  "promptText": "...",
  "tagNames": ["prompts", "tag1"]
}'

Single prompt + image (inbound Telegram media):

bun run ~/.agents/skills/laniameda-storage/scripts/ingest.ts '{
  "promptText": "...",
  "imagePath": "/Users/michael/.openclaw/media/inbound/file_xxx.jpg",
  "tagNames": ["prompts", "ai-image"]
}'

One prompt with multiple variation images (e.g. same template, different brand examples): Use the same promptText + same promptIngestKey in every item. The prompt is created once (deduped on promptIngestKey); each asset is linked to the same prompt.

bun run ~/.agents/skills/laniameda-storage/scripts/ingest.ts '[
  { "promptText": "...", "promptIngestKey": "my-prompt-slug", "imagePath": "/path/example1.jpg", "tagNames": ["prompts", "fashion"] },
  { "promptText": "...", "promptIngestKey": "my-prompt-slug", "imagePath": "/path/example2.jpg", "tagNames": ["prompts", "fashion"] },
  { "promptText": "...", "promptIngestKey": "my-prompt-slug", "imagePath": "/path/example3.jpg", "tagNames": ["prompts", "fashion"] }
]'

Result: ONE prompt record, THREE assets — all linked to the same prompt via promptId.

When to use this pattern:

  • User shares a prompt template with multiple example outputs/variations
  • Multiple brand applications of the same prompt (e.g. fashion lookbook for Ozon, Yandex, etc.)
  • Same prompt, different style results

Batch (multiple distinct prompts or unrelated images): Pass a JSON array without a shared promptIngestKey — each item is fully independent:

bun run ~/.agents/skills/laniameda-storage/scripts/ingest.ts '[
  { "promptText": "prompt one", "tagNames": ["prompts", "cinematic"] },
  { "promptText": "prompt two", "imagePath": "/path/img.jpg", "tagNames": ["prompts", "portrait"] },
  { "imageUrl": "https://...", "tagNames": ["resources"] }
]'

Returns array of {promptId?, assetId?} — one result per item.

When to use batch vs. prompt+variations:

  • Multiple distinct prompts → plain batch (no shared promptIngestKey)
  • Same prompt + multiple example images → shared promptIngestKey pattern above
  • Never concatenate multiple distinct prompts into one promptText

⚠️ Common mistake — orphaned assets: If you save variation images WITHOUT promptText + promptIngestKey, those assets will be saved as orphans (no prompt link). Always include both in every item when using the variations pattern.

After saving

Report: what was saved, tags applied. If duplicate (same ingestKey), say "already in KB".

Schema changes

If the Convex schema or action signature has changed, read references/convex-interface.md before running — it has the source file paths and current interface docs.

适合场景

01

研究助手

02

事实核查

03

知识库问答

04

带来源的搜索总结

能力概览

能力 1

组合搜索和大模型调用

能力 2

支持多来源检索和总结

能力 3

强调引用来源和事实核查

能力 4

适合研究型 Agent 流程

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.74%
按下载量换算24

Claude

31.28%
按下载量换算22

Cursor

17.56%
按下载量换算12

Gemini CLI

9.51%
按下载量换算7

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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