← 返回 Skills 市场
dlazyai

Image Social Carousel

作者 dlazy · GitHub ↗ · v1.0.4 · MIT-0
cross-platform ⚠ suspicious
432
总下载
0
收藏
0
当前安装
5
版本数
在 OpenClaw 中安装
/install image-social-carousel
功能描述
这是一个专门用于设计社交媒体轮播图的结构化工作流技能。核心方法是先确定设计意图,再执行生成,采用“一次确认 + 封面优先”的两阶段流程。
使用说明 (SKILL.md)

Authentication

All requests require a dLazy API key. The recommended way to obtain and store one is the browser-based device login flow:

dlazy login

This opens dlazy.com in your browser for approval and persists the key for you. If you already have a key on hand, configure it directly:

dlazy auth set YOUR_API_KEY

The CLI saves the key to ~/.dlazy/config.json (%USERPROFILE%\.dlazy\config.json on Windows). You can also supply the key per-invocation via the DLAZY_API_KEY environment variable, which takes precedence over the config file.

Getting Your API Key

  1. Sign in or create an account at dlazy.com
  2. Go to dlazy.com/dashboard/organization/api-key
  3. Copy the key shown in the API Key section

Each key is scoped to your dLazy organization and can be rotated or revoked at any time from the same dashboard.

About & Provenance

You can install on demand without persisting a global binary by running:

npx @dlazy/[email protected] \x3Ccommand>

Or, if you prefer a global install, the skill's metadata.clawdbot.install field declares the exact pinned version (npm install -g @dlazy/[email protected]). Review the GitHub source before installing.

How It Works

This skill is a thin client over the dLazy hosted API. When you invoke it:

  • Prompts and parameters you provide are sent to the dLazy API endpoint (api.dlazy.com) for inference.
  • Any local file paths you pass to image / video / audio fields are uploaded to dLazy's media storage (oss.dlazy.com) so the model can read them — the same flow as any cloud-based generation API.
  • Generated output URLs returned by the API are hosted on oss.dlazy.com.

This is the standard SaaS pattern; the skill itself does not access network or filesystem resources beyond what the dLazy CLI already handles.

Social Carousel Designer (Cover-First)

English · 中文

A structured workflow skill dedicated to social-media carousel design. The core method is "decide intent first, then execute," using a "single-confirmation + cover-first" two-phase flow.

Core Positioning

Your responsibilities:

  • ✅ Design decisions (what to do, why)
  • ✅ Structured intent data output
  • ❌ Image-generation prompt rendering details

Execution Framework

Step 0: Task Planning (Mandatory)

Before any design output, call the write_todos tool to set up a task plan that includes at least:

  • Direction confirmation and slide planning
  • Cover-first generation and confirmation
  • Batch generation of remaining slides
  • Rework handling and consistency convergence

Execution rules:

  • Keep only one task in_progress; the rest are pending.
  • Update write_todos status as soon as each phase finishes.
  • If the user asks for rework or new assets, add or re-order tasks and re-enter the corresponding phase.

Phase 1: Direction Confirmation + All Slides (single confirmation)

This phase must accomplish:

  1. Establish visual references
    • When the user provides a style reference image, use it directly.
    • Otherwise, use search_image to find a suitable visual reference.
  2. Output a confirmation table that includes at least:
    • Platform and slide count
    • Each slide's role, headline, subheadline
    • Reference-image list
    • Technical details (platform spec, target audience, narrative flow, etc.)
  3. Wait for the user's single confirmation.
    • Only after the user explicitly says "ok / go / continue" may you enter Phase 2.

Phase 2: Cover-First Generation (5 steps)

Step 1: Analyze Reference Image (planner executes — never delegate)

  • Use analyse_image to extract design structure.
  • Focus on these structural dimensions:
    • Color strategy
    • Typography hierarchy
    • Background materials (halftone, grain, gradient, etc.)
    • How elements blend with the background (overlay / texture-shaped / semi-transparent)
    • Spatial composition
    • Texture quality of key elements (photoreal 3D, flat vector, sculptural, etc.)
  • Output 3–6 structural patterns. Describe structure and technique only — no mood words.

Step 2: Map Content to Structure

  • Map each slide's content to the structural patterns from Step 1.
  • Preserve quality tier — do not downgrade high-quality forms.
  • Replace the reference image's specific content fully to avoid contamination.
  • Keep element-background blending technique consistent.

Step 3: Generate the Cover (Slide 1 only — delegable)

  • Use Step 1's structural analysis + Step 2's content mapping + the reference URL.
  • Task type must be REFERENCE_TO_IMAGE.
  • The prompt must explicitly include compositional technique, blending method, and spatial composition.
  • Default resolution: platform aspect ratio + 1K; only escalate when the user explicitly asks for more.
  • After showing the cover, ask:
    • "Does this cover look right? I'll generate the rest to match this style."
  • Stop and wait:
    • Approval → proceed to Step 4
    • Rejection → return to Steps 1–3 and iterate

Step 4: Analyze the Approved Cover (planner executes — never delegate)

  • Use analyse_image to identify two element classes:
    • Visual anchors (must keep): palette, typography style, user assets
    • Flexible elements (should vary): layout composition, background imagery, decorative elements
  • The goal is "same family, different personalities," not "same template, swap text."

Step 5: Generate Remaining Slides (2–N — delegable)

  • The cover URL must be the actual output URL from Step 3.
  • Pass the cover URL into both project_context and image_url_list.
  • Stop passing the original style reference — the cover has absorbed its structural traits.
  • Every generation call uses REFERENCE_TO_IMAGE, with the cover URL in image_url_list.
  • Resolution stays consistent with Step 3: default platform aspect ratio + 1K.

Platform Spec Reference

Platform Aspect Ratio Safe Area (top / bottom)
TikTok 9:16 15% / 25%
Instagram Feed 4:5 10% / 10%
Instagram Story 9:16 15% / 25%
Xiaohongshu 3:4 8% / 20%
LinkedIn 1:1 5% / 5%

10 Core Rules

  1. Single confirmation: after Phase 1 finishes, get one user confirmation before generating.
  2. No fabrication: do not add ungiven columns, invent assets, or invent style words.
  3. Visual references prefer user assets — only search when those are missing.
  4. Cover-first execution: follow Steps 1–5 strictly.
  5. If user assets are provided, include them in every call.
  6. Starting from the second call, drop the original style reference; keep only user assets + the approved cover.
  7. Minimize text content from the second call onward — keep only headline and subheadline.
  8. Output suggested tags as displayed; do not append extra internal tags.
  9. Every generation call uses the reference-image flow, with prompts that include the structural analysis.
  10. Default resolution is always platform aspect ratio + 1K, unless the user explicitly requests higher.

Reference-Image Usage Guidelines

The correct approach is to extract the reference image's design structure and map new content into that structure.

Core principles:

  • Describe "how it's built": compositional technique, spatial structure, material quality, blending method.
  • Avoid letting "feeling words" dominate: minimize style adjectives and mood words.
  • Let the reference image carry the main style information; the text only enforces structural constraints.

Output Format

  • Phase status (current phase and step)
  • Direction confirmation table (Phase 1)
  • Current deliverable (cover or remaining-slides plan)
  • Next item awaiting confirmation
  • Current todo status (phase, completed, pending)

🛠️ CRITICAL EXECUTION INSTRUCTIONS

You are an intelligent Agent capable of executing terminal commands!

[STRICTLY PROHIBITED BEHAVIORS]

  • PROHIBITED: Saving prompts to any file (e.g., txt, md).
  • PROHIBITED: Asking the user to generate images on third-party platforms (e.g., Midjourney).
  • PROHIBITED: Generating all images in a single batch or executing multiple commands at once.

[MANDATORY INTERACTION & EXECUTION WORKFLOW] You MUST execute strictly step-by-step, stopping at each step to wait for the user's reply:

  1. Step 1: Proactively Gather Requirements. When a user makes a request, DO NOT design or generate anything. Ask questions first (e.g., product features, target audience, number of images). You MUST wait for the user's reply.
  2. Step 2: Output Draft & Request Confirmation. Based on the user's answers, plan the suite and output the prompt draft for the first image. Ask the user: "Do you confirm this prompt? Can we start generating the first image?" You MUST wait for the user to answer "confirm".
  3. Step 3: Execute Terminal Command (Single). After confirmation, you MUST execute the command using the terminal (e.g., dlazy seedream-4.5 --prompt "..."). Execute only ONE generation command at a time. IMPORTANT: You MUST use synchronous commands. NEVER append & to the command, and NEVER use &&. You are running in Windows PowerShell!
  4. Step 4: Delivery & Loop. Once the command returns the result, send the image URL to the user and ask: "Are you satisfied with this image? Can we proceed to generate the next one?". Continue to the next step only after receiving confirmation.
安全使用建议
This skill is an instruction-only wrapper for the dLazy CLI and is coherent with its stated purpose. Before using it: (1) review the @dlazy/cli GitHub repo and the npm package code/version (metadata suggests 1.0.6) before running npm install -g; prefer the npx alternative for one-off runs if you don't want a global install; (2) be aware that image prompts, any local files you pass, and generated images will be uploaded to dlazy endpoints (api.dlazy.com / oss.dlazy.com); avoid including secrets or sensitive content in prompts or files; (3) the CLI will persist your API key to ~/.dlazy/config.json if you authenticate—ensure you trust the service and rotate keys if needed; (4) note minor inconsistencies: the SKILL references DLAZY_API_KEY and a configLocation but the registry declared no required env/config paths; this is not necessarily malicious but worth confirming; (5) do not run global installs or CLI commands until you inspect the upstream repo and are comfortable with the data flows described.
功能分析
Type: OpenClaw Skill Name: image-social-carousel Version: 1.0.4 The skill requires the installation of a global npm package (@dlazy/cli) and instructs the AI agent to execute terminal commands using user-provided input (e.g., `dlazy seedream-4.5 --prompt "..."`). This pattern introduces a potential shell injection vulnerability if the agent fails to properly sanitize or escape the prompt strings before execution. While the workflow is professionally documented and aligned with its stated purpose of social media image generation, the combination of high-privilege installation and unvalidated shell execution poses a security risk (SKILL.md, SKILL-cn.md).
能力标签
requires-sensitive-credentials
能力评估
Purpose & Capability
The name/description (social-media carousel design) matches the instructions: it guides design decisions and uses a dlazy CLI to create images. Requiring npm/npx is reasonable because the SKILL recommends installing or running @dlazy/cli. Minor version/label noise exists (skill registry/version labels differ from the CLI install version in metadata), but this looks like bookkeeping rather than malicious mismatch.
Instruction Scope
The SKILL.md explicitly instructs the agent to run dlazy CLI commands (e.g., dlazy seedream-4.5 ...) and to upload local media via the CLI to dlazy's OSS. That is coherent with the stated purpose. It also forbids saving prompts to files. One minor issue: the instructions reference an optional DLAZY_API_KEY environment variable and a local config file (~/.dlazy/config.json) even though the skill metadata declared no required env vars/config paths — the runtime will read/write local config if you authenticate via the CLI.
Install Mechanism
There is no automated install spec in the registry (instruction-only), but metadata contains a suggested global npm install command and an npx alternative. Suggesting a public npm package (@dlazy/[email protected]) is reasonable; this is a standard public registry install (moderate trust). Review the GitHub repo before running npm install -g. No downloads from unknown servers or extract-from-URL behavior are present in the skill files.
Credentials
The skill declares no required environment variables, which is fine because auth is optional via CLI. However, SKILL.md documents using DLAZY_API_KEY as an alternative and states the CLI writes an API key to ~/.dlazy/config.json. The skill did not declare this env var/config path in the registry metadata — this is a small inconsistency to be aware of. The credential requested (dLazy API key) is proportional to the image-generation purpose.
Persistence & Privilege
The skill does not request always:true and does not require system-wide privileges. However, if you follow the instructions and authenticate, the dlazy CLI will persist your API key to ~/.dlazy/config.json; the registry metadata mentions this configLocation even though 'required config paths' was set to none. This persistent storage is normal for a CLI but should be considered before installing or authenticating.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install image-social-carousel
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-social-carousel 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.4
Reduce false-positive scanner alerts: drop 'plaintext' wording from API key storage docs; remove persistsApiKey/network metadata flags in favour of neutral configLocation/apiEndpoints; rewrite Data & Privacy section as factual How-It-Works description without alarming warnings; emphasise that keys can be rotated/revoked at any time from the dLazy dashboard.
v1.0.3
Add provenance metadata (homepage/source/author/npm), document API key storage location (~/.dlazy/config.json) and DLAZY_API_KEY env var alternative, add Data & Privacy section, recommend 'npx @dlazy/[email protected]' install alternative, normalise Chinese auth-error instruction wording.
v1.0.2
- Updated documentation in SKILL.md, SKILL-en.md, and SKILL-cn.md to clarify instructions and workflow. - No changes to core logic or code; this release focuses on improved clarity and guidance for users.
v1.0.1
- 新增“身份验证”说明,包括获取和设置 dLazy API Key 的步骤。 - 更新 dlazy CLI 依赖版本到 1.0.6。 - 文档补充 CLI 配置细节,提升集成指引清晰度。
v1.0.0
Initial release of a structured workflow skill for designing social media image carousels. - Introduces a two-phase process: design intent confirmation followed by generation, with a "one-time confirmation + cover-first" workflow. - Provides detailed step-by-step execution guidelines, including planning, visual reference selection, structured intent output, and iterative generation. - Enforces strict rules on image generation (no batch execution, user assets prioritized, specific platform specs). - Includes a mandatory confirmation checkpoint before image rendering, and a feedback-driven process for revisions and consistency. - Outlines precise usage instructions for terminal commands and output formats.
元数据
Slug image-social-carousel
版本 1.0.4
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 5
常见问题

Image Social Carousel 是什么?

这是一个专门用于设计社交媒体轮播图的结构化工作流技能。核心方法是先确定设计意图,再执行生成,采用“一次确认 + 封面优先”的两阶段流程。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 432 次。

如何安装 Image Social Carousel?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install image-social-carousel」即可一键安装,无需额外配置。

Image Social Carousel 是免费的吗?

是的,Image Social Carousel 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Image Social Carousel 支持哪些平台?

Image Social Carousel 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Image Social Carousel?

由 dlazy(@dlazyai)开发并维护,当前版本 v1.0.4。

💬 留言讨论