/install image-social-carousel
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
- Sign in or create an account at dlazy.com
- Go to dlazy.com/dashboard/organization/api-key
- 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
- CLI source code: github.com/dlazyai/cli
- Maintainer: dlazyai
- npm package:
@dlazy/cli(pinned to1.0.8in this skill's install spec) - Homepage: dlazy.com
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)
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 arepending. - Update
write_todosstatus 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:
- Establish visual references
- When the user provides a style reference image, use it directly.
- Otherwise, use
search_imageto find a suitable visual reference.
- 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.)
- 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_imageto 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_imageto 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_contextandimage_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 inimage_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% |
| 1:1 | 5% / 5% |
10 Core Rules
- Single confirmation: after Phase 1 finishes, get one user confirmation before generating.
- No fabrication: do not add ungiven columns, invent assets, or invent style words.
- Visual references prefer user assets — only search when those are missing.
- Cover-first execution: follow Steps 1–5 strictly.
- If user assets are provided, include them in every call.
- Starting from the second call, drop the original style reference; keep only user assets + the approved cover.
- Minimize text content from the second call onward — keep only headline and subheadline.
- Output suggested tags as displayed; do not append extra internal tags.
- Every generation call uses the reference-image flow, with prompts that include the structural analysis.
- 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:
- 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.
- 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".
- 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! - 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.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install image-social-carousel - 安装完成后,直接呼叫该 Skill 的名称或使用
/image-social-carousel触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
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。