功能描述

Runs web research and orchestrates image/video/3D generation, logo and product visuals, upload, and Markdown reporting. Use when users need an end-to-end mul...

使用说明 (SKILL.md)

Hopola

Name: Design With Hopola
Author: typisin

Purpose

Hopola turns "web research → image/video/3D generation → result upload → report output" into a reusable workflow, supporting both one-shot end-to-end execution and stage-based execution.

When to Use

The user needs content production based on web information and also needs visual outputs and delivery.
The user wants one-pass execution for research, generation, upload, and consolidated reporting.
The user only needs a specific stage (search only / generation only / upload only / report only).
The user needs video or 3D outputs included in the same report.

Prerequisites

web-access is available for web search and page reading.
OpenClaw Gateway is reachable.
OPENCLOW_KEY is configured in the runtime environment.
Upload policy endpoint uses MAAT_TOKEN_API (compatible with MEITU_TOKEN_API / NEXT_PUBLIC_MAAT_TOKEN_API / NEXT_PUBLIC_MEITU_TOKEN_API).
MAAT_TOKEN_API_ALLOWED_HOSTS controls trusted upload policy hosts (comma-separated, includes strategy.stariidata.com by default).
OPENCLAW_REQUEST_LOG defaults to 0 and should be enabled only for temporary debugging.
If OPENCLOW_KEY is missing, stop at precheck and return structured_error.code=GATEWAY_KEY_MISSING; do not call Gateway tools.

ClawHub Release Structure

SKILL.md: Main orchestration and strategy
subskills/search/SKILL.md: Search subskill
subskills/generate-image/SKILL.md: Image generation subskill
subskills/generate-video/SKILL.md: Video generation subskill
subskills/generate-3d/SKILL.md: 3D generation subskill
subskills/logo-design/SKILL.md: Logo design subskill
subskills/product-image/SKILL.md: Product image subskill
subskills/cutout/SKILL.md: Product cutout subskill
subskills/upload/SKILL.md: Upload subskill
subskills/report/SKILL.md: Report subskill
playbooks/design-intake.md: Design intake template and proposal framework
scripts/: Release validation and packaging scripts
assets/: Logo, cover, and flow diagram
README.zh-CN.md / README.en.md: Bilingual documentation

Execution Modes

Mode A: Single-Entry Full Pipeline

Execute in order:

Intake & Normalize: Collect explicit URLs plus session-uploaded images and normalize them into upload-ready assets.
Search: Invoke the search subskill to extract usable facts.
Discover: Query Gateway MCP tools and identify image/video/3D tools.
Generate: Invoke the corresponding generation subskill by task type.
Upload: Invoke the upload subskill to submit images or result links.
Report: Invoke the report subskill to output a Markdown report.

Mode B: Stage-Based Execution

Execute only one stage based on user instruction:

SearchOnly
GenerateImageOnly
GenerateVideoOnly
Generate3DOnly
GenerateLogoOnly
GenerateProductImageOnly
CutoutOnly
UploadOnly
ReportOnly

Standard Inputs

query: Search topic or target question.
search_scope: Search scope and constraints (optional).
image_prompt: Prompt for image generation (optional, required in GenerateImageOnly).
video_prompt: Prompt for video generation (optional, required in GenerateVideoOnly).
model3d_prompt: Prompt for 3D generation (optional, required in Generate3DOnly).
logo_prompt: Prompt for logo generation (optional, required in GenerateLogoOnly).
product_prompt: Prompt for product image generation (optional, required in GenerateProductImageOnly).
product_image_url: Source product image URL (optional in input, mandatory before product-image generation tool call).
image_url: Source image URL for cutout (optional, required in CutoutOnly).
session_uploaded_images: Session-level uploaded image references (optional; local file path, attachment handle, or markdown image source).
auto_upload_session_images: Whether to auto-upload session-uploaded images first (default true).
normalized_source_images: Internal normalized image list produced by intake stage (optional internal field).
primary_source_image_url: First available uploaded URL after normalization (optional internal field).
cutout_mode: Cutout mode for edge handling (optional, default auto).
output_background: Background mode after cutout (optional, default transparent).
desired_scenes: Requested product image scenes list (optional, required before generation).
product_info_confirmed: Whether product information is confirmed before generation (optional, default false).
source_image_confirmed: Whether the source product image is explicitly provided or confirmed by user (optional, default false; mandatory before product-image generation).
image_style: Style preference (optional).
image_size: Image size (optional).
video_ratio: Video aspect ratio (optional).
video_duration: Video duration (optional).
model3d_quality: 3D quality level (optional).
upload_enabled: Whether to upload results (default true).
report_format: Fixed as markdown.
mode: pipeline or stage.
task_type: image|video|3d|logo|product-image.
stage: When mode=stage, use one of search|generate-image|generate-video|generate-3d|generate-logo|generate-product-image|cutout|upload|report.
gateway_base_url: Default https://hopola.ai.
openclaw_key_env: Default OPENCLOW_KEY.
response_language: Optional explicit output language code (en, zh-CN, ja, etc.); if omitted, infer from the latest user message.

Standard Output (Markdown)

The report must include at least:

Summary of search findings
Generation outputs (image/video/3D)
Upload results
Security status and error alerts
Final conclusion and next-step suggestions

Configuration Placeholders

GATEWAY_BASE_URL: Gateway domain
GATEWAY_MCP_TOOLS_ENDPOINT: /api/gateway/mcp/tools
GATEWAY_MCP_CALL_ENDPOINT: /api/gateway/mcp/call
GATEWAY_KEY_HEADER: X-OpenClaw-Key
MAAT_UPLOAD_SCRIPT_PATH: scripts/maat_upload.py
IMAGE_PREFERRED_TOOL_NAME: Preferred fixed tool for image generation
VIDEO_PREFERRED_TOOL_NAME: Preferred fixed tool for video generation
MODEL3D_PREFERRED_TOOL_NAME: Preferred fixed tool for 3D generation
MODEL3D_SECONDARY_PREFERRED_TOOL_NAME: Secondary preferred tool for 3D generation
LOGO_PREFERRED_TOOL_NAME: Preferred fixed tool for logo generation
PRODUCT_IMAGE_PREFERRED_TOOL_NAME: Preferred fixed tool for product image generation
IMAGE_MCP_TOOL_MATCH_RULES: Auto-discovery rules for image tools
VIDEO_MCP_TOOL_MATCH_RULES: Auto-discovery rules for video tools
MODEL3D_MCP_TOOL_MATCH_RULES: Auto-discovery rules for 3D tools
IMAGE_MCP_ARGS_SCHEMA: Argument mapping schema for image tools
VIDEO_MCP_ARGS_SCHEMA: Argument mapping schema for video tools
MODEL3D_MCP_ARGS_SCHEMA: Argument mapping schema for 3D tools
LOGO_MCP_ARGS_SCHEMA: Argument mapping schema for logo tools
PRODUCT_IMAGE_MCP_ARGS_SCHEMA: Argument mapping schema for product image tools
REQUEST_TIMEOUT_MS: Request timeout
RETRY_MAX: Max retries
MAAT_TOKEN_API: Upload policy endpoint (default https://strategy.stariidata.com/upload/policy)
MAAT_TOKEN_API_ALLOWED_HOSTS: Trusted hosts for upload policy endpoint
OPENCLAW_REQUEST_LOG: Request log switch (0 by default)

Execution Rules

In the search stage, prioritize strategic online retrieval with web-access; combine search and page reading when necessary.
Before routing, normalize natural-language intent first: if user asks for product visuals such as 商品图/主图/详情图/场景图 or equivalent expressions, force task_type=product-image and stage=generate-product-image.
For task_type=product-image or stage=generate-product-image, if no usable source image is present (product_image_url missing and session_uploaded_images empty/unresolvable), only return inquiry guidance asking user to upload product image or provide public URL; do not call any product-image generation tool.
Before any stage requiring image URL input (product_image_url, image_url, model3d_image_url), run intake normalization:
- Merge explicit URL fields with session_uploaded_images.
- If an item is a local/session file, attachment reference, or markdown image source, invoke subskills/upload/SKILL.md to upload first.
- Write uploaded links back to corresponding URL fields; if multiple links exist, use the first as primary_source_image_url and keep all in normalized_source_images.
When required URL fields are missing but session-uploaded images are available, auto-fill from normalized uploaded URLs and continue workflow.
For task_type=product-image, if explicit product_image_url is not a public http(s) URL, force upload normalization first and block generation until URL backfill succeeds.
For task_type=product-image, source normalization origin must be traceable as one of user_public_url|uploaded_from_session|uploaded_from_local|uploaded_from_markdown|uploaded_from_non_public_url.
If auto_upload_session_images=true and upload fails, stop the dependent generation stage and return structured missing/failed inputs with retry suggestions.
For generation tasks, use "preferred fixed tool first; auto-discovery fallback if unavailable"; product-image follows stricter fixed-tool-only rules in subskills/product-image/SKILL.md.
For logo design, default to aiflow_nougat_create; for product image, default to image_praline_edit_v2.
For product-image generation, always choose image_praline_edit_v2 as the first and default call target; do not switch to other image-edit tools when image_praline_edit_v2 is available.
For task_type=product-image or stage=generate-product-image, routing must enter subskills/product-image/SKILL.md as the only orchestration entry.
For task_type=product-image or stage=generate-product-image, do not bypass subskills/product-image/SKILL.md to call product-image tools directly.
For task_type=product-image or stage=generate-product-image, first execute mandatory user inquiry and confirmation before any product-image tool call.
Product-image detailed execution rules (source confirmation, non-URL upload normalization, prechecks, confirmation card, error mapping) are defined only in subskills/product-image/SKILL.md.
For product-image generation, source image must be user-provided or explicitly user-confirmed in current context; otherwise stop before any tool call.
If source confirmation precheck fails or image_list does not use the confirmed source URL, return structured_error.code=PRODUCT_IMAGE_UNCONFIRMED_SOURCE and stop before tool call.
Logo tasks must invoke an actual generation tool and return image links, not text-only plans.
For 3D generation, default to 3d_hy_image_generate; for single-image input, prioritize fal_tripo_image_to_3d.
In the discover stage, call GET /api/gateway/mcp/tools first and build arguments from returned tool_schema.
Gateway tool list may appear under data.list or tools; for product-image task, resolve tool identity by tool_name|skill_name|name and require exact match image_praline_edit_v2.
If both image_praline_edit_v2 and similarly named tools appear, always select image_praline_edit_v2.
In the generate stage, call POST /api/gateway/mcp/call with tool_name and args.
In the upload stage, invoke subskills/upload/SKILL.md and return stable accessible URLs for all local/session images.
Session-uploaded local images must follow the same upload stage constraints (format/size/accessibility validation and failed-upload retry guidance).
All Gateway requests must include X-OpenClaw-Key in headers, and keys must be read only from environment variables.
Before any Gateway call, precheck OPENCLOW_KEY. If missing, return structured_error with code=GATEWAY_KEY_MISSING, stage=precheck, and actionable retry suggestions.
The report stage must output Markdown consistently and explicitly mark missing fields.
For product-image tasks, never use local/offline image synthesis fallback (for example PIL scripts or local compositing) to replace Gateway + subskill execution.
For product-image generation responses, include execution evidence fields: tool_name_used, source_image_url_used, source_image_origin, precheck_report, and non-sensitive call receipt fields from Gateway response.

Language Policy

All conversational text must follow response_language when provided; otherwise infer from the latest user message.
If language inference is uncertain, follow the language used in the latest user instruction in the current turn.
Do not output Chinese by default; Chinese is allowed only when user language is Chinese.
Do not mix multiple natural languages in one response, except for immutable technical tokens (API paths, field names, tool names).
Confirmation prompts, error messages, report sections, and suggestions must all use the same target language.
If any upstream subskill returns text in a different language, rewrite it into the target language before returning.

Failure Handling

Intra-stage failure: Retry by configuration, up to RETRY_MAX.
Inter-stage degradation: If one stage fails, continue producing available outputs and document failure reasons in the report.
Traceability: Record failed stage, input summary, and retry conclusion in the report.
Product-image precheck failures must return unified structured_error with code, stage, message, details, retry_suggestions.
Product-image precheck code must use one of PRODUCT_IMAGE_TOOL_UNAVAILABLE|PRODUCT_IMAGE_SOURCE_NOT_ACCESSIBLE|PRODUCT_IMAGE_MISSING_ARGS|PRODUCT_IMAGE_UPLOAD_REQUIRED|PRODUCT_IMAGE_UPLOAD_FAILED|PRODUCT_IMAGE_UNCONFIRMED_SOURCE|PRODUCT_IMAGE_CREATE_TOOL_FORBIDDEN.
Permission interception: If response is 403 with code=403001, return data.redirect_url directly and prompt the user to activate membership.
Tool unavailable: If the fixed tool is missing, automatically enable discovery fallback and record the fallback path, except product-image which must follow subskills/product-image/SKILL.md fixed-tool policy.

Security and Compliance

Never expose keys, tokens, or cookies in outputs.
Validate image format and size before upload (max 20MB).
Return only necessary results and publicly accessible links.
Run the release security validation script before packaging to detect plaintext keys and block packaging.

安全使用建议

Key points to check before installing or enabling this skill: - Metadata mismatch: The package registry shows no required env vars, but SKILL.md and scripts require OPENCLOW_KEY and request network/http and local_file:read permissions. Do not rely solely on the registry summary — inspect SKILL.md and README. - Credential handling: OPENCLOW_KEY is needed to call hopola.ai Gateway; ensure you provide a scoped key and that it is stored securely. The upload flow may call an upload policy endpoint (MAAT_TOKEN_API); set MAAT_TOKEN_API_ALLOWED_HOSTS to only trusted hosts. - Local file access and uploads: The skill will read session/local file paths (e.g., /mnt/data/session_uploads/*) and may upload them. Avoid supplying sensitive files. Review scripts/maat_upload.py to confirm what is sent to the network and that sensitive fields are redacted/logged appropriately. - Endpoint trust: Default upload policy endpoint is https://strategy.stariidata.com/upload/policy — if you override MAAT_TOKEN_API, ensure the host is whitelisted via MAAT_TOKEN_API_ALLOWED_HOSTS. Verify gateway_base_url setting and that you trust hopola.ai for generation calls. - Audit the included scripts: maat_upload.py includes host allowlist checks and redaction logic, which is good, but you should read it and validate behavior in your environment. validate_release.py checks for plaintext keys in the repo; still ensure no keys are committed. - If you have limited security expertise: ask the skill publisher to confirm the omitted registry declarations (required env vars and permissions), or only enable the skill in a test environment where you can control keys and sample inputs. If you want, I can extract the exact lines where credentials/permissions are declared in SKILL.md and list the environment variables referenced in the scripts for a precise checklist to compare against your runtime environment.

功能分析

Type: OpenClaw Skill Name: hopola Version: 1.0.9 The Hopola skill bundle is a well-structured and security-conscious multimodal production workflow for web research, media generation, and asset delivery. It includes a dedicated security validation script (validate_release.py) that checks for plaintext API keys and ensures guardrails are in place, as well as an upload script (maat_upload.py) that implements sensitive data redaction for logs and validates the accessibility of uploaded assets. The instructions in SKILL.md are strictly aligned with the stated purpose, emphasizing credential safety and structured error handling (e.g., GATEWAY_KEY_MISSING), with no evidence of malicious intent, data exfiltration, or unauthorized execution.

能力标签

cryptocan-make-purchases

能力评估

ℹ Purpose & Capability

The skill's name and description (web research → generation → upload → report) align with the included SKILL.md, subskills, and scripts. The declared runtime behavior (discover Gateway tools, call generation tools, run upload subprocess) is coherent with the stated purpose. However the registry-level summary at the top of the package metadata lists no required env vars / credentials / permissions while SKILL.md and scripts explicitly declare/expect OPENCLOW_KEY and require network:http and local_file:read — this metadata mismatch is noteworthy and could mislead users who only inspect registry fields.

ℹ Instruction Scope

SKILL.md and subskills explicitly instruct the agent to perform web searches (web-access), call Gateway endpoints (hopola.ai) with X-OpenClaw-Key, and to read and normalize session-uploaded local files (e.g., /mnt/data/session_uploads/*) and then upload them via scripts/maat_upload.py. Those activities are within the skill's documented purpose but they grant the skill discretion to read user-supplied local files and send them to external upload endpoints — verify that only intended files are supplied and that upload policies/hosts are trusted.

✓ Install Mechanism

There is no install spec (instruction-only from an install perspective). The package includes multiple Python/Bash scripts but no remote download/extract or third-party installation steps. That reduces installation risk compared to arbitrary download/install steps. Still review the included scripts (maat_upload.py) since they will be executed by the agent runtime when upload is invoked.

⚠ Credentials

SKILL.md and README files require OPENCLOW_KEY for Gateway calls and reference MAAT_TOKEN_API (and legacy env names) plus MAAT_TOKEN_API_ALLOWED_HOSTS and OPENCLAW_REQUEST_LOG. The package top-level registry metadata, however, lists no required env vars or primary credential — this mismatch is concerning because users relying on registry declarations may not realize the skill expects/uses credentials and host allowlists. The credential requests themselves are proportionate to the task (gateway key for generation calls; upload policy endpoint config for uploads), but confirm the runtime environment protects OPENCLOW_KEY and that MAAT_TOKEN_API_ALLOWED_HOSTS is set appropriately so uploads go only to trusted hosts.

✓ Persistence & Privilege

always:false and default autonomous invocation are used (normal). The skill does not request permanent system-wide privileges in the files provided and does not attempt to modify other skills' config. It does access local session files (read) and performs network calls; these are expected for its purpose but increase blast radius if misused.

版本历史

v1.0.9

Hopola 1.0.9 - Added required credentials (`OPENCLOW_KEY`) and permissions (`network:http`, `local_file:read`) to the skill manifest. - Clarified behavior on missing `OPENCLOW_KEY`: the skill now stops at precheck and returns a structured error (`GATEWAY_KEY_MISSING`) instead of proceeding. - No changes to core features or the pipeline; documentation was updated to reflect stricter prechecks for credentials.

v1.0.8

## Hopola 1.0.8 Changelog - Added compliance documentation for "harden-openclaw-clawhub-upload-compliance," including new checklist, spec, and tasks files. - SKILL.md: Expanded prerequisites and configuration to clarify upload policy API endpoints, trusted hosts, and request logging options. - No behavior changes; this release focuses on infrastructure and compliance documentation improvements.

v1.0.7

- Introduced new product image auditing and enforcement documentation/specifications under `.trae`. - Removed legacy `hopola-product-images/SKILL.md` files. - Enhanced protocol for handling product image workflows, including stricter requirements for source image confirmation before generation. - Updated input contract: added `product_image_url` and `source_image_confirmed` as mandatory parameters for product image tasks. - Updated routing logic to enforce user guidance and input checks when product image sources are missing. - Improved normalization and upload handling for session images and source URLs.

v1.0.6

Version 1.0.4 - Added four new files to support the "fix-openclaw-chat-image-upload" specification and related functionality. - Updated product image workflow: the default tool changed from `image_praline_edit_v2` to `image_praline_edit_2_hoppa`. - Clarified that product image generation must invoke `subskills/product-image/SKILL.md` as the primary subskill.

v1.0.3

**Expanded support for session-uploaded images and improved input normalization.** - Added an "Intake & Normalize" stage to pipeline: explicitly collects and normalizes URLs plus session-uploaded images before search and generation. - New inputs: `session_uploaded_images`, `auto_upload_session_images`, `normalized_source_images`, and `primary_source_image_url` for improved handling of local/session image uploads. - Workflow now auto-uploads session images and fills missing required URLs when possible, streamlining user experience. - Configuration updated with `MAAT_UPLOAD_SCRIPT_PATH`. - New `response_language` input optionally controls output language, with inference from the latest user message if omitted. - Added sample output file: `outputs/tshirt_home_1.png`.

v1.0.2

**Expanded product image workflow and cutout support; robust upload fallback.** - Added new subskills for cutout processing and product image fixed workflow (`subskills/cutout/`, `subskills/hopola-product-images/`). - Expanded inputs: now supports cutout-specific parameters and product image confirmation workflow. - Introduced mandatory user information and confirmation flow before product image generation. - Modified product image tool: now uses `image_praline_edit_v2` by default. - Enhanced upload process: on failure or inaccessible URLs, automatically falls back to new MAAT upload scripts. - Updated documentation and SKILL.md to describe new workflow protocols, input fields, and execution rules.

v1.0.1

- English translation of documentation and interface descriptions. - No logic or behavior changes; documentation only. - All usages, execution modes, and parameters now described in English. - Clarified structure, configuration placeholders, and best practices for multimodal generation workflow.

v1.0.0

Hopola is an end-to-end skill pipeline for: - web research - image generation - video generation - 3D model generation - logo design - product image generation - asset upload - Markdown report delivery site：hopola.ai

元数据

Slug hopola

版本 1.0.9

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 8

常见问题

Design With Hopola 是什么？

Runs web research and orchestrates image/video/3D generation, logo and product visuals, upload, and Markdown reporting. Use when users need an end-to-end mul... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 183 次。

如何安装 Design With Hopola？

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

Design With Hopola 是免费的吗？

是的，Design With Hopola 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Design With Hopola 支持哪些平台？

Design With Hopola 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 Design With Hopola？

由 typisin（@typisin）开发并维护，当前版本 v1.0.9。

Design With Hopola