{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-hismax--redink","slug":"hismax--redink","name":"RedInk","type":"webapp","url":"https://redink.top","page_url":"https://unfragile.ai/hismax--redink","categories":["image-generation"],"tags":["ai","aigc","content-generator","docker","flask","gemini","nano-banana-pro","python","social-media","text-to-image","vue","xiaohongshu"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-hismax--redink__cap_0","uri":"capability://text.generation.language.topic.to.outline.ai.text.generation.with.structured.json.output","name":"topic-to-outline ai text generation with structured json output","description":"Converts a user-provided text topic into a structured content outline by routing requests through pluggable AI text generation clients (Google GenAI, OpenAI-compatible APIs). The system uses a provider configuration abstraction layer to support multiple LLM backends, with prompt engineering that enforces JSON schema compliance for downstream image generation. Implements retry mechanisms and error handling to ensure reliable outline generation even with transient API failures.","intents":["Generate a multi-point content outline from a single topic string for Xiaohongshu posts","Support multiple LLM providers without code changes via configuration","Ensure outline structure is deterministic and parseable for image generation pipeline"],"best_for":["Content creators automating Xiaohongshu post generation at scale","Teams wanting to swap LLM providers (Google Gemini, OpenAI, local Ollama) without redeployment"],"limitations":["Outline generation latency depends on selected LLM provider (typically 2-10 seconds)","JSON parsing failures fall back to retry logic but may timeout on malformed LLM responses","No built-in prompt optimization — uses fixed templates, limiting customization of outline structure"],"requires":["Python 3.8+","API key for at least one text generation provider (Google GenAI API key, OpenAI API key, or local Ollama endpoint)","Provider configuration file (YAML/JSON) specifying text model and endpoint"],"input_types":["text (topic string, e.g., '秋季显白美甲')","optional reference images (multimodal input for context)"],"output_types":["JSON object with structured outline (array of content points/sections)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_1","uri":"capability://image.visual.multi.provider.image.generation.with.consistent.visual.styling","name":"multi-provider image generation with consistent visual styling","description":"Generates 6-9 styled images from outline content by orchestrating multiple image generation backends (Google GenAI, Banana.dev Nano Pro, OpenAI-compatible APIs) through an abstraction layer. Each image is generated with embedded Chinese text, consistent visual design across the series, and optional reference image conditioning. The system applies image compression and optimization post-generation to reduce file sizes while maintaining quality for social media distribution.","intents":["Generate a complete set of visually consistent images for a Xiaohongshu post from structured outline","Support multiple image generation providers (Google Gemini Vision, Banana.dev Nano Pro, DALL-E) via configuration","Embed Chinese text accurately in generated images without OCR post-processing","Optimize image file sizes for social media upload constraints"],"best_for":["Content creators needing high-volume image generation with consistent branding","Teams evaluating different image generation providers for cost/quality tradeoffs","Xiaohongshu creators requiring Chinese text rendering in generated images"],"limitations":["Image generation latency is provider-dependent (Google GenAI: 5-15s, Banana Nano Pro: 10-30s per image)","Consistency across 6-9 images depends on prompt quality and LLM instruction-following; no explicit style transfer or LoRA fine-tuning","Reference image conditioning is optional and may not always influence output style as expected","No built-in A/B testing or quality scoring — all generated images are treated equally"],"requires":["Python 3.8+","API credentials for at least one image generation provider (Google GenAI API key, Banana.dev API key, or OpenAI API key)","Provider configuration specifying image model, resolution, and style parameters","Outline data from Phase 1 (topic-to-outline generation)"],"input_types":["JSON outline (array of content points)","optional reference images (for style conditioning)","image generation parameters (resolution, style, quality)"],"output_types":["PNG/JPEG images (high-resolution originals + thumbnail variants)","image metadata (generation timestamps, provider used, compression ratio)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_10","uri":"capability://image.visual.chinese.text.rendering.and.embedding.in.generated.images","name":"chinese text rendering and embedding in generated images","description":"Embeds Chinese text directly into generated images during the image generation phase, using LLM-based text generation (outline content) and provider-specific text rendering capabilities. The system generates Chinese text via the outline generation phase, passes it to image generation prompts with explicit text embedding instructions, and validates that generated images contain readable Chinese text. Handles character encoding (UTF-8), font selection, and text layout to ensure accurate Chinese text rendering without post-generation OCR or manual text addition.","intents":["Generate Xiaohongshu images with embedded Chinese text describing products, tips, or stories","Ensure Chinese text is readable and accurately rendered in generated images","Avoid manual text addition or post-processing by embedding text during generation"],"best_for":["Content creators generating Xiaohongshu posts with Chinese text overlays","E-commerce sellers showcasing products with Chinese product descriptions","Teams wanting to automate Chinese content creation without manual design"],"limitations":["Text rendering quality depends on image generation model's instruction-following; some models may ignore text embedding instructions","No control over font, size, or placement of embedded text; depends on model's interpretation of prompts","Chinese character support varies by provider; some models may struggle with rare characters or traditional Chinese","No validation that generated text matches requested content; requires manual review"],"requires":["Image generation provider with strong instruction-following (Google GenAI, OpenAI GPT-4V preferred)","Chinese text input (from outline generation phase)","Prompts with explicit text embedding instructions (e.g., 'embed the following Chinese text in the image: ...')"],"input_types":["Chinese text strings (from outline generation)","image generation prompts with text embedding instructions"],"output_types":["images with embedded Chinese text"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_11","uri":"capability://tool.use.integration.api.routes.and.rest.endpoints.for.generation.workflow","name":"api routes and rest endpoints for generation workflow","description":"Exposes Flask REST API endpoints for the two-phase generation workflow: POST /api/generate/outline (topic → outline), POST /api/generate/images (outline → images), and GET /api/generate/status (progress polling). Each endpoint accepts JSON request bodies with generation parameters (topic, reference images, provider config), validates inputs, and returns JSON responses with generated content or error details. Implements request validation, error handling, and optional authentication/rate limiting for production deployments.","intents":["Provide programmatic access to content generation for third-party integrations","Enable frontend SPA to communicate with backend generation services","Support batch generation workflows via API calls"],"best_for":["Developers integrating RedInk into larger applications or workflows","Teams building custom frontends or mobile apps on top of RedInk API","Automation scripts or CI/CD pipelines calling RedInk for content generation"],"limitations":["API is synchronous; long-running generation requests (30+ seconds) may timeout without SSE streaming","No built-in authentication or rate limiting; requires external API gateway (Kong, AWS API Gateway) for production","Request validation is basic; no JSON schema validation or OpenAPI documentation","No versioning; API changes may break existing integrations"],"requires":["Flask web framework","HTTP client library (requests, curl, etc.) for API calls","JSON request/response format"],"input_types":["JSON request bodies with generation parameters (topic, reference images, provider config)"],"output_types":["JSON responses with generated content (outline, images) or error details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_2","uri":"capability://image.visual.reference.image.multimodal.conditioning.for.content.generation","name":"reference image multimodal conditioning for content generation","description":"Accepts optional user-uploaded reference images and incorporates them into both outline generation and image generation pipelines via multimodal LLM APIs. The system encodes reference images as base64 or file uploads, passes them to text and image generation models that support vision capabilities, and uses them to influence content style, tone, and visual direction without explicit fine-tuning. Handles image validation, format conversion, and size constraints before submission to downstream providers.","intents":["Upload reference images (brand materials, product photos, style examples) to guide content generation","Ensure generated content aligns with user's visual brand or aesthetic preferences","Condition image generation on reference images to maintain consistency with existing assets"],"best_for":["Brand teams and agencies generating on-brand content at scale","Content creators with established visual identities wanting to maintain consistency","E-commerce sellers showcasing products through AI-generated lifestyle images"],"limitations":["Reference image influence is probabilistic — not guaranteed to match user expectations; LLM interpretation varies","Only providers with vision capabilities (Google GenAI, OpenAI GPT-4V) support multimodal conditioning; Banana.dev Nano Pro does not","Large reference images increase API latency and may hit token/size limits on some providers","No explicit style transfer or LoRA fine-tuning — relies on LLM's ability to infer style from images"],"requires":["Python 3.8+","Text/image generation provider with multimodal (vision) API support","Reference images in common formats (JPEG, PNG, WebP)","Image size constraints: typically <5MB per image, <4096x4096 pixels"],"input_types":["image files (JPEG, PNG, WebP)","image URLs (if provider supports remote image fetching)"],"output_types":["base64-encoded image data (for API submission)","image metadata (dimensions, format, encoding)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_3","uri":"capability://automation.workflow.server.sent.events.sse.streaming.for.real.time.generation.progress","name":"server-sent events (sse) streaming for real-time generation progress","description":"Streams generation progress updates to the frontend in real-time using HTTP Server-Sent Events (SSE), allowing users to monitor outline generation and image generation phases without polling. The backend emits progress events at key checkpoints (outline started, outline completed, image 1 generated, image 2 generated, etc.), and the frontend Vue.js application listens to these events and updates the UI reactively. Enables long-running operations (30+ seconds) to feel responsive and transparent to users.","intents":["Display real-time progress updates during multi-step content generation","Prevent frontend timeout perception during long-running image generation","Provide transparency into which generation phase is currently executing"],"best_for":["Web applications with long-running backend operations (>5 seconds)","Teams wanting to improve perceived performance and user confidence during generation"],"limitations":["SSE requires persistent HTTP connection; incompatible with some proxies/firewalls that timeout idle connections","No built-in reconnection logic — connection loss requires manual page refresh","Progress granularity is limited to backend emit points; cannot stream partial image data","Requires browser support for EventSource API (not available in IE11)"],"requires":["Flask backend with SSE support (flask-cors, custom route handlers)","Frontend with EventSource API support (modern browsers)","Network connectivity without aggressive connection timeout policies"],"input_types":["generation request (topic, reference images, provider config)"],"output_types":["SSE event stream (text/event-stream MIME type) with JSON event data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_4","uri":"capability://tool.use.integration.provider.configuration.abstraction.with.runtime.provider.swapping","name":"provider configuration abstraction with runtime provider swapping","description":"Implements a configuration-driven provider selection system where text and image generation providers are specified in YAML/JSON configuration files (provider_config.yaml) rather than hardcoded in application logic. At runtime, the system instantiates the appropriate text/image generator client based on configuration, enabling users to swap providers (Google GenAI → OpenAI → Ollama) without code changes or redeployment. Configuration includes API endpoints, model names, authentication credentials, and provider-specific parameters (temperature, max_tokens, image resolution).","intents":["Switch between multiple LLM/image generation providers without code changes","Configure provider-specific parameters (model name, API endpoint, authentication) via YAML","Support local/self-hosted providers (Ollama) alongside cloud providers (OpenAI, Google)"],"best_for":["Teams evaluating multiple providers for cost/quality/latency tradeoffs","Organizations with compliance requirements to use self-hosted or specific cloud providers","Developers wanting to test against multiple providers in CI/CD pipelines"],"limitations":["Configuration file must be manually edited; no UI for provider management in current version","Provider-specific parameters are not validated at startup — invalid configs fail at runtime during first generation request","No built-in provider health checks or fallback logic if primary provider is unavailable","Configuration changes require application restart (no hot-reload)"],"requires":["Python 3.8+","YAML/JSON configuration file with provider credentials and model specifications","API keys/credentials for selected providers (Google GenAI, OpenAI, Ollama endpoint URL)"],"input_types":["YAML/JSON configuration file (provider_config.yaml)"],"output_types":["instantiated provider client objects (TextGenerationClient, ImageGeneratorClient subclasses)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_5","uri":"capability://image.visual.image.compression.and.optimization.for.social.media.distribution","name":"image compression and optimization for social media distribution","description":"Automatically compresses and optimizes generated images post-generation to meet Xiaohongshu platform constraints (file size, aspect ratio, resolution). The system applies lossy/lossless compression algorithms, generates thumbnail variants, and validates output dimensions and file sizes before returning to user. Compression parameters are tunable via configuration to balance quality vs. file size based on platform requirements.","intents":["Reduce generated image file sizes for faster upload to Xiaohongshu","Generate thumbnail variants for preview/gallery display","Ensure images meet platform-specific dimension and file size constraints"],"best_for":["Content creators uploading large volumes of images to Xiaohongshu","Teams with bandwidth constraints or slow upload speeds","Platforms with strict file size or dimension limits"],"limitations":["Compression is lossy by default; quality loss is visible on high-zoom inspection","Compression ratio depends on image content (photographic vs. text-heavy); not guaranteed to meet all size constraints","No adaptive quality adjustment — uses fixed compression parameters regardless of image complexity","Thumbnail generation adds ~200-500ms latency per image"],"requires":["Python 3.8+","PIL/Pillow library for image processing","Compression configuration (quality level, target file size, thumbnail dimensions)"],"input_types":["PNG/JPEG images (high-resolution, post-generation)"],"output_types":["compressed PNG/JPEG images (optimized for social media)","thumbnail images (smaller dimensions for preview)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_6","uri":"capability://automation.workflow.error.handling.and.retry.mechanisms.for.api.failures","name":"error handling and retry mechanisms for api failures","description":"Implements exponential backoff retry logic for transient API failures (rate limits, timeouts, temporary outages) across text and image generation providers. The system catches provider-specific exceptions, logs errors with context, and automatically retries failed requests with increasing delays (1s, 2s, 4s, 8s, etc.) up to a configurable maximum. Distinguishes between retryable errors (429 rate limit, 503 service unavailable) and fatal errors (401 auth failure, 400 bad request) to avoid wasting retries on unrecoverable failures.","intents":["Automatically recover from transient API failures without user intervention","Provide transparent error reporting when generation fails after all retries","Optimize retry strategy to balance resilience vs. latency"],"best_for":["Production systems requiring high availability and resilience to provider outages","Teams with rate-limited API quotas wanting to maximize successful requests","Long-running batch generation jobs where transient failures are common"],"limitations":["Retry logic adds latency (up to 15+ seconds for max retries); not suitable for real-time applications","Exponential backoff may not be optimal for all failure modes (e.g., rate limits may require longer delays)","No built-in circuit breaker pattern — will continue retrying even if provider is persistently down","Retry configuration is global; no per-provider or per-request customization"],"requires":["Python 3.8+","Provider API error codes and retry-ability classification","Retry configuration (max retries, backoff multiplier, max delay)"],"input_types":["API request (text generation, image generation)"],"output_types":["successful API response (after retries) or final error exception"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_7","uri":"capability://automation.workflow.vue.js.frontend.with.pinia.state.management.for.generation.workflow","name":"vue.js frontend with pinia state management for generation workflow","description":"Provides a Vue.js single-page application (SPA) frontend with Pinia state management that orchestrates the two-phase content generation workflow (outline → image generation). The frontend includes HomeView for topic input and reference image upload, SettingsView for provider configuration, and reactive UI components that display generation progress via SSE streaming. Pinia stores manage application state (generation status, generated images, provider settings) and enable cross-component communication without prop drilling.","intents":["Provide an intuitive UI for users to input topics and upload reference images","Display real-time generation progress and generated images","Allow users to configure AI providers and generation parameters without backend restart"],"best_for":["Non-technical content creators wanting a simple, visual interface for content generation","Teams wanting to customize the UI for their brand or platform"],"limitations":["Frontend is tightly coupled to backend API routes; changes to backend API require frontend updates","No built-in image editing or post-processing UI; users must edit images externally","State management is in-memory; page refresh loses generation history and progress","No offline support; requires constant backend connectivity"],"requires":["Node.js 14+ and npm/yarn","Vue.js 3.x","Pinia state management library","Backend Flask API running on localhost:5000 (configurable)"],"input_types":["text input (topic string)","file upload (reference images)","form inputs (provider selection, generation parameters)"],"output_types":["rendered UI components (image gallery, progress indicators, settings forms)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_8","uri":"capability://automation.workflow.docker.containerization.with.docker.compose.orchestration","name":"docker containerization with docker-compose orchestration","description":"Packages the entire RedInk application (Flask backend + Vue.js frontend) into Docker containers with docker-compose orchestration for easy deployment and scaling. The docker-compose.yml file defines services for the backend (Python Flask), frontend (Node.js build + Nginx reverse proxy), and optional services (Redis for caching, PostgreSQL for persistence). Enables one-command deployment ('docker-compose up') without manual dependency installation or configuration, with environment variables for provider credentials and configuration.","intents":["Deploy RedInk to production or staging environments without manual setup","Scale generation capacity by running multiple backend containers","Isolate dependencies and ensure reproducible deployments across environments"],"best_for":["DevOps teams deploying RedInk to cloud platforms (AWS, GCP, Azure, Kubernetes)","Organizations wanting containerized, reproducible deployments","Teams scaling generation capacity with load balancing"],"limitations":["Docker adds ~500ms-1s startup overhead per container; not suitable for serverless/FaaS deployments","Image size is large (~1GB+) due to Python dependencies and Node.js build tools; slow to push/pull in bandwidth-constrained environments","No built-in health checks or auto-scaling; requires external orchestration (Kubernetes, Docker Swarm) for production HA","Persistent storage (generated images) requires external volume mounts or cloud storage integration"],"requires":["Docker 20.10+","docker-compose 1.29+","Environment variables for provider credentials (GOOGLE_API_KEY, OPENAI_API_KEY, etc.)","Optional: Kubernetes cluster for production deployments"],"input_types":["docker-compose.yml configuration file","environment variables (.env file or docker-compose environment section)"],"output_types":["running Docker containers (backend, frontend, optional services)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hismax--redink__cap_9","uri":"capability://automation.workflow.high.concurrency.mode.for.batch.image.generation","name":"high-concurrency mode for batch image generation","description":"Supports high-concurrency image generation by allowing multiple image generation requests to be submitted in parallel to the backend, with configurable concurrency limits and request queuing. The system uses async/await patterns (Python asyncio or thread pools) to manage concurrent API calls to image generation providers, with per-provider rate limit handling to avoid exceeding API quotas. Enables batch generation of multiple posts (e.g., 10 posts × 6-9 images = 60-90 images) without sequential bottlenecks.","intents":["Generate multiple Xiaohongshu posts in parallel to reduce total generation time","Maximize throughput of image generation API calls within rate limit constraints","Support batch content creation workflows for content agencies or e-commerce sellers"],"best_for":["Content agencies generating 10+ posts per day","E-commerce sellers with high-volume product image generation needs","Teams with generous API quotas wanting to maximize throughput"],"limitations":["Concurrency is limited by provider rate limits (e.g., OpenAI: 3 requests/min for free tier); exceeding limits causes 429 errors","Memory usage scales with concurrency; high concurrency (50+ parallel requests) may exhaust server memory","No built-in request prioritization or fairness; all requests are treated equally","Concurrent requests to same provider may cause rate limit collisions; no intelligent request scheduling"],"requires":["Python 3.8+ with asyncio support","Provider API quotas sufficient for concurrent requests (e.g., OpenAI paid tier for >3 req/min)","Server resources (CPU, memory) to handle concurrent request processing"],"input_types":["batch generation requests (array of topics/outlines)"],"output_types":["concurrent image generation responses (images generated in parallel)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for at least one text generation provider (Google GenAI API key, OpenAI API key, or local Ollama endpoint)","Provider configuration file (YAML/JSON) specifying text model and endpoint","API credentials for at least one image generation provider (Google GenAI API key, Banana.dev API key, or OpenAI API key)","Provider configuration specifying image model, resolution, and style parameters","Outline data from Phase 1 (topic-to-outline generation)","Image generation provider with strong instruction-following (Google GenAI, OpenAI GPT-4V preferred)","Chinese text input (from outline generation phase)","Prompts with explicit text embedding instructions (e.g., 'embed the following Chinese text in the image: ...')","Flask web framework"],"failure_modes":["Outline generation latency depends on selected LLM provider (typically 2-10 seconds)","JSON parsing failures fall back to retry logic but may timeout on malformed LLM responses","No built-in prompt optimization — uses fixed templates, limiting customization of outline structure","Image generation latency is provider-dependent (Google GenAI: 5-15s, Banana Nano Pro: 10-30s per image)","Consistency across 6-9 images depends on prompt quality and LLM instruction-following; no explicit style transfer or LoRA fine-tuning","Reference image conditioning is optional and may not always influence output style as expected","No built-in A/B testing or quality scoring — all generated images are treated equally","Text rendering quality depends on image generation model's instruction-following; some models may ignore text embedding instructions","No control over font, size, or placement of embedded text; depends on model's interpretation of prompts","Chinese character support varies by provider; some models may struggle with rare characters or traditional Chinese","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31576783103516914,"quality":0.49,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2026-03-17T01:20:53Z"},"community":{"stars":5203,"forks":997,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=hismax--redink","compare_url":"https://unfragile.ai/compare?artifact=hismax--redink"}},"signature":"8hgor0VNoa/AVAl6u4gdpbl0rW7b1xJ6xxeMdj1NPBkRcnI4+dIqPtJ7lfFL0wdzcA/A2JPl/ZswyYuAhINdDw==","signedAt":"2026-06-22T06:46:22.166Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hismax--redink","artifact":"https://unfragile.ai/hismax--redink","verify":"https://unfragile.ai/api/v1/verify?slug=hismax--redink","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}