{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-emacs-org-mode-package","slug":"emacs-org-mode-package","name":"Emacs org-mode package","type":"repo","url":"https://github.com/rksm/org-ai","page_url":"https://unfragile.ai/emacs-org-mode-package","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-emacs-org-mode-package__cap_0","uri":"capability://text.generation.language.org.mode.block.based.conversational.ai.with.streaming.responses","name":"org-mode block-based conversational ai with streaming responses","description":"Enables users to create #+begin_ai...#+end_ai special blocks within org-mode documents that function as persistent conversation contexts. The system parses block syntax to extract configuration (model, temperature, system prompts), maintains conversation history as org-mode content, and streams responses directly into the buffer using Emacs' asynchronous request handling. The orchestration layer (org-ai.el) dispatches parsed blocks to service adapters which handle provider-specific API communication while maintaining buffer-local state for insertion positions and active requests.","intents":["I want to write and iterate on prompts directly in my org document without switching contexts","I need to maintain a persistent conversation history with an AI model within my notes","I want to see AI responses stream in real-time as they're generated, not wait for full completion","I need different AI configurations (model, temperature, system prompt) for different blocks in the same document"],"best_for":["Emacs power users who live in org-mode for note-taking and documentation","Researchers and writers iterating on content with AI assistance","Teams using org-mode for project planning and want AI-assisted task breakdown"],"limitations":["Conversation history is stored as plain org-mode text, not in a structured database — no built-in search or analytics across conversations","Streaming responses require asynchronous Emacs operations which can block UI on slower machines","Block syntax parsing is org-mode specific — cannot be used in other Emacs buffers without custom integration","No automatic conversation pruning — long conversations can make org files unwieldy"],"requires":["Emacs 27.1+ (for async request support)","org-mode 9.1+","API key for at least one supported AI service (OpenAI, Anthropic, Google Gemini, etc.)","Network connectivity for cloud-based providers"],"input_types":["org-mode block syntax with configuration headers","plain text prompts and conversation history","code snippets embedded in org blocks"],"output_types":["streamed text responses inserted into org buffer","structured org-mode content (headings, lists, code blocks)","conversation history as org-mode text"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_1","uri":"capability://tool.use.integration.multi.provider.ai.service.abstraction.with.unified.request.interface","name":"multi-provider ai service abstraction with unified request interface","description":"Abstracts 8+ AI service providers (OpenAI, Anthropic, Google Gemini, Perplexity, DeepSeek, Azure OpenAI, local Oobabooga, Stable Diffusion) behind a single unified request interface. The org-ai-openai.el adapter module handles provider-specific API details including authentication, request formatting, response parsing, and error handling. Service selection is configured globally or per-block, and the dispatcher (org-ai-complete-block) routes requests to the appropriate adapter without requiring users to understand provider-specific APIs.","intents":["I want to switch between different AI providers without rewriting my prompts or configuration","I need to use OpenAI for text but Stable Diffusion for images, with a single configuration system","I want to compare responses from Claude vs GPT-4 by changing a single parameter","I need to support both cloud and local LLMs in the same workflow"],"best_for":["Teams evaluating multiple AI providers and wanting to avoid vendor lock-in","Users with local LLM infrastructure (Oobabooga) who want cloud fallback options","Organizations with existing relationships with multiple AI vendors"],"limitations":["Abstraction layer cannot expose all provider-specific features — advanced parameters like OpenAI's vision_detail or Anthropic's extended thinking require custom configuration","Error handling is generic — provider-specific errors (rate limits, quota exceeded) are normalized, losing context","Response format normalization adds ~50-100ms latency per request","Local LLM support (Oobabooga) requires manual setup and is not auto-discovered"],"requires":["Emacs 27.1+","API keys for desired providers (OpenAI, Anthropic, Google, etc.)","For local LLMs: Oobabooga/text-generation-webui running on accessible network","For Stable Diffusion: AUTOMATIC1111 WebUI instance accessible via HTTP"],"input_types":["unified request objects with provider-agnostic parameters (model, temperature, max_tokens)","provider-specific configuration overrides"],"output_types":["normalized response objects with text content and metadata","streaming response chunks with consistent format across providers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_10","uri":"capability://safety.moderation.authentication.and.api.key.management.with.secure.credential.storage","name":"authentication and api key management with secure credential storage","description":"Manages API credentials for multiple AI services through Emacs' auth-source library, supporting encrypted credential storage in .authinfo.gpg or system keychains. Users configure service endpoints and credential lookup patterns, and the system retrieves credentials at request time without exposing them in configuration files. Supports per-service authentication and fallback mechanisms for multiple API keys.","intents":["I want to store my API keys securely without putting them in plain text config files","I need to use different API keys for different services (OpenAI, Anthropic, etc.)","I want to share my org-ai configuration with teammates without exposing credentials","I need to rotate API keys without editing configuration files"],"best_for":["Teams sharing configuration files and needing secure credential management","Users with multiple API keys for different services","Organizations with security policies requiring encrypted credential storage"],"limitations":["Auth-source integration requires GPG setup for encrypted storage — adds complexity","Credential lookup adds ~100-200ms latency per request","No built-in credential rotation or expiration management","Fallback mechanisms are not automatic — require manual configuration","System keychain integration varies by OS (macOS Keychain, Linux Secret Service, Windows Credential Manager)"],"requires":["Emacs 27.1+","auth-source library (built-in to modern Emacs)","For encrypted storage: GPG installed and configured","API keys for desired services"],"input_types":["credential lookup patterns (service name, host, user)","encrypted credential storage"],"output_types":["retrieved API credentials for request authentication","authentication headers for API calls"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_11","uri":"capability://text.generation.language.local.llm.support.with.oobabooga.text.generation.webui.integration","name":"local llm support with oobabooga text-generation-webui integration","description":"Integrates with Oobabooga's text-generation-webui for running local LLMs without cloud API dependencies. The org-ai-oobabooga.el adapter communicates with the WebUI API, supporting model selection, parameter configuration, and streaming responses. Users can switch between cloud and local models using identical org-mode syntax, enabling privacy-preserving and cost-effective AI workflows for users with local GPU infrastructure.","intents":["I want to run AI models locally to avoid API costs and maintain privacy","I need to use open-source models like Llama or Mistral without cloud dependencies","I want to switch between cloud and local models without changing my workflow","I need to fine-tune models on proprietary data without sending it to cloud services"],"best_for":["Users with local GPU infrastructure (NVIDIA, AMD, Apple Silicon)","Organizations with privacy requirements or data sensitivity","Researchers experimenting with open-source models","Cost-conscious teams running high-volume AI operations"],"limitations":["Requires local GPU with sufficient VRAM (6GB+ for small models, 24GB+ for large models)","Model inference is slower than cloud APIs — 5-50 tokens/second vs 50-100+ for cloud","Oobabooga setup and model management is manual — no automatic model discovery","Limited model selection compared to cloud providers — only open-source models","No built-in fine-tuning or training support — requires external tools","Streaming responses depend on local hardware performance"],"requires":["Emacs 27.1+","Oobabooga/text-generation-webui installed and running","GPU with sufficient VRAM (6GB+ recommended)","Network access to Oobabooga instance (localhost or network address)"],"input_types":["model name and parameters compatible with Oobabooga","text prompts"],"output_types":["generated text from local model","streaming response chunks"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_12","uri":"capability://memory.knowledge.org.mode.content.embedding.and.link.management.for.conversation.persistence","name":"org-mode content embedding and link management for conversation persistence","description":"Manages conversation history and AI responses as native org-mode content with automatic link creation and metadata tracking. Responses are inserted as org headings, lists, or code blocks depending on content type, and metadata (timestamp, model, tokens used) is stored as org properties. Supports linking between related conversations and organizing conversations hierarchically within org files.","intents":["I want to keep my AI conversations organized in my existing org-mode notes","I need to link related AI conversations and track how ideas evolved","I want to search my conversation history using org-mode search and tags","I need to export conversations to other formats (PDF, HTML) along with my notes"],"best_for":["Researchers and writers maintaining conversation history alongside notes","Teams using org-mode for project documentation and wanting AI-assisted content","Users wanting to analyze conversation patterns and evolution over time"],"limitations":["Org-mode content is plain text — no structured conversation metadata beyond properties","No built-in conversation search or analytics — requires org-mode search tools","Conversation organization is manual — no automatic categorization or tagging","Large conversations can make org files unwieldy — no automatic archiving","Link management is manual — no automatic link creation or maintenance"],"requires":["Emacs 27.1+","org-mode 9.1+"],"input_types":["AI responses as text","metadata (timestamp, model, tokens)"],"output_types":["org-mode headings, lists, or code blocks","org properties with metadata","internal org links between conversations"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_2","uri":"capability://text.generation.language.speech.to.text.and.text.to.speech.integration.with.bidirectional.voice.i.o","name":"speech-to-text and text-to-speech integration with bidirectional voice i/o","description":"Integrates OpenAI Whisper API for speech-to-text transcription and platform-native TTS (macOS say, espeak, greader) for text-to-speech output through the org-ai-talk.el module. Users can invoke voice input to generate prompts or voice output to hear AI responses read aloud. The system handles audio encoding/decoding, manages Whisper API communication, and coordinates with system TTS engines, enabling hands-free AI interaction workflows.","intents":["I want to dictate prompts to the AI without typing, especially for quick brainstorming","I need to hear AI responses read aloud while I'm away from the keyboard","I want to create voice-based workflows for accessibility or multitasking scenarios","I need to transcribe voice notes into org-mode for later processing"],"best_for":["Users with accessibility needs (visual impairment, RSI)","Developers and writers who prefer voice-based brainstorming","Mobile or remote workers who need hands-free interaction"],"limitations":["Speech-to-text requires Whisper API calls which add 2-5 second latency per utterance","TTS quality varies by platform — macOS say is higher quality than espeak","No built-in audio recording UI — requires external tools or manual audio file handling","Whisper API has per-minute rate limits and costs ~$0.02 per minute of audio","Language support depends on Whisper model capabilities (works well for English, variable for others)"],"requires":["Emacs 27.1+","OpenAI API key for Whisper (speech-to-text only)","macOS (for say), or espeak/greader installed on Linux","Audio input device for speech capture","Network connectivity for Whisper API"],"input_types":["audio files (WAV, MP3, M4A, FLAC, OGG via Whisper)","text content from org buffer"],"output_types":["transcribed text inserted into org buffer","audio output from system TTS engine"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_3","uri":"capability://image.visual.image.generation.with.dall.e.and.stable.diffusion.integration","name":"image generation with dall-e and stable diffusion integration","description":"Provides image generation capabilities through two separate adapters: org-ai-openai-image.el for OpenAI DALL-E and org-ai-sd.el for local Stable Diffusion (AUTOMATIC1111 WebUI). Users specify image prompts in org-mode blocks with configuration for size, quality, and style. The system sends requests to the appropriate service, downloads/retrieves generated images, and embeds them as org-mode image links in the document. Supports both cloud-based (DALL-E) and self-hosted (Stable Diffusion) workflows.","intents":["I want to generate images from text descriptions and embed them directly in my org document","I need to use Stable Diffusion locally to avoid API costs and maintain privacy","I want to compare DALL-E and Stable Diffusion outputs for the same prompt","I need to generate multiple image variations and organize them in my notes"],"best_for":["Content creators and designers iterating on visual concepts","Teams with privacy requirements who need local image generation","Users wanting cost-effective image generation at scale"],"limitations":["DALL-E API costs $0.04-0.20 per image depending on resolution and quality","Stable Diffusion requires local GPU (VRAM 6GB+) or significant CPU time for generation","Generated images are embedded as links, not stored in org file — requires external image management","No built-in image editing or refinement — requires external tools for post-processing","DALL-E has usage policies restricting certain content types"],"requires":["Emacs 27.1+","For DALL-E: OpenAI API key","For Stable Diffusion: AUTOMATIC1111 WebUI running on accessible network with GPU","Image viewing capability in Emacs (built-in or via external viewer)"],"input_types":["text prompts describing desired image","configuration parameters (size, quality, style, negative prompts)"],"output_types":["image files (PNG, JPEG)","org-mode image links [[file:path/to/image.png]]","image metadata (generation parameters, timestamp)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_4","uri":"capability://automation.workflow.block.level.configuration.with.per.request.model.and.parameter.overrides","name":"block-level configuration with per-request model and parameter overrides","description":"Allows fine-grained configuration at the individual org-mode block level through special syntax headers (#+ai_model, #+ai_temperature, #+ai_system_prompt, etc.). The block parser (org-ai-block.el) extracts these headers and merges them with global configuration, creating a request-specific configuration object. This enables users to use different models, temperatures, and system prompts for different blocks without global reconfiguration, supporting experimentation and multi-purpose workflows within a single org file.","intents":["I want to use GPT-4 for one block and GPT-3.5 for another to balance cost and quality","I need different system prompts for different types of tasks (coding vs writing vs analysis)","I want to experiment with different temperature settings for the same prompt","I need to override global settings for a specific block without affecting others"],"best_for":["Researchers and developers experimenting with different model configurations","Teams with heterogeneous AI needs in a single project","Users optimizing cost by using cheaper models for simple tasks"],"limitations":["Configuration is stored as org-mode text headers, not in a structured format — no validation until request time","No configuration inheritance or templating — each block must specify all overrides explicitly","Configuration changes require editing org file, not runtime UI","No version control or history of configuration changes per block"],"requires":["Emacs 27.1+","org-mode 9.1+","Understanding of org-mode header syntax"],"input_types":["org-mode block headers with configuration key-value pairs","global configuration fallbacks"],"output_types":["merged configuration object used for API request","configuration validation errors"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_5","uri":"capability://code.generation.editing.multi.file.project.operations.with.context.aggregation","name":"multi-file project operations with context aggregation","description":"Enables AI operations across multiple files in a project through global commands that aggregate file content as context. Users can invoke commands like 'refactor this codebase' or 'summarize project documentation' which collect relevant files, construct a unified prompt with file content, and send to the AI service. The system handles file discovery, content aggregation, and response insertion back into appropriate files or a summary buffer.","intents":["I want to refactor a codebase and have the AI understand the full project structure","I need to generate documentation by analyzing multiple source files at once","I want to ask the AI questions about my entire project, not just a single file","I need to apply consistent changes across multiple files based on AI suggestions"],"best_for":["Developers working on multi-file codebases","Technical writers documenting large projects","Teams performing bulk refactoring or migration tasks"],"limitations":["Context aggregation can exceed token limits for large projects — requires manual file selection or chunking","No built-in project structure understanding — treats all files as flat text","Response insertion is manual or requires custom logic — no automatic file patching","File discovery relies on Emacs project.el or manual specification — may miss relevant files","No caching of file content — re-aggregates on each request even if files haven't changed"],"requires":["Emacs 27.1+","project.el or manual project root specification","Sufficient API token limits for aggregated context"],"input_types":["file paths or project root directory","file selection patterns or explicit file lists","aggregated file content as context"],"output_types":["AI analysis or refactoring suggestions","modified file content","summary or report in org buffer"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_6","uri":"capability://automation.workflow.asynchronous.streaming.response.handling.with.buffer.insertion","name":"asynchronous streaming response handling with buffer insertion","description":"Implements non-blocking asynchronous request handling using Emacs' async request library, allowing AI responses to stream into the buffer without freezing the editor. The system maintains buffer-local state (insertion position, current request ID) to coordinate streaming chunks, handles response parsing from provider APIs, and inserts text incrementally as it arrives. Supports cancellation of in-flight requests and graceful error handling without blocking user interaction.","intents":["I want to see AI responses appear in real-time as they're generated, not wait for full completion","I need to continue editing while an AI request is processing in the background","I want to cancel a long-running request if I realize the prompt was wrong","I need to handle network errors gracefully without losing my work"],"best_for":["Users with slow network connections who benefit from streaming feedback","Developers iterating rapidly on prompts and wanting immediate feedback","Users with long-running requests (summarization, analysis) who need to multitask"],"limitations":["Streaming adds complexity to error handling — partial responses may be inserted before error occurs","Buffer insertion during streaming can cause performance issues with very large responses (>100KB)","Cancellation is not always immediate — in-flight API requests may continue processing","Streaming requires provider support — not all services support streaming responses","Buffer-local state management can conflict with multiple simultaneous requests in same buffer"],"requires":["Emacs 27.1+ (for async request support)","Provider support for streaming responses (OpenAI, Anthropic support; others may not)"],"input_types":["async HTTP request objects","streaming response chunks from provider APIs"],"output_types":["incremental text insertion into org buffer","completion status and metadata"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_7","uri":"capability://automation.workflow.yasnippet.template.integration.for.prompt.engineering","name":"yasnippet template integration for prompt engineering","description":"Integrates with Emacs' yasnippet library to provide pre-built templates for common AI interaction patterns (code review, documentation generation, refactoring, etc.). Users can insert templates that expand into org-mode blocks with pre-configured prompts, system messages, and parameters. Templates support variable substitution and can be customized per-user or per-project, enabling rapid creation of well-structured AI requests without manual prompt engineering.","intents":["I want to quickly create a code review prompt without typing the full template","I need consistent prompt structure across my team for similar tasks","I want to reuse successful prompts from previous projects","I need to experiment with different prompt structures without starting from scratch"],"best_for":["Teams with standardized AI workflows and prompt templates","Users who frequently perform similar AI tasks (code review, documentation, etc.)","Organizations wanting to enforce prompt quality standards"],"limitations":["Templates are static text — no dynamic content generation or conditional logic","Yasnippet integration requires yasnippet to be installed and configured","Template discovery is not built-in — users must know template names or browse files","No template versioning or sharing mechanism — templates are local files","Templates cannot access runtime context (current file, project structure) automatically"],"requires":["Emacs 27.1+","yasnippet package installed and configured","Template files in yasnippet directory"],"input_types":["yasnippet template syntax with variable placeholders","user input for template variable substitution"],"output_types":["expanded org-mode blocks with pre-configured prompts","template instances ready for execution"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_8","uri":"capability://text.generation.language.custom.prompt.engineering.with.system.message.configuration","name":"custom prompt engineering with system message configuration","description":"Allows users to define custom system prompts (#+ai_system_prompt header) that shape AI behavior for specific tasks. System prompts are merged with user prompts at request time and sent to the AI service as system-level instructions. Supports multi-line system prompts, variable substitution, and per-block customization, enabling users to create specialized AI personas or task-specific behaviors without modifying the core system.","intents":["I want to create a specialized AI assistant for code review with specific criteria","I need the AI to adopt a particular writing style or tone for documentation","I want to enforce specific output formats or constraints on AI responses","I need to create domain-specific AI assistants (legal, medical, technical) with custom instructions"],"best_for":["Users with specific domain expertise who want to encode it in system prompts","Teams with standardized AI interaction patterns and requirements","Organizations building specialized AI workflows"],"limitations":["System prompt effectiveness depends on model capability — not all models follow instructions equally well","No validation of system prompt quality — poorly written prompts may not produce desired results","System prompts count against token limits — very long prompts reduce available context for user input","No built-in testing or evaluation of system prompt effectiveness","System prompts are stored as plain text — no version control or change tracking"],"requires":["Emacs 27.1+","org-mode 9.1+","Understanding of prompt engineering principles"],"input_types":["multi-line system prompt text","variable placeholders for substitution"],"output_types":["merged system + user prompt sent to AI service","AI responses shaped by system prompt instructions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-emacs-org-mode-package__cap_9","uri":"capability://automation.workflow.error.handling.and.request.cancellation.with.graceful.degradation","name":"error handling and request cancellation with graceful degradation","description":"Implements comprehensive error handling for API failures, network issues, and malformed requests with user-facing error messages and recovery options. Supports request cancellation mid-stream, timeout handling, and retry logic for transient failures. Errors are displayed in org buffer or minibuffer without disrupting editor state, and users can cancel in-flight requests or adjust configuration and retry.","intents":["I want to cancel a request that's taking too long or using the wrong model","I need clear error messages when API calls fail so I can fix the problem","I want automatic retry for transient network failures","I need to handle rate limiting gracefully without losing my work"],"best_for":["Users with unreliable network connections","Teams hitting API rate limits and needing retry strategies","Users experimenting with different models and wanting quick failure feedback"],"limitations":["Retry logic is basic — no exponential backoff or sophisticated retry strategies","Error messages may be provider-specific and not always user-friendly","Cancellation doesn't always stop API processing — request may continue on server","No built-in rate limit tracking — users must manually manage quota","Partial responses from failed requests may remain in buffer"],"requires":["Emacs 27.1+","Network connectivity (for error detection)"],"input_types":["API error responses","network timeout signals","user cancellation requests"],"output_types":["error messages in org buffer or minibuffer","recovery suggestions","request cancellation confirmation"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Emacs 27.1+ (for async request support)","org-mode 9.1+","API key for at least one supported AI service (OpenAI, Anthropic, Google Gemini, etc.)","Network connectivity for cloud-based providers","Emacs 27.1+","API keys for desired providers (OpenAI, Anthropic, Google, etc.)","For local LLMs: Oobabooga/text-generation-webui running on accessible network","For Stable Diffusion: AUTOMATIC1111 WebUI instance accessible via HTTP","auth-source library (built-in to modern Emacs)","For encrypted storage: GPG installed and configured"],"failure_modes":["Conversation history is stored as plain org-mode text, not in a structured database — no built-in search or analytics across conversations","Streaming responses require asynchronous Emacs operations which can block UI on slower machines","Block syntax parsing is org-mode specific — cannot be used in other Emacs buffers without custom integration","No automatic conversation pruning — long conversations can make org files unwieldy","Abstraction layer cannot expose all provider-specific features — advanced parameters like OpenAI's vision_detail or Anthropic's extended thinking require custom configuration","Error handling is generic — provider-specific errors (rate limits, quota exceeded) are normalized, losing context","Response format normalization adds ~50-100ms latency per request","Local LLM support (Oobabooga) requires manual setup and is not auto-discovered","Auth-source integration requires GPG setup for encrypted storage — adds complexity","Credential lookup adds ~100-200ms latency per request","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.039Z","last_scraped_at":"2026-05-03T14:00:05.262Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=emacs-org-mode-package","compare_url":"https://unfragile.ai/compare?artifact=emacs-org-mode-package"}},"signature":"CJInupHEFcmE4+BH4vsBD5mTMJhEtmkDeJtFjH4o5pPjgg1kQwSwMdNJDTiEYDpH65Y2mGqNxOfqvEk+3fq3CQ==","signedAt":"2026-06-22T19:03:52.558Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/emacs-org-mode-package","artifact":"https://unfragile.ai/emacs-org-mode-package","verify":"https://unfragile.ai/api/v1/verify?slug=emacs-org-mode-package","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"}}