{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hugging-face-cli","slug":"hugging-face-cli","name":"Hugging Face CLI","type":"cli","url":"https://github.com/huggingface/huggingface_hub","page_url":"https://unfragile.ai/hugging-face-cli","categories":["cli-tools"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hugging-face-cli__cap_0","uri":"capability://data.processing.analysis.intelligent.file.download.with.automatic.caching.and.resume.support","name":"intelligent file download with automatic caching and resume support","description":"Downloads individual files or entire repository snapshots from the Hub with built-in caching layer that stores files locally, supports resumable downloads via HTTP range requests, and implements smart cache invalidation. Uses a content-addressed cache structure where files are stored by their blob hash, enabling deduplication across multiple model versions and automatic cleanup of unused files.","intents":["Download a specific model file without fetching the entire repository","Cache downloaded models locally to avoid re-downloading on subsequent runs","Resume interrupted downloads without starting from scratch","Manage disk space by cleaning up old cached model versions"],"best_for":["ML engineers building inference pipelines that load models repeatedly","Teams deploying models in bandwidth-constrained environments","Developers integrating Hugging Face models into production applications"],"limitations":["Cache directory must be writable; no in-memory-only mode for large models","Resume support depends on server HTTP/1.1 Range header support; some CDNs may not support resumable downloads","Cache invalidation is based on file hash; metadata-only updates (e.g., model card changes) don't trigger re-downloads"],"requires":["Python 3.8+","Network connectivity to Hugging Face Hub or configured mirror","Disk space for cache (configurable via HF_HOME environment variable)"],"input_types":["repository identifier (string: 'username/model-name')","file path within repository (string)","optional revision/branch/tag (string)"],"output_types":["local file path (string)","file content (bytes)","cache metadata (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_1","uri":"capability://data.processing.analysis.repository.snapshot.download.with.selective.file.filtering","name":"repository snapshot download with selective file filtering","description":"Downloads entire repository snapshots with optional filtering by file patterns, allowing developers to exclude large files (e.g., safetensors, ONNX variants) and download only needed components. Implements a two-pass strategy: first fetches repository metadata to enumerate files, then downloads only selected files in parallel, with automatic handling of symlinks and LFS pointers.","intents":["Download a model repository but exclude certain file types to save bandwidth","Fetch only tokenizer and config files without downloading model weights","Download multiple files in parallel to reduce total transfer time","Handle repositories with mixed storage backends (Git, Git-LFS, Xet)"],"best_for":["Data scientists prototyping with multiple model variants","CI/CD pipelines that need selective model components","Edge deployment scenarios with strict bandwidth/storage constraints"],"limitations":["Filtering is applied client-side after metadata fetch; no server-side filtering reduces initial request overhead","Parallel downloads are limited by default to avoid overwhelming Hub infrastructure; configurable but may trigger rate limiting","Symlinks are resolved locally; circular symlink detection is basic and may fail on complex repository structures"],"requires":["Python 3.8+","Hugging Face Hub API access (public or authenticated)","Sufficient disk space for filtered snapshot"],"input_types":["repository identifier (string)","optional allow_patterns (list of glob strings)","optional ignore_patterns (list of glob strings)","optional revision (string)"],"output_types":["local directory path (string)","file manifest with metadata (dict)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_10","uri":"capability://automation.workflow.cache.management.and.cleanup.with.disk.space.monitoring","name":"cache management and cleanup with disk space monitoring","description":"Provides utilities for inspecting and managing the local Hub cache directory, including cache size calculation, file listing by age/size, and automatic cleanup of old or unused files. Implements cache strategy with configurable retention policies (LRU, size-based, age-based). Monitors available disk space and warns before cache exceeds thresholds.","intents":["Check how much disk space is used by cached models","Delete old cached model versions to free up disk space","List all cached files with metadata (size, last accessed, model name)","Configure automatic cache cleanup based on disk space or age"],"best_for":["ML practitioners with limited disk space (laptops, edge devices)","CI/CD systems that run multiple model inference jobs and need cache cleanup","Teams managing shared compute resources with cache quotas"],"limitations":["Cache cleanup is manual or requires external scheduling; no built-in daemon for automatic cleanup","Cleanup policies are simple (LRU, size-based); no support for complex policies (e.g., keep models by task type)","Disk space monitoring is approximate; actual available space may differ due to filesystem overhead","Cache inspection requires iterating all files; slow on systems with thousands of cached files"],"requires":["Python 3.8+","Read/write access to cache directory (HF_HOME or ~/.cache/huggingface)"],"input_types":["cache directory path (string, optional; defaults to HF_HOME)","cleanup strategy (enum: 'lru', 'size', 'age')","cleanup threshold (integer: bytes or days)"],"output_types":["cache statistics dict (total_size, file_count, etc.)","file listing with metadata (list of dicts)","cleanup report (files deleted, space freed)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_11","uri":"capability://automation.workflow.command.line.interface.with.subcommand.routing.and.progress.reporting","name":"command-line interface with subcommand routing and progress reporting","description":"Provides a comprehensive CLI (huggingface-cli) with subcommands for all major Hub operations (login, download, upload, repo management). Implements progress bars for file operations, colored output for readability, and structured error messages. Uses argparse for command parsing with automatic help generation and shell completion support.","intents":["Login to Hub from terminal without Python code","Download a model from command line for use in non-Python applications","Upload a folder of files to Hub repository from shell script","Manage repository settings (visibility, tags) from terminal"],"best_for":["ML practitioners working in terminal-based environments","Shell scripts and CI/CD pipelines that need Hub integration","Teams without Python expertise who need Hub access"],"limitations":["CLI is less flexible than Python API; complex workflows require multiple command invocations","Progress reporting is terminal-dependent; may not work correctly in non-TTY environments (CI/CD logs)","Error messages are human-readable but not machine-parseable; scripts must parse stdout/stderr","Shell completion requires manual setup (bash/zsh profile modification)"],"requires":["Python 3.8+ with huggingface_hub installed","Terminal with ANSI color support (optional but recommended)"],"input_types":["command name (string: 'login', 'download', 'upload', etc.)","command arguments (strings)","optional flags (--token, --repo-type, etc.)"],"output_types":["command output (text to stdout)","progress bars (to stderr)","exit code (0 for success, non-zero for error)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_12","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.implementation.for.llm.integration","name":"model context protocol (mcp) server implementation for llm integration","description":"Implements MCP server that exposes Hub functionality (search, download, upload, inference) as tools callable by LLMs and AI agents. Provides structured tool definitions with JSON schemas for parameter validation. Enables LLMs to autonomously search for models, download files, and run inference without human intervention.","intents":["Enable an AI agent to search Hub for models matching specific criteria","Allow LLMs to download and inspect model cards programmatically","Provide LLMs with ability to run inference on Hub models","Enable autonomous model evaluation workflows driven by LLM agents"],"best_for":["AI agent developers building autonomous ML workflows","LLM applications that need to discover and use models dynamically","Research teams exploring LLM-driven model selection and evaluation"],"limitations":["MCP server requires separate process; adds latency compared to direct Python API calls","Tool definitions are static; no dynamic tool generation based on available models","LLM tool calling is probabilistic; agents may fail to use tools correctly or get stuck in loops","Security model assumes trusted LLM; no fine-grained access control per tool"],"requires":["Python 3.8+","MCP client (Claude, other LLM with MCP support)","Network connectivity between MCP server and client"],"input_types":["tool name (string: 'search_models', 'download_file', 'run_inference')","tool parameters (JSON object matching schema)"],"output_types":["tool result (JSON object)","error message (string, if tool fails)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_13","uri":"capability://automation.workflow.commit.api.with.atomic.multi.file.operations.and.conflict.resolution","name":"commit api with atomic multi-file operations and conflict resolution","description":"Provides a low-level commit API (create_commit) for atomic multi-file operations on Hub repositories. Implements conflict detection and resolution strategies (abort, overwrite, merge), file deletion via commit operations, and support for both HTTP and Git backends. Enables transactional semantics where multiple files are committed together or not at all.","intents":["Upload multiple files atomically (all succeed or all fail)","Delete files from repository without using Git","Resolve conflicts when multiple processes upload to same repository","Implement custom version control workflows on top of Hub"],"best_for":["Distributed training systems that need atomic model checkpoints","Multi-process applications uploading to Hub simultaneously","Custom ML platforms building on top of Hub infrastructure"],"limitations":["Commit API is lower-level than upload_file; requires manual file staging and commit message composition","Conflict resolution is limited to predefined strategies; no custom merge logic","Atomic semantics are best-effort; network failures during commit may leave partial state","Commit history is immutable; no support for amending or rebasing commits"],"requires":["Python 3.8+","Hugging Face token with write access","Understanding of commit-based version control concepts"],"input_types":["list of CommitOperation objects (add, delete, copy)","commit message (string)","optional parent commit hash (string)"],"output_types":["commit hash (string)","commit URL (string)","commit metadata (dict)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_2","uri":"capability://data.processing.analysis.http.based.file.upload.with.git.lfs.and.xet.backend.support","name":"http-based file upload with git-lfs and xet backend support","description":"Uploads files to Hub repositories via HTTP multipart requests with automatic routing to appropriate storage backend (standard Git, Git-LFS for large files, or Xet for deduplication). Implements chunked upload for large files, automatic LFS pointer generation, and conflict resolution via commit-based versioning. Supports both single-file and batch folder uploads with progress tracking.","intents":["Upload a trained model to a Hub repository without using Git","Upload large model weights (>100MB) with automatic LFS backend selection","Batch upload an entire training output directory with progress reporting","Create new model repositories and upload initial files in a single operation"],"best_for":["ML practitioners without Git expertise uploading models from notebooks","Automated training pipelines that need to persist models without Git overhead","Teams using Windows or environments where Git LFS is difficult to configure"],"limitations":["HTTP upload is slower than Git push for very large repositories (>10GB) due to lack of delta compression","Concurrent uploads to same repository may cause conflicts; requires manual merge or retry","LFS backend selection is automatic based on file size; no fine-grained control over which files use which backend","Upload progress tracking is approximate; actual bytes transferred may differ from reported due to compression"],"requires":["Python 3.8+","Hugging Face user token with write access to target repository","Network connectivity to Hub (no offline mode for uploads)"],"input_types":["local file path (string)","repository identifier (string)","optional commit message (string)","optional file metadata (dict)"],"output_types":["commit URL (string)","uploaded file metadata (dict)","commit hash (string)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_3","uri":"capability://tool.use.integration.unified.repository.operations.api.with.branch.and.tag.management","name":"unified repository operations api with branch and tag management","description":"Provides a Python API (HfApi class) for repository lifecycle management including creation, deletion, visibility changes, and branch/tag operations. Implements REST API calls to Hub backend with automatic error handling, retry logic, and permission validation. Supports both model and dataset repositories with identical interface patterns.","intents":["Create a new model repository programmatically without using web UI","Change repository visibility from private to public after training completes","Create release tags for model versions to enable reproducible deployments","List all repositories owned by a user with filtering by type or creation date"],"best_for":["MLOps engineers automating model release workflows","Research teams managing multiple model variants and versions","Organizations building internal model registries on top of Hub"],"limitations":["Repository creation requires valid organization context; personal repos have different permission model","Branch operations are limited to main branch and feature branches; no support for complex merge strategies","Tag operations don't support annotated tags with metadata; only lightweight refs are supported","Bulk operations (delete multiple repos) require sequential API calls; no batch endpoint"],"requires":["Python 3.8+","Hugging Face user token with appropriate permissions","Valid repository name (alphanumeric, hyphens, underscores only)"],"input_types":["repository name (string)","repository type (enum: 'model', 'dataset', 'space')","optional organization name (string)","optional private flag (boolean)"],"output_types":["repository metadata (dict with url, id, created_at, etc.)","branch/tag information (dict)","operation status (boolean or error)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_4","uri":"capability://search.retrieval.model.and.dataset.search.with.metadata.filtering.and.ranking","name":"model and dataset search with metadata filtering and ranking","description":"Implements search functionality across Hub repositories using server-side filtering and ranking. Supports filtering by task type, library, language, license, and custom metadata fields. Returns paginated results with metadata including model size, downloads, and last update time. Implements efficient pagination via cursor-based offsets rather than page numbers.","intents":["Find all vision transformer models fine-tuned for image classification","Search for datasets in a specific language with permissive licenses","Discover trending models by sorting by download count or recency","Filter models by inference framework (PyTorch, TensorFlow, ONNX) to match deployment requirements"],"best_for":["Researchers exploring available models for transfer learning","Data scientists building model selection pipelines","Teams evaluating multiple model candidates for a specific task"],"limitations":["Search is limited to indexed metadata; full-text search of model cards is not supported","Filtering is server-side but limited to predefined fields; custom metadata queries require client-side filtering","Ranking algorithms are fixed (download count, recency); no support for custom ranking functions","Pagination is cursor-based; jumping to arbitrary page numbers is not supported"],"requires":["Python 3.8+","Network connectivity to Hub search API","Optional: Hugging Face token for authenticated requests (higher rate limits)"],"input_types":["search query (string, optional)","filter parameters (dict with keys like 'task_ids', 'library_names', 'languages')","sort order (enum: 'downloads', 'trending', 'last_modified')","pagination cursor (string, optional)"],"output_types":["list of model/dataset metadata dicts","pagination cursor for next page (string)","total result count (integer)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_5","uri":"capability://tool.use.integration.inference.client.with.multi.provider.task.routing.and.streaming.support","name":"inference client with multi-provider task routing and streaming support","description":"Provides a unified Python interface (InferenceClient) for running inference on 35+ ML tasks across multiple providers (Hugging Face Inference API, Replicate, Together AI, Fal AI, SambaNova). Automatically routes requests to appropriate provider based on model availability and user configuration. Supports both synchronous and asynchronous execution, streaming responses for text generation, and structured output parsing.","intents":["Run inference on a model without downloading weights or managing GPU resources","Switch between inference providers (HF to Replicate) without changing application code","Stream text generation responses to enable real-time UI updates","Execute multiple inference tasks in parallel using async client"],"best_for":["Application developers building AI features without ML infrastructure","Teams evaluating multiple inference providers for cost/latency tradeoffs","Prototyping workflows that need rapid iteration without model deployment"],"limitations":["Provider routing is manual; no automatic failover if primary provider is unavailable","Streaming is supported for text generation but not all task types (e.g., image generation returns full response)","Async client requires event loop management; not suitable for synchronous-only applications","Provider-specific parameters (e.g., Replicate webhook URLs) are not abstracted; require provider-specific knowledge"],"requires":["Python 3.8+","API key for selected inference provider (HF token, Replicate key, Together API key, etc.)","Network connectivity to inference provider"],"input_types":["model identifier (string: 'username/model-name')","task-specific input (text, image, audio, etc.)","optional inference parameters (dict: temperature, max_tokens, etc.)"],"output_types":["task-specific output (text, image, structured JSON, etc.)","streaming iterator for text generation (AsyncIterator[str])","inference metadata (latency, provider, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_6","uri":"capability://text.generation.language.model.card.generation.and.management.with.structured.metadata","name":"model card generation and management with structured metadata","description":"Provides Python classes (ModelCard, DatasetCard, SpaceCard) for creating and managing repository documentation with structured YAML frontmatter. Automatically validates metadata against Hub schema, generates markdown templates, and syncs card content to repository. Supports programmatic metadata updates without manual YAML editing.","intents":["Generate a model card with standard metadata (model architecture, training data, limitations) from Python","Update model card metadata (tags, license) without manually editing YAML","Validate model card against Hub requirements before uploading","Create dataset cards with data provenance and licensing information"],"best_for":["ML practitioners documenting models for reproducibility and compliance","Organizations enforcing model documentation standards across teams","Automated training pipelines that generate model cards as part of release process"],"limitations":["Card validation is schema-based; custom metadata fields require manual YAML editing","Template generation is opinionated; limited customization of generated markdown structure","Card sync to repository requires separate upload operation; no automatic push on metadata changes","Markdown content is not validated; invalid markdown is accepted as long as YAML is valid"],"requires":["Python 3.8+","Hugging Face token with write access to repository (for sync operations)"],"input_types":["model metadata dict (keys: 'tags', 'license', 'model_name', etc.)","markdown content (string)","optional template name (string)"],"output_types":["ModelCard object with validated metadata","rendered markdown (string)","YAML frontmatter (string)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_7","uri":"capability://tool.use.integration.framework.agnostic.model.persistence.with.modelhubmixin.pattern","name":"framework-agnostic model persistence with modelhubmixin pattern","description":"Implements a mixin pattern (ModelHubMixin, PyTorchModelHubMixin, etc.) that adds push_to_hub() and from_pretrained() methods to any ML model class. Automatically handles model serialization, config file generation, and repository management. Supports framework-specific implementations for PyTorch, TensorFlow, Keras, and custom frameworks.","intents":["Add Hub integration to a custom PyTorch model with one-line inheritance","Save a trained model to Hub with automatic config and metadata generation","Load a model from Hub with automatic deserialization and device placement","Enable model versioning and release management without custom code"],"best_for":["ML researchers building custom model architectures","Teams standardizing model deployment across multiple frameworks","Open-source projects enabling users to share models easily"],"limitations":["Mixin pattern requires model class to inherit from ModelHubMixin; retrofitting existing classes requires wrapper","Serialization format is framework-specific; models saved with PyTorchModelHubMixin cannot be loaded with TensorFlow loader","Config generation is automatic but may miss custom hyperparameters; requires manual config.json editing for complex models","Device placement is automatic for PyTorch but may not match user's hardware (e.g., GPU vs CPU)"],"requires":["Python 3.8+","Target ML framework (PyTorch, TensorFlow, etc.)","Hugging Face token with write access for push_to_hub()"],"input_types":["model instance (any class inheriting from ModelHubMixin)","repository identifier (string)","optional config dict (framework-specific)"],"output_types":["model instance (loaded from Hub)","config dict (loaded from Hub)","commit URL (from push_to_hub)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_8","uri":"capability://data.processing.analysis.file.system.abstraction.layer.hffilesystem.with.fsspec.integration","name":"file system abstraction layer (hffilesystem) with fsspec integration","description":"Implements a POSIX-like file system interface (HfFileSystem) that wraps Hub repositories as virtual file systems, enabling use with fsspec-compatible tools. Supports standard operations (ls, cat, open, glob) on Hub files without downloading entire repositories. Integrates with Pandas, Polars, and other data tools that accept fsspec URLs.","intents":["Read a CSV dataset from Hub directly into Pandas without downloading the file","List files in a Hub repository using standard file system operations","Use glob patterns to find all .parquet files in a dataset repository","Stream large files from Hub without loading into memory"],"best_for":["Data scientists working with Hub datasets in Jupyter notebooks","Data pipelines using Pandas/Polars that need Hub data integration","Tools built on fsspec that want Hub support without custom adapters"],"limitations":["Write operations are not supported; HfFileSystem is read-only","Glob patterns are evaluated client-side after fetching directory listings; no server-side glob support","Streaming is supported but requires keeping file handle open; no automatic connection pooling","Symlinks and special files are not fully supported; behavior may differ from POSIX file systems"],"requires":["Python 3.8+","fsspec library (installed as dependency)","Optional: Hugging Face token for authenticated access to private repositories"],"input_types":["Hub URL (string: 'hf://repo-id/path/to/file')","file path (string)","glob pattern (string)"],"output_types":["file content (bytes or text)","file listing (list of dicts with metadata)","file handle (for streaming)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__cap_9","uri":"capability://safety.moderation.authentication.and.token.management.with.automatic.credential.detection","name":"authentication and token management with automatic credential detection","description":"Manages Hugging Face API tokens with automatic detection from environment variables, config files, and interactive login. Implements secure token storage in platform-specific locations (keyring on Linux/macOS, credential manager on Windows). Provides token validation and permission checking before operations.","intents":["Authenticate to Hub without hardcoding tokens in code","Store tokens securely in OS credential manager instead of plain text files","Validate token permissions before attempting authenticated operations","Support multiple tokens for different Hub accounts or organizations"],"best_for":["ML practitioners working in shared environments (notebooks, shared servers)","CI/CD pipelines that need secure token management","Teams with multiple Hub accounts needing token isolation"],"limitations":["Automatic credential detection follows fixed priority order (env var > config file > keyring); no custom priority configuration","Keyring integration requires OS-specific credential manager; may fail on headless servers or containers","Token validation is basic (format check); no real-time permission verification against Hub","Multiple tokens require manual switching; no automatic token selection based on repository ownership"],"requires":["Python 3.8+","Hugging Face account and API token","Optional: keyring library for secure storage (auto-installed)"],"input_types":["token string (optional; auto-detected if not provided)","token type (enum: 'user', 'org', 'write', 'read')"],"output_types":["authentication status (boolean)","user info dict (username, organizations, etc.)","token metadata (creation date, last used, etc.)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-cli__headline","uri":"capability://tool.use.integration.hugging.face.cli.for.model.and.dataset.management","name":"hugging face cli for model and dataset management","description":"The Hugging Face CLI is a command-line interface designed for managing machine learning models, datasets, and spaces, enabling users to upload, download, and search repositories seamlessly.","intents":["best CLI for managing ML models","Hugging Face CLI for dataset management","command line tool for Hugging Face Hub","how to use Hugging Face CLI","Hugging Face CLI features","CLI for uploading ML models"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Network connectivity to Hugging Face Hub or configured mirror","Disk space for cache (configurable via HF_HOME environment variable)","Hugging Face Hub API access (public or authenticated)","Sufficient disk space for filtered snapshot","Read/write access to cache directory (HF_HOME or ~/.cache/huggingface)","Python 3.8+ with huggingface_hub installed","Terminal with ANSI color support (optional but recommended)","MCP client (Claude, other LLM with MCP support)","Network connectivity between MCP server and client"],"failure_modes":["Cache directory must be writable; no in-memory-only mode for large models","Resume support depends on server HTTP/1.1 Range header support; some CDNs may not support resumable downloads","Cache invalidation is based on file hash; metadata-only updates (e.g., model card changes) don't trigger re-downloads","Filtering is applied client-side after metadata fetch; no server-side filtering reduces initial request overhead","Parallel downloads are limited by default to avoid overwhelming Hub infrastructure; configurable but may trigger rate limiting","Symlinks are resolved locally; circular symlink detection is basic and may fail on complex repository structures","Cache cleanup is manual or requires external scheduling; no built-in daemon for automatic cleanup","Cleanup policies are simple (LRU, size-based); no support for complex policies (e.g., keep models by task type)","Disk space monitoring is approximate; actual available space may differ due to filesystem overhead","Cache inspection requires iterating all files; slow on systems with thousands of cached files","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"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=hugging-face-cli","compare_url":"https://unfragile.ai/compare?artifact=hugging-face-cli"}},"signature":"eSSyhD8RzyPTSLSU1dQ7yuhGsIvUEzJOj34Su5P9q0aBzBP0vsg89y1/gRgJEuAVQTSa+HRbgweWp+6sTj2dDA==","signedAt":"2026-06-20T11:13:54.654Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hugging-face-cli","artifact":"https://unfragile.ai/hugging-face-cli","verify":"https://unfragile.ai/api/v1/verify?slug=hugging-face-cli","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"}}