{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"prodigy","slug":"prodigy","name":"Prodigy","type":"cli","url":"https://prodi.gy","page_url":"https://unfragile.ai/prodigy","categories":["data-pipelines","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"prodigy__cap_0","uri":"capability://automation.workflow.python.driven.recipe.based.annotation.pipeline.definition","name":"python-driven recipe-based annotation pipeline definition","description":"Prodigy uses a decorator-based recipe system (@prodigy.recipe) where Python functions define complete annotation workflows including data loading, label schema, UI configuration, and optional model predictions. Recipes are CLI-invoked with parameters (dataset name, source file, labels) that override function defaults, enabling rapid iteration without code changes. This approach treats annotation pipelines as first-class Python objects rather than configuration files, allowing full programmatic control over data flow and task generation.","intents":["Define custom annotation workflows without leaving Python","Parameterize annotation tasks to run the same recipe against different datasets and label sets","Integrate model predictions and active learning scoring into the annotation UI dynamically","Version control annotation pipelines alongside training code"],"best_for":["Python-fluent ML engineers building production NLP pipelines","Teams that treat annotation as code and want it in version control","Rapid prototyping workflows where annotation schema changes frequently"],"limitations":["Requires Python coding proficiency; non-technical annotators cannot modify recipes","Recipe complexity grows with custom data loaders and model integration logic","No visual recipe builder; all customization is code-based"],"requires":["Python 3.6+ (inferred from spaCy ecosystem)","Prodigy installed via pip","Understanding of Prodigy's @prodigy.recipe decorator API"],"input_types":["JSONL files","CSV files","Image folders","Audio/video files","Custom Python data iterables"],"output_types":["Annotation tasks streamed to web UI","Metadata passed to custom interfaces"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_1","uri":"capability://planning.reasoning.active.learning.with.model.assisted.annotation.and.uncertainty.scoring","name":"active learning with model-assisted annotation and uncertainty scoring","description":"Prodigy integrates external model predictions (from spaCy, transformers, or custom models) into the annotation UI to pre-populate labels and prioritize uncertain examples. The system accepts model predictions as JSON objects in the annotation stream and uses them to score task difficulty or confidence, though the specific uncertainty sampling algorithm and model retraining loop are not publicly documented. This reduces labeling effort by surfacing high-uncertainty examples first and providing model suggestions that annotators accept/reject.","intents":["Minimize annotation effort by focusing on examples the model is uncertain about","Seed annotation UI with model predictions to speed up labeling","Identify and correct model errors in a structured workflow","Build active learning loops where model retraining is triggered by annotation batches"],"best_for":["Teams with existing trained models (spaCy, transformers) wanting to improve them iteratively","Projects with large unlabeled datasets where random sampling is inefficient","Rapid iteration cycles where model → annotation → retrain loops are frequent"],"limitations":["Active learning algorithm details are undocumented; uncertainty scoring mechanism is proprietary/unknown","Requires external model to generate predictions; Prodigy does not train models itself","No built-in model retraining loop; users must export annotations and retrain externally","Model inference latency not documented; unclear if predictions are pre-computed or generated on-demand"],"requires":["Pre-trained model (spaCy, transformers, or custom) that outputs predictions","Predictions formatted as JSON objects with confidence/probability scores","Recipe that integrates model predictions into the annotation stream"],"input_types":["Model predictions as JSON (with confidence scores)","Unlabeled text/images from data source"],"output_types":["Ranked annotation tasks (sorted by uncertainty)","Annotation decisions (accept/reject/correct model predictions)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_10","uri":"capability://data.processing.analysis.annotation.statistics.and.quality.metrics.computation","name":"annotation statistics and quality metrics computation","description":"Prodigy provides a stats command (prodigy stats) that computes aggregate statistics over annotations in a dataset, including label distribution, annotation counts, and optionally agreement metrics if multiple annotators are present. The stats functionality is accessible via CLI and Python API, enabling users to monitor annotation progress and data quality without manual analysis. Statistics are computed directly from the SQLite database and can be filtered by dataset, label, or time range.","intents":["Monitor annotation progress and dataset composition","Identify label imbalance or underrepresented categories","Compute inter-annotator agreement if multiple annotators are used","Track annotation velocity and estimate time to completion"],"best_for":["Project managers tracking annotation progress","Data scientists analyzing label distribution before training","Quality assurance workflows monitoring annotator performance"],"limitations":["Available metrics are not fully documented; unclear which statistics are computed","No built-in inter-annotator agreement metrics (Cohen's kappa, Fleiss' kappa); unclear if these are supported","Statistics are computed on-demand; no historical tracking or trend analysis","No visualization; output is text-based or raw numbers"],"requires":["Annotations stored in Prodigy database","Dataset name","Optional: filter criteria (label, time range, etc.)"],"input_types":["Annotations from Prodigy database"],"output_types":["Text-based statistics (counts, percentages)","Numeric metrics (agreement scores, if supported)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_11","uri":"capability://image.visual.custom.html.javascript.interface.extension.for.domain.specific.annotation","name":"custom html/javascript interface extension for domain-specific annotation","description":"Prodigy allows users to create custom annotation interfaces by providing HTML and JavaScript that hooks into Prodigy's frontend API. Custom interfaces receive task data as JSON, render custom UI elements, and submit annotations back to Prodigy via JavaScript function calls. This enables domain-specific annotation UIs (e.g., custom graph visualization, timeline annotation, specialized medical imaging tools) without modifying Prodigy's core code. The custom interface mechanism is recipe-based and integrates with the same task streaming and database persistence as built-in interfaces.","intents":["Build specialized annotation UIs for domain-specific tasks (e.g., timeline annotation, graph labeling)","Integrate third-party visualization libraries (D3.js, Plotly, etc.) into annotation workflows","Create interactive annotation tools with custom interaction patterns","Extend Prodigy's built-in interfaces with additional features"],"best_for":["Projects with unique annotation requirements not covered by built-in interfaces","Teams with web development expertise willing to build custom UIs","Domain-specific applications (medical imaging, scientific data annotation, etc.)"],"limitations":["Custom interface API is not fully documented; integration points and available functions are unclear","Requires HTML/JavaScript knowledge; no visual interface builder","Custom interfaces must be tested and debugged manually; no built-in testing framework","Performance and responsiveness with complex visualizations is undocumented","No component library or reusable UI patterns; each custom interface is built from scratch"],"requires":["HTML/JavaScript knowledge","Understanding of Prodigy's custom interface API (documentation incomplete)","Recipe that specifies custom interface path","Optional: third-party JavaScript libraries for visualization"],"input_types":["Task data as JSON (text, images, structured data)"],"output_types":["Annotation objects submitted via JavaScript API"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_12","uri":"capability://code.generation.editing.integration.with.spacy.models.for.nlp.task.assistance","name":"integration with spacy models for nlp task assistance","description":"Prodigy is tightly integrated with spaCy (same vendor, Explosion AI) and can use spaCy models to pre-populate NER annotations, provide entity suggestions, and score prediction confidence. Recipes can load spaCy models and pass predictions to the annotation UI, where annotators accept, reject, or correct suggestions. This integration is documented through case studies and examples but the specific API for spaCy model integration is not fully detailed in provided documentation.","intents":["Accelerate NER annotation by pre-populating entity suggestions from spaCy models","Use spaCy model confidence scores for active learning (prioritizing uncertain predictions)","Iteratively improve spaCy models by annotating errors and retraining","Leverage pre-trained spaCy models to reduce annotation effort"],"best_for":["Teams using spaCy for NLP tasks wanting to improve models iteratively","NER projects where pre-trained models can provide useful suggestions","Rapid prototyping where spaCy models accelerate annotation"],"limitations":["Integration is specific to spaCy; other NLP frameworks require custom integration","spaCy model quality determines suggestion quality; poor models provide unhelpful suggestions","Model retraining loop is not automated; users must export annotations and retrain spaCy models externally","No documentation on how to integrate custom spaCy components or pipelines"],"requires":["spaCy installed (pip install spacy)","Pre-trained spaCy model (downloaded via spacy download)","Recipe that loads spaCy model and generates predictions"],"input_types":["Text for NER annotation","spaCy model predictions"],"output_types":["Corrected NER annotations","Training data for spaCy model retraining"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_13","uri":"capability://image.visual.image.annotation.with.bounding.boxes.segmentation.and.classification","name":"image annotation with bounding boxes, segmentation, and classification","description":"Prodigy supports computer vision annotation tasks including drawing bounding boxes on images, creating segmentation masks, and classifying images or regions. The image annotation interface allows users to draw rectangles or polygons on images and assign labels to regions or entire images. Annotations are stored with pixel coordinates and label information, enabling export for object detection or segmentation model training. The image annotation capability is built-in but details on supported image formats, coordinate systems, and export formats are not fully documented.","intents":["Annotate bounding boxes for object detection model training","Create segmentation masks for semantic or instance segmentation","Classify images or image regions with labels","Prepare computer vision datasets for model training"],"best_for":["Computer vision projects requiring object detection or segmentation annotations","Teams building image classification datasets","Rapid prototyping of vision models with annotated training data"],"limitations":["Image annotation interface details are not documented; unclear which drawing tools are available","Supported image formats are not specified (likely JPEG, PNG, but undocumented)","Export format for segmentation masks is undocumented","No built-in image preprocessing or augmentation","Performance with large images or high-resolution datasets is undocumented"],"requires":["Image files in supported format (JPEG, PNG, etc.)","Recipe configured for image annotation task","Image folder or list of image paths"],"input_types":["Image files (JPEG, PNG, etc.)","Optional: pre-computed bounding box suggestions"],"output_types":["Bounding box coordinates and labels","Segmentation masks (format undocumented)","Image classification labels"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_14","uri":"capability://image.visual.audio.and.video.annotation.task.support","name":"audio and video annotation task support","description":"Prodigy documentation mentions support for audio and video annotation as a task type, though specific details on the annotation interface, supported formats, and capabilities are not provided in available documentation. The audio/video annotation feature is listed in the docs navigation but implementation details are absent, suggesting it may be a documented but underdeveloped feature or require custom interface implementation.","intents":["Annotate audio for speech recognition or speaker identification","Label video segments for action recognition or object tracking","Create transcriptions or captions for audio/video content"],"best_for":["Projects involving audio or video annotation (if feature is fully implemented)"],"limitations":["Audio/video annotation details are not documented; unclear if this is a built-in feature or requires custom implementation","Supported audio/video formats are undocumented","Interface capabilities (playback controls, timeline scrubbing, etc.) are undocumented","Export formats for audio/video annotations are undocumented","This feature may be incomplete or in development"],"requires":["Audio or video files in supported format (undocumented)","Recipe configured for audio/video annotation task"],"input_types":["Audio files (format undocumented)","Video files (format undocumented)"],"output_types":["Annotations with timestamps and labels (format undocumented)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_15","uri":"capability://automation.workflow.lifetime.license.model.with.one.time.purchase.and.flexible.team.options","name":"lifetime license model with one-time purchase and flexible team options","description":"Prodigy uses a lifetime license model where users pay once for perpetual access, rather than a subscription-based SaaS model. The pricing structure offers flexible options for individuals and teams, though specific pricing tiers and team size limits are not documented in available materials. This contrasts with SaaS annotation platforms that charge recurring subscription fees, making Prodigy cost-effective for long-term projects.","intents":["Avoid recurring SaaS subscription costs for annotation tools","Purchase annotation software with predictable one-time cost","Deploy annotation tools across teams without per-user licensing fees"],"best_for":["Cost-conscious teams with long-term annotation needs","Organizations avoiding SaaS subscription models","Projects with uncertain duration where subscription costs are unpredictable"],"limitations":["Pricing tiers and team size limits are not documented; unclear what 'flexible options' means","No free tier or trial mentioned; unclear if trial access is available","Licensing model for commercial vs. non-commercial use is undocumented","No information on license transfer, renewal, or upgrade paths","Pricing is not publicly listed; requires contacting vendor"],"requires":["One-time purchase of Prodigy license","License key or activation mechanism (undocumented)"],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_2","uri":"capability://image.visual.multi.task.annotation.interface.with.task.specific.ui.templates","name":"multi-task annotation interface with task-specific ui templates","description":"Prodigy provides built-in annotation interfaces for common NLP tasks (NER span labeling, text classification, relation extraction, dependencies) and computer vision tasks (image bounding boxes, segmentation). Each interface is a pre-built HTML/JavaScript component that renders annotation tasks and captures user interactions (clicks, drags, selections). Users can select which interface to use via recipe parameters, and custom interfaces can be built by providing HTML/JavaScript that hooks into Prodigy's JavaScript API for task submission and UI state management.","intents":["Annotate named entities with span selection and label assignment","Classify entire documents or text spans with single or multi-label options","Draw bounding boxes or polygons on images for object detection or segmentation","Define relationships and dependencies between entities or spans","Create custom annotation interfaces for domain-specific tasks"],"best_for":["Teams annotating standard NLP tasks (NER, classification, relations) without custom UI needs","Computer vision projects requiring image annotation","Projects with unique annotation requirements that need custom HTML/JavaScript interfaces"],"limitations":["Built-in interfaces are fixed; customization requires writing HTML/JavaScript","Custom interface API is not fully documented; integration points with Prodigy's JavaScript runtime are unclear","No drag-and-drop interface builder; all custom interfaces must be hand-coded","Interface responsiveness and performance with large documents/images is undocumented"],"requires":["Selection of built-in interface (ner, textcat, relations, etc.) or custom HTML/JavaScript","For custom interfaces: understanding of Prodigy's JavaScript API and event handling","Annotation data formatted to match interface expectations (e.g., spans with start/end offsets for NER)"],"input_types":["Text with optional pre-computed spans/entities","Images with optional bounding box suggestions","Structured data with entity/relation metadata"],"output_types":["Annotation objects with selected labels, spans, or coordinates","User interaction metadata (time spent, corrections made)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_3","uri":"capability://data.processing.analysis.sqlite.backed.annotation.database.with.pluggable.storage.backends","name":"sqlite-backed annotation database with pluggable storage backends","description":"Prodigy stores all annotations in a SQLite database by default, with each annotation record containing the task data, user input, timestamp, and metadata. The database schema is managed automatically by Prodigy; users interact with annotations through the Python API or CLI commands (prodigy db-out, prodigy stats). The architecture supports pluggable database backends (mechanism undocumented), suggesting alternative storage systems can be integrated, though only SQLite is documented as officially supported.","intents":["Persist annotations durably across annotation sessions","Query and export annotations for model training or analysis","Track annotation history and metadata (timestamps, annotator identity if multi-user)","Integrate with external data warehouses or annotation management systems"],"best_for":["Single-machine or small-team annotation workflows where SQLite is sufficient","Projects requiring local data storage with no cloud connectivity","Teams that want to export annotations for external training pipelines"],"limitations":["SQLite has single-writer limitation; concurrent annotation by multiple users may cause contention","No built-in user/team management; database does not track which annotator created each annotation","Scale limits unknown; no performance benchmarks for large datasets (Nesta case mentions 7M job ads but unclear if all stored in single database)","Database plugins are mentioned but not documented; unclear which alternatives are supported or how to implement custom backends","No built-in backup or replication; data durability depends on filesystem"],"requires":["SQLite (included with Python)","Prodigy CLI or Python API to interact with database","Dataset name to organize annotations (Prodigy creates separate tables per dataset)"],"input_types":["Annotation objects from web UI","Bulk imports via Python API"],"output_types":["JSONL export via prodigy db-out","Statistics via prodigy stats","Raw SQLite database for direct querying"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_4","uri":"capability://data.processing.analysis.batch.annotation.export.and.format.conversion.for.model.training","name":"batch annotation export and format conversion for model training","description":"Prodigy provides CLI commands (prodigy db-out) and Python API methods to export annotations from the SQLite database in formats suitable for model training. Exported data includes the original text/image, user-provided labels, and optionally model predictions and metadata. The export mechanism supports filtering by dataset, label, or other criteria, enabling users to prepare training datasets without manual data wrangling. Specific export formats (JSONL, spaCy training format, etc.) are mentioned in case studies but not fully documented.","intents":["Export annotations to train spaCy or transformer models","Convert Prodigy annotations to standard formats (JSONL, CoNLL, etc.) for external tools","Filter and subset annotations for specific training tasks","Prepare evaluation datasets from annotated examples"],"best_for":["ML engineers exporting annotations to train spaCy or transformer models","Teams using external training frameworks (PyTorch, TensorFlow) that require standard data formats","Workflows where annotation and training are separate stages"],"limitations":["Export formats are not fully documented; unclear which formats are natively supported vs. require custom scripts","No built-in format conversion for non-standard task types (custom interfaces may require manual export logic)","Export performance with large datasets is undocumented","No streaming export; entire dataset must be loaded into memory"],"requires":["Annotations stored in Prodigy database","Dataset name and optional filter criteria","Target format specification (JSONL, spaCy, etc.)"],"input_types":["Annotations from Prodigy database"],"output_types":["JSONL files","spaCy training format (inferred from case studies)","Raw JSON objects"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_5","uri":"capability://planning.reasoning.a.b.evaluation.and.annotation.review.workflows","name":"a/b evaluation and annotation review workflows","description":"Prodigy supports review and comparison tasks where annotators evaluate existing annotations or compare model outputs side-by-side. The review interface allows users to accept, reject, or correct previous annotations, and A/B evaluation tasks can present two model predictions or annotation variants for comparison. This capability is built into the task routing system, enabling conditional workflows where review tasks are triggered based on annotation disagreement, model confidence, or other criteria.","intents":["Review and correct annotations from previous rounds or other annotators","Compare two model outputs or annotation variants to select the better one","Identify and resolve annotation disagreements in multi-annotator workflows","Evaluate model quality by comparing predictions to gold-standard annotations"],"best_for":["Quality assurance workflows where annotations are reviewed before training","Model evaluation where human judges compare predictions","Multi-annotator projects requiring consensus or arbitration"],"limitations":["Review workflow mechanics are not documented; unclear how disagreement detection or task routing works","No built-in inter-annotator agreement metrics (Cohen's kappa, Fleiss' kappa)","A/B evaluation interface details are undocumented","No support for multi-way comparisons (>2 options)"],"requires":["Existing annotations or model predictions to review","Recipe configured with review task type","Comparison criteria or disagreement detection logic"],"input_types":["Existing annotations from database","Model predictions for comparison"],"output_types":["Review decisions (accept/reject/correct)","Comparison results (A preferred over B, etc.)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_6","uri":"capability://automation.workflow.local.first.deployment.with.no.cloud.connectivity.or.telemetry","name":"local-first deployment with no cloud connectivity or telemetry","description":"Prodigy runs entirely on the user's machine or self-hosted infrastructure with no required connectivity to Prodigy's servers. The web UI is served locally (typically localhost:8080), and all data (annotations, models, configurations) remain on the user's hardware. Prodigy explicitly does not collect telemetry or phone home, making it suitable for privacy-sensitive or air-gapped environments. This architecture contrasts with SaaS platforms that require cloud accounts and data transmission.","intents":["Annotate sensitive data (healthcare, legal, financial) without transmitting to third-party servers","Deploy annotation workflows in air-gapped or offline environments","Maintain full control over data location and access","Avoid vendor lock-in and SaaS subscription costs"],"best_for":["Organizations with strict data privacy requirements (healthcare, finance, government)","Teams in regulated industries (HIPAA, GDPR, SOC 2 compliance)","Projects requiring offline or air-gapped annotation","Cost-sensitive teams avoiding SaaS subscription models"],"limitations":["Single-machine or self-hosted deployment limits scalability; no managed infrastructure","Users responsible for database backups, security patching, and infrastructure maintenance","No built-in user authentication or access control; assumes trusted local network","Multi-user annotation requires manual network setup or reverse proxy configuration","No cloud-based collaboration features (real-time sync, remote access)"],"requires":["Python 3.6+ and pip","Local machine or self-hosted server with sufficient disk space for SQLite database","Network access to localhost (or configured remote access via reverse proxy)"],"input_types":["Local files (JSONL, CSV, images)","Data from local databases or APIs"],"output_types":["Local SQLite database","Exported JSONL files"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_7","uri":"capability://planning.reasoning.conditional.task.routing.and.dynamic.workflow.branching","name":"conditional task routing and dynamic workflow branching","description":"Prodigy supports task routing where annotation workflows branch based on conditions (e.g., if model confidence is low, route to review task; if entity type is rare, route to specialized annotator). The routing mechanism is recipe-based and uses Python logic to determine which task to present next based on previous annotations or model predictions. This enables complex workflows where different examples follow different annotation paths without requiring separate datasets or manual task assignment.","intents":["Route low-confidence model predictions to review tasks automatically","Assign specialized annotation tasks based on entity type or document category","Implement multi-stage annotation workflows (initial annotation → review → final approval)","Optimize annotation effort by routing easy examples to fast-track paths"],"best_for":["Complex annotation workflows with multiple stages or task types","Projects where different examples require different annotation approaches","Quality assurance pipelines with automatic routing based on confidence or disagreement"],"limitations":["Task routing logic is recipe-based and not fully documented; implementation details are unclear","No visual workflow builder; routing logic must be coded in Python","Routing decisions are made at annotation time; no pre-computation or batch routing","No built-in metrics for routing effectiveness or task distribution"],"requires":["Recipe with conditional logic to determine task routing","Criteria for routing decisions (model confidence, entity type, previous annotations, etc.)","Multiple task types defined in the same recipe or separate recipes"],"input_types":["Annotation data with metadata for routing decisions","Model predictions with confidence scores"],"output_types":["Routed annotation tasks","Task assignment metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_8","uri":"capability://code.generation.editing.programmatic.dataset.and.annotation.management.via.python.api","name":"programmatic dataset and annotation management via python api","description":"Prodigy exposes a Python API (in addition to CLI) for creating datasets, adding annotations, querying the database, and managing annotation workflows programmatically. Users can import Prodigy as a Python library and call functions to interact with the database, enabling integration with Jupyter notebooks, training scripts, and custom automation. This API-first design allows annotation workflows to be embedded in larger ML pipelines without shell scripting or manual data export.","intents":["Integrate annotation workflows into Jupyter notebooks for exploratory analysis","Programmatically add annotations or model predictions to the database","Query annotations and compute statistics within Python training scripts","Automate annotation pipelines triggered by external events or model retraining"],"best_for":["ML engineers and data scientists working in Python","Jupyter-based workflows where annotation and analysis are interleaved","Automated pipelines where annotation is triggered by model training or data ingestion"],"limitations":["Python API is not fully documented; available functions and signatures are unclear","No async/concurrent API; blocking calls may limit throughput","API stability and backward compatibility guarantees are undocumented","Limited to Python; no language bindings for other languages"],"requires":["Python 3.6+","Prodigy installed as a Python package","Knowledge of Prodigy's Python API (documentation incomplete)"],"input_types":["Python objects (dicts, lists, custom classes)","Data from pandas DataFrames or other Python libraries"],"output_types":["Annotation objects","Query results (lists of annotations)","Statistics and metadata"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__cap_9","uri":"capability://data.processing.analysis.streaming.annotation.task.generation.from.dynamic.data.sources","name":"streaming annotation task generation from dynamic data sources","description":"Prodigy recipes can define data loaders that stream annotation tasks from various sources (JSONL files, CSV, image folders, APIs, databases) without loading the entire dataset into memory. Tasks are generated on-demand as the annotator progresses through the UI, enabling annotation of datasets larger than available RAM. The streaming architecture also supports integration with model prediction APIs, where predictions are fetched on-demand for each task rather than pre-computed.","intents":["Annotate large datasets that don't fit in memory","Fetch model predictions on-demand from external APIs during annotation","Stream data from live sources (databases, APIs) for continuous annotation","Reduce startup latency by generating tasks incrementally"],"best_for":["Large-scale annotation projects (millions of examples)","Continuous annotation workflows where data is ingested over time","Projects with external model prediction APIs that should not be pre-computed"],"limitations":["Streaming data loaders must be custom-implemented in recipes; no generic streaming loader","On-demand model prediction fetching adds latency per task (network round-trip)","No built-in caching or batching for prediction APIs","Streaming order is deterministic but not randomized; no built-in shuffling"],"requires":["Custom data loader function in recipe that yields annotation tasks","Data source (file, API, database) accessible from annotation machine","Optional: model prediction API for on-demand scoring"],"input_types":["JSONL files (streamed line-by-line)","CSV files","Image folders","API responses","Database queries"],"output_types":["Annotation tasks (streamed to UI)","Annotations (written to database)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prodigy__headline","uri":"capability://data.processing.analysis.scriptable.data.annotation.tool","name":"scriptable data annotation tool","description":"Prodigy is a powerful scriptable annotation tool designed for efficient data labeling, utilizing active learning to minimize effort in tasks like NER, text classification, and image annotation.","intents":["best data annotation tool","data annotation tool for machine learning","scriptable annotation tool for NER","annotation tool with active learning","CLI tool for data labeling"],"best_for":["data scientists","machine learning engineers"],"limitations":["requires Python environment"],"requires":["Python"],"input_types":["text","images"],"output_types":["annotations","labeled data"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+ (inferred from spaCy ecosystem)","Prodigy installed via pip","Understanding of Prodigy's @prodigy.recipe decorator API","Pre-trained model (spaCy, transformers, or custom) that outputs predictions","Predictions formatted as JSON objects with confidence/probability scores","Recipe that integrates model predictions into the annotation stream","Annotations stored in Prodigy database","Dataset name","Optional: filter criteria (label, time range, etc.)","HTML/JavaScript knowledge"],"failure_modes":["Requires Python coding proficiency; non-technical annotators cannot modify recipes","Recipe complexity grows with custom data loaders and model integration logic","No visual recipe builder; all customization is code-based","Active learning algorithm details are undocumented; uncertainty scoring mechanism is proprietary/unknown","Requires external model to generate predictions; Prodigy does not train models itself","No built-in model retraining loop; users must export annotations and retrain externally","Model inference latency not documented; unclear if predictions are pre-computed or generated on-demand","Available metrics are not fully documented; unclear which statistics are computed","No built-in inter-annotator agreement metrics (Cohen's kappa, Fleiss' kappa); unclear if these are supported","Statistics are computed on-demand; no historical tracking or trend analysis","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:25.060Z","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=prodigy","compare_url":"https://unfragile.ai/compare?artifact=prodigy"}},"signature":"nJoZ9kBZCLTNfKEdgihGTWjwKo321xsopULttA7jWKJD0pOX3ZMbppQ+ZuRKFS7ndHEiy+fkWlMHuUMxscQJAg==","signedAt":"2026-07-08T07:34:32.202Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/prodigy","artifact":"https://unfragile.ai/prodigy","verify":"https://unfragile.ai/api/v1/verify?slug=prodigy","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"}}