{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"singer","slug":"singer","name":"Singer","type":"repo","url":"https://www.singer.io","page_url":"https://unfragile.ai/singer","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"singer__cap_0","uri":"capability://data.processing.analysis.json.based.protocol.data.exchange.between.extractors.and.loaders","name":"json-based protocol data exchange between extractors and loaders","description":"Singer defines a standardized JSON message protocol (SCHEMA, RECORD, STATE, ACTIVATE_VERSION) that enables any data extraction tool (tap) to pipe output directly into any data loading tool (target) without custom integration code. Messages flow via stdout/stdin using Unix pipes, with each message type serving a specific function: SCHEMA defines table structure using JSON Schema, RECORD contains individual data rows, STATE checkpoints extraction progress for resumability, and ACTIVATE_VERSION manages versioning. This protocol-first design decouples extractors from loaders, allowing composition of 200+ community connectors without modification.","intents":["I want to move data from Salesforce to PostgreSQL without writing custom code","I need to extract from multiple SaaS APIs and consolidate into a single data warehouse","I want to build a reusable data connector that works with any Singer-compatible destination"],"best_for":["Data engineers building ETL pipelines with heterogeneous sources/destinations","Teams wanting to avoid vendor lock-in by using open protocol standards","Organizations leveraging existing Singer ecosystem (200+ maintained connectors)"],"limitations":["Protocol does not specify error handling semantics — error propagation between tap and target is implementation-dependent","No built-in schema evolution handling — breaking schema changes may require manual intervention","JSON serialization overhead adds latency per message; not optimized for sub-millisecond latency requirements","Protocol assumes stateless tap/target execution — complex stateful transformations must occur outside Singer"],"requires":["Tap implementation (Python package or CLI in any language)","Target implementation (Python package or CLI in any language)","Unix-like shell environment supporting pipes (bash, zsh, sh)","Understanding of JSON Schema for SCHEMA message validation"],"input_types":["JSON messages (SCHEMA, RECORD, STATE, ACTIVATE_VERSION)","stdin stream from tap executable"],"output_types":["JSON messages (SCHEMA, RECORD, STATE, ACTIVATE_VERSION)","stdout stream to target executable"],"categories":["data-processing-analysis","integration-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_1","uri":"capability://data.processing.analysis.incremental.data.extraction.with.state.checkpointing","name":"incremental data extraction with state checkpointing","description":"Singer taps emit STATE messages containing extraction progress metadata (e.g., last-synced timestamp, cursor position, offset) that targets write to persistent storage. On subsequent runs, taps read the previous STATE and resume extraction from that checkpoint rather than re-extracting all data. This pattern enables efficient incremental syncs without requiring the tap to maintain state itself — state is external and passed via messages. Taps can implement various incremental strategies: timestamp-based (modified_at > last_sync), cursor-based (id > last_id), or API-native pagination tokens, all serialized in the STATE message as JSON.","intents":["I want to sync only new/changed records from Stripe since the last run to avoid re-processing","I need to resume a failed extraction from where it stopped without losing progress","I want to implement incremental syncs across multiple data sources with different cursor strategies"],"best_for":["Data teams running scheduled syncs (hourly, daily) where full refresh is prohibitively expensive","Pipelines extracting from high-volume sources (millions of records) where incremental is mandatory","Organizations needing resumable extractions that survive network failures or target downtime"],"limitations":["STATE message format is tap-specific — no standardized schema, making state inspection/debugging difficult","Requires tap to implement incremental logic; not all sources support efficient incremental extraction (e.g., some APIs lack timestamp filters)","Soft deletes and record updates may not be captured if tap only tracks insertion time, not modification time","State storage is target-responsibility — if target fails to persist STATE, resumability is lost","No built-in conflict resolution if STATE is corrupted or manually edited"],"requires":["Tap implementation supporting incremental extraction (not all 200+ taps support this)","Target implementation that persists STATE messages to durable storage","Source system supporting filtering by timestamp, cursor, or pagination token","Agreement on STATE message format between tap and target"],"input_types":["STATE message from previous run (JSON object with tap-specific structure)","Source system API or database supporting incremental queries"],"output_types":["STATE message containing extraction checkpoint (JSON object)","RECORD messages for new/changed rows only"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_10","uri":"capability://automation.workflow.configuration.management.via.json.config.files.and.environment.variables","name":"configuration management via json config files and environment variables","description":"Singer taps and targets are configured via JSON config files (passed via `--config` flag) containing source/destination credentials, extraction parameters (e.g., table names, filters), and loading parameters (e.g., schema, batch size). Config files are tap/target-specific — there's no standardized schema. Credentials can also be passed via environment variables, allowing secure credential management without embedding secrets in config files. Orchestration tools (Airflow, Meltano) typically manage config file generation and environment variable injection. Config files are human-readable JSON, enabling version control and templating. No built-in encryption or secret management — credentials are stored as plaintext in config files or environment variables.","intents":["I want to manage Singer credentials securely without embedding them in config files","I need to template config files for different environments (dev, staging, prod)","I want to version control Singer pipeline configurations"],"best_for":["Teams using environment-based configuration (dev, staging, prod)","Organizations with existing secret management infrastructure (e.g., HashiCorp Vault, AWS Secrets Manager)","Pipelines requiring different configurations per environment"],"limitations":["No standardized config schema — each tap/target defines its own config format, making it difficult to template or validate","No built-in encryption or secret management — credentials are plaintext in config files or environment variables","Config validation is tap/target-specific — no framework-level validation","No config inheritance or composition — each pipeline requires a separate config file","Environment variable substitution is not built-in — orchestration tools must implement it"],"requires":["JSON config file with source/destination credentials","Environment variables for sensitive credentials (optional but recommended)","Understanding of tap/target-specific config schema"],"input_types":["JSON config file (tap/target-specific schema)","Environment variables (for credential injection)"],"output_types":["Parsed configuration (used by tap/target at runtime)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_2","uri":"capability://tool.use.integration.language.agnostic.tap.target.implementation.via.cli.protocol","name":"language-agnostic tap/target implementation via cli protocol","description":"Singer taps and targets are standalone CLI executables that read/write JSON messages via stdin/stdout, enabling implementation in any programming language (Python, Node.js, Go, Rust, etc.). The framework does not mandate a language-specific SDK or runtime — only that the executable implements the Singer protocol specification. This is enforced by the Unix pipe model: a tap is invoked as `tap-name [args]` and outputs JSON to stdout; a target is invoked as `target-name [args]` and reads JSON from stdin. Community taps/targets are typically distributed as pip packages (Python) but can be any compiled binary or script.","intents":["I want to write a custom tap in Go for performance-critical extraction from a proprietary database","I need to integrate a legacy ETL tool written in Java into Singer without rewriting it","I want to contribute a tap in my preferred language (Rust, Node.js) to the Singer ecosystem"],"best_for":["Polyglot teams with expertise in multiple languages","Organizations with existing CLI tools that can be wrapped as Singer taps/targets","Performance-sensitive use cases where language choice matters (e.g., Go for concurrent extraction)"],"limitations":["No framework-provided scaffolding or SDK — developers must implement protocol parsing/serialization from scratch or use community libraries (availability varies by language)","Debugging cross-language pipelines is harder — errors in a Go tap piped to a Python target require understanding both runtimes","No type safety across language boundaries — JSON schema validation is runtime-only, not compile-time","Dependency management is per-language — a Python tap and Node.js target have separate dependency trees","Performance varies significantly by language choice; no framework optimization across language boundaries"],"requires":["CLI executable (compiled binary or script) that reads/writes JSON","Implementation of Singer protocol specification (SCHEMA, RECORD, STATE, ACTIVATE_VERSION message parsing)","Ability to invoke executable from shell with arguments (e.g., `tap-name --config config.json`)","JSON serialization library in chosen language"],"input_types":["Command-line arguments (config file path, state file path, catalog file path)","stdin stream (for targets receiving RECORD messages)"],"output_types":["stdout stream (JSON messages: SCHEMA, RECORD, STATE, ACTIVATE_VERSION)","Exit code (0 for success, non-zero for failure)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_3","uri":"capability://tool.use.integration.community.maintained.connector.ecosystem.200.taps.and.targets","name":"community-maintained connector ecosystem (200+ taps and targets)","description":"Singer provides a curated directory of 200+ open-source, community-maintained data connectors (taps for extraction, targets for loading) covering SaaS APIs (Salesforce, HubSpot, Stripe, Shopify, Zendesk, Jira, GitHub), databases (MySQL, PostgreSQL, Oracle, DynamoDB), analytics platforms (Google Analytics, Mixpanel, Amplitude), and file sources (S3, SFTP, Google Sheets). These connectors are distributed as pip-installable Python packages and implement the Singer protocol, allowing users to compose pipelines without writing custom code. The ecosystem is maintained by the Singer community and Meltano (a Singer-based orchestration platform), with varying levels of maintenance (some actively updated, others community-supported).","intents":["I want to extract from Salesforce and load to Snowflake without writing any code","I need to find if a Singer connector exists for my data source before building a custom one","I want to use a maintained, battle-tested tap rather than building my own"],"best_for":["Teams with common data sources (SaaS, standard databases) where pre-built connectors exist","Organizations wanting to avoid custom connector development and maintenance overhead","Users preferring community-vetted, actively-maintained connectors over proprietary solutions"],"limitations":["Connector quality and maintenance varies — some are actively updated, others are community-supported with slow response times","Not all data sources have Singer connectors — niche or proprietary systems may require custom tap development","Connector feature coverage may be incomplete — a tap might not support all source API endpoints or authentication methods","Authentication patterns differ across taps — no standardized credential management (each tap defines its own config schema)","Connector versioning is independent — upgrading a tap may introduce breaking changes in STATE message format or output schema"],"requires":["Python 3.6+ (most taps/targets are Python packages)","pip package manager","Source system API credentials (e.g., Salesforce OAuth token, Stripe API key)","Destination system credentials (e.g., PostgreSQL connection string, Snowflake warehouse URL)"],"input_types":["Connector name and version (e.g., tap-salesforce==1.2.3)","Configuration JSON (source credentials, extraction parameters)"],"output_types":["Installed CLI executable (tap or target)","Singer protocol messages (SCHEMA, RECORD, STATE)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_4","uri":"capability://automation.workflow.composable.tap.to.target.pipelines.via.unix.pipes","name":"composable tap-to-target pipelines via unix pipes","description":"Singer pipelines are constructed by piping a tap executable's stdout directly into a target executable's stdin using standard Unix shell pipes (e.g., `tap-salesforce | target-postgres`). The tap streams SCHEMA, RECORD, and STATE messages as JSON lines to stdout; the target reads these messages from stdin and loads data into the destination. This composition model requires no orchestration framework, configuration files, or intermediate storage — the pipe itself is the data transport. Multiple taps can be composed into a single target using shell redirection, and targets can be chained (though this is less common). The simplicity enables ad-hoc pipelines via command line or integration into shell scripts, Makefiles, or orchestration tools (Airflow, Meltano, etc.).","intents":["I want to run a quick one-off extraction from an API to a CSV file without setting up infrastructure","I need to compose multiple taps into a single target for data consolidation","I want to integrate Singer into my existing shell scripts or Makefile-based workflows"],"best_for":["Ad-hoc data extraction tasks and one-off migrations","Teams comfortable with Unix shell and command-line tools","Lightweight pipelines that don't require complex orchestration or monitoring","Integration into existing shell-based automation (Makefiles, cron jobs, bash scripts)"],"limitations":["No built-in error handling or retry logic — if tap or target fails, the entire pipe fails; recovery requires manual intervention","Difficult to monitor and debug — errors are printed to stderr but not structured; no built-in logging aggregation","No parallelization — taps run sequentially; extracting from multiple sources requires multiple pipe invocations","Pipe buffering can cause memory issues with large datasets — data is buffered in OS pipe buffers (typically 64KB), not streamed efficiently","No built-in scheduling or orchestration — requires external tools (cron, Airflow, Meltano) for recurring syncs","State management is manual — targets must persist STATE messages; no framework-managed state store"],"requires":["Unix-like shell (bash, zsh, sh) supporting pipes and process substitution","Tap and target CLI executables installed and in PATH","Source and destination system credentials (passed via config files or environment variables)"],"input_types":["Tap executable name and arguments (e.g., tap-salesforce --config config.json)","Target executable name and arguments (e.g., target-postgres --config config.json)"],"output_types":["Data loaded into target system (e.g., rows inserted into PostgreSQL table)","STATE messages persisted by target (for resumable syncs)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_5","uri":"capability://data.processing.analysis.schema.definition.and.validation.via.json.schema","name":"schema definition and validation via json schema","description":"Singer taps emit SCHEMA messages containing a JSON Schema definition of the table structure (column names, data types, constraints) before emitting RECORD messages. Targets use this schema to validate incoming records, infer destination table structure, and handle type mapping (e.g., JSON Schema 'string' → PostgreSQL 'text'). The schema is embedded in the data stream, not stored separately, allowing targets to dynamically create tables or validate records without external schema artifacts. JSON Schema supports nested objects and arrays, enabling representation of complex data types. Targets can enforce strict schema validation (reject records with unexpected fields) or lenient validation (ignore extra fields), depending on implementation.","intents":["I want to automatically create tables in my data warehouse based on the source schema","I need to validate that extracted records conform to the expected structure before loading","I want to handle schema changes (new columns, type changes) gracefully during incremental syncs"],"best_for":["Automated data warehouse setup where schema is inferred from source","Pipelines requiring schema validation to catch data quality issues early","Scenarios where source schema evolves and targets must adapt"],"limitations":["JSON Schema is verbose and not all data types map cleanly to destination systems (e.g., JSON Schema 'number' is ambiguous: int, float, decimal?)","No standardized type mapping between JSON Schema and destination databases — each target implements its own mapping","Schema evolution (adding/removing columns) is not explicitly handled — targets must decide whether to fail, ignore, or auto-migrate","Nested objects and arrays are supported in JSON Schema but not all targets can represent them (e.g., flat SQL tables)","No schema versioning — if source schema changes, targets must detect and handle the change"],"requires":["Tap implementation that emits valid JSON Schema in SCHEMA messages","Target implementation that parses JSON Schema and maps to destination types","Understanding of JSON Schema specification (draft-07 or later)"],"input_types":["SCHEMA message containing JSON Schema definition (JSON object)","RECORD messages conforming to schema"],"output_types":["Destination table structure (inferred from schema)","Validation errors (if records violate schema)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_6","uri":"capability://code.generation.editing.custom.tap.development.with.authentication.and.pagination.handling","name":"custom tap development with authentication and pagination handling","description":"Developers can build custom Singer taps by implementing the Singer protocol specification: reading a config file (JSON with source credentials), emitting SCHEMA messages for each table, emitting RECORD messages for each row, and emitting STATE messages for incremental checkpoints. Taps must handle source-specific concerns: authentication (OAuth, API keys, database credentials), pagination (cursor-based, offset-based, keyset pagination), rate limiting, and error handling. Singer provides no framework scaffolding — developers implement these concerns directly in their tap code. Community libraries (e.g., singer-python for Python) provide utilities for JSON serialization and common patterns, but are optional. Taps are typically distributed as pip packages with a CLI entry point that accepts `--config`, `--state`, and `--catalog` arguments.","intents":["I need to extract data from a proprietary system not covered by existing Singer taps","I want to build a reusable tap for my company's internal API","I need to implement custom incremental logic for a source that doesn't support standard filtering"],"best_for":["Teams with proprietary or niche data sources requiring custom extraction logic","Organizations wanting to build internal data connectors as reusable packages","Developers comfortable implementing authentication, pagination, and error handling"],"limitations":["No framework scaffolding — developers must implement protocol parsing, JSON serialization, and error handling from scratch","Authentication patterns are tap-specific — no standardized credential management across taps","Pagination strategies vary by API — developers must understand source API pagination model and implement accordingly","Rate limiting is not handled by framework — taps must implement backoff and retry logic","Testing is developer-responsibility — no built-in testing framework or fixtures","Documentation is sparse — developers must read Singer protocol spec and reference taps to understand patterns","Incremental extraction requires understanding source API capabilities (timestamp filters, cursors, etc.)"],"requires":["Programming language with JSON serialization library (Python, Node.js, Go, etc.)","Understanding of Singer protocol specification (SCHEMA, RECORD, STATE, ACTIVATE_VERSION messages)","Source system API documentation and credentials","Knowledge of source API authentication method (OAuth, API key, basic auth, etc.)","Understanding of source API pagination model"],"input_types":["Config file (JSON with source credentials and extraction parameters)","State file (JSON with previous extraction checkpoint, optional)","Catalog file (JSON with table definitions and field selections, optional)"],"output_types":["SCHEMA messages (JSON Schema for each table)","RECORD messages (data rows as JSON objects)","STATE messages (extraction checkpoint for resumability)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_7","uri":"capability://code.generation.editing.custom.target.development.with.data.type.mapping.and.idempotency.handling","name":"custom target development with data type mapping and idempotency handling","description":"Developers can build custom Singer targets by implementing the Singer protocol specification: reading SCHEMA messages to infer destination table structure, reading RECORD messages to load data, and persisting STATE messages for resumable syncs. Targets must handle destination-specific concerns: data type mapping (JSON Schema types → destination database types), table creation/alteration, duplicate handling (idempotency), and error recovery. Singer provides no framework scaffolding — developers implement these concerns directly. Targets typically implement upsert logic (insert or update based on primary key) to handle duplicate records from resumed extractions. Targets are distributed as pip packages with a CLI entry point accepting `--config` argument.","intents":["I need to load data into a proprietary data warehouse not covered by existing Singer targets","I want to build a custom target that transforms data during load (e.g., denormalization, aggregation)","I need to implement idempotent loading to handle duplicate records from failed extractions"],"best_for":["Teams with proprietary or niche data destinations requiring custom loading logic","Organizations needing custom data transformations during load","Scenarios requiring idempotent loading to handle extraction failures"],"limitations":["No framework scaffolding — developers must implement protocol parsing, type mapping, and error handling from scratch","Data type mapping is destination-specific — no standardized mapping from JSON Schema to all database types","Idempotency requires understanding destination capabilities (primary keys, upsert support, etc.)","Schema evolution (adding/removing columns) is not handled by framework — targets must decide how to handle","Testing is developer-responsibility — no built-in testing framework or fixtures","STATE persistence is target-responsibility — targets must durably store STATE messages (e.g., in database, file system)","No built-in monitoring or alerting — targets must implement their own logging and error reporting"],"requires":["Programming language with JSON serialization library","Understanding of Singer protocol specification (SCHEMA, RECORD, STATE messages)","Destination system API or database driver","Knowledge of destination data types and type mapping from JSON Schema","Understanding of destination idempotency mechanisms (primary keys, upsert, etc.)"],"input_types":["Config file (JSON with destination credentials and loading parameters)","stdin stream of SCHEMA, RECORD, and STATE messages"],"output_types":["Data loaded into destination system (rows inserted/updated)","STATE messages persisted to durable storage (for resumable syncs)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_8","uri":"capability://data.processing.analysis.batch.and.incremental.sync.modes.with.full.refresh.capability","name":"batch and incremental sync modes with full refresh capability","description":"Singer taps support two primary sync modes: full refresh (re-extract all data from source, ignoring previous STATE) and incremental (extract only new/changed data since last STATE checkpoint). Targets can be configured to replace existing data (full refresh mode) or merge with existing data (incremental mode). Full refresh is useful for initial data loads or when source data is small; incremental is essential for large sources where repeated full extracts are prohibitively expensive. Taps implement incremental logic by filtering source queries (e.g., WHERE modified_at > last_sync_time) or using pagination cursors; targets implement merge logic by upserting records based on primary keys. The choice between modes is typically made at pipeline invocation time (e.g., via command-line flags or orchestration tool configuration).","intents":["I want to do an initial full load of all historical data, then incremental syncs for new records","I need to re-sync all data from a source to fix data quality issues","I want to switch between full refresh and incremental modes based on pipeline requirements"],"best_for":["Pipelines with large data volumes where incremental syncs are mandatory for cost/performance","Initial data loads followed by ongoing incremental syncs","Scenarios requiring periodic full refreshes to correct data quality issues"],"limitations":["Full refresh can be very expensive for large sources — may take hours or days and consume significant API quota","Incremental mode requires source support for filtering (timestamp, cursor, etc.) — not all APIs support efficient incremental queries","Switching between modes requires careful state management — mixing full refresh and incremental can cause data duplication or loss","Soft deletes are not handled by incremental mode — if a record is deleted in source, incremental sync won't detect it","No built-in mechanism to detect when full refresh is needed (e.g., source schema change, data corruption)"],"requires":["Tap implementation supporting both full refresh and incremental modes","Target implementation supporting both replace (full refresh) and merge (incremental) logic","Source system supporting filtering for incremental extraction (optional, but recommended)"],"input_types":["Sync mode flag (full-refresh or incremental)","STATE message from previous run (for incremental mode)"],"output_types":["RECORD messages (all records for full refresh, new/changed records for incremental)","STATE message (checkpoint for resumability)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__cap_9","uri":"capability://automation.workflow.integration.with.orchestration.and.scheduling.tools.airflow.meltano.cron","name":"integration with orchestration and scheduling tools (airflow, meltano, cron)","description":"Singer pipelines can be invoked from external orchestration tools (Apache Airflow, Meltano, cron jobs, shell scripts) because taps and targets are CLI executables. Orchestration tools can invoke Singer pipelines via subprocess calls, passing configuration files and managing scheduling, error handling, and monitoring. Meltano (a Singer-based orchestration platform) provides native Singer integration with YAML-based pipeline definitions, environment variable management, and built-in tap/target discovery. Airflow can invoke Singer via BashOperator or custom operators. Cron jobs can invoke Singer pipelines via shell scripts. This flexibility allows Singer to integrate into existing data infrastructure without requiring a centralized Singer orchestration platform.","intents":["I want to schedule Singer pipelines to run daily using Airflow","I need to integrate Singer into my existing cron-based data pipeline","I want to use Meltano for Singer orchestration and environment management"],"best_for":["Teams already using Airflow or other orchestration tools","Organizations wanting to integrate Singer into existing data infrastructure","Users preferring lightweight scheduling (cron) over centralized orchestration"],"limitations":["No built-in orchestration — Singer is protocol-only; scheduling requires external tools","Error handling and retry logic must be implemented by orchestration tool, not Singer","Monitoring and alerting are orchestration-tool-dependent — no built-in Singer observability","State management is orchestration-tool-dependent — orchestration tool must persist STATE messages","Parallelization (running multiple taps concurrently) requires orchestration tool support","Meltano adds complexity if you prefer lightweight tools — it's a full orchestration platform, not just a Singer wrapper"],"requires":["Orchestration tool (Airflow, Meltano, cron, etc.)","Ability to invoke CLI executables from orchestration tool","Configuration file management (passing config files to taps/targets)"],"input_types":["Orchestration tool configuration (DAG definition, cron schedule, etc.)","Singer tap/target CLI invocation"],"output_types":["Scheduled pipeline execution","Data loaded into destination system"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"singer__headline","uri":"capability://data.processing.analysis.open.source.data.extraction.and.loading.framework","name":"open-source data extraction and loading framework","description":"Singer is an open-source standard for writing data extraction and loading scripts, enabling seamless data movement between various sources and destinations using a JSON-based specification.","intents":["best data extraction framework","data loading solution for ETL processes","open-source data pipeline tools","data integration framework for developers","data connectors for various sources"],"best_for":["developers looking for a standardized data pipeline solution"],"limitations":[],"requires":["Python for installation"],"input_types":["data from APIs, databases, files"],"output_types":["loaded data into specified destinations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Tap implementation (Python package or CLI in any language)","Target implementation (Python package or CLI in any language)","Unix-like shell environment supporting pipes (bash, zsh, sh)","Understanding of JSON Schema for SCHEMA message validation","Tap implementation supporting incremental extraction (not all 200+ taps support this)","Target implementation that persists STATE messages to durable storage","Source system supporting filtering by timestamp, cursor, or pagination token","Agreement on STATE message format between tap and target","JSON config file with source/destination credentials","Environment variables for sensitive credentials (optional but recommended)"],"failure_modes":["Protocol does not specify error handling semantics — error propagation between tap and target is implementation-dependent","No built-in schema evolution handling — breaking schema changes may require manual intervention","JSON serialization overhead adds latency per message; not optimized for sub-millisecond latency requirements","Protocol assumes stateless tap/target execution — complex stateful transformations must occur outside Singer","STATE message format is tap-specific — no standardized schema, making state inspection/debugging difficult","Requires tap to implement incremental logic; not all sources support efficient incremental extraction (e.g., some APIs lack timestamp filters)","Soft deletes and record updates may not be captured if tap only tracks insertion time, not modification time","State storage is target-responsibility — if target fails to persist STATE, resumability is lost","No built-in conflict resolution if STATE is corrupted or manually edited","No standardized config schema — each tap/target defines its own config format, making it difficult to template or validate","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:25.061Z","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=singer","compare_url":"https://unfragile.ai/compare?artifact=singer"}},"signature":"XNT4kLxZf2d7nKltsstXzHt8APg8rJ3teIgvQLD6ZIqPIkKChahzfMZugRrh7vA2fqVZ3f6BriMifwhuTur6BA==","signedAt":"2026-06-22T03:44:56.601Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/singer","artifact":"https://unfragile.ai/singer","verify":"https://unfragile.ai/api/v1/verify?slug=singer","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"}}