Agent skill

receive

Ingest and parse incoming messages, events, or signals into structured form. Use when processing external inputs, handling API responses, parsing webhook payloads, or ingesting sensor data.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/receive

SKILL.md

Intent

Execute receive to ingest incoming data from external sources and parse it into a structured, validated form for downstream processing.

Success criteria:

  • Input data is successfully ingested from the source
  • Data is parsed according to expected format
  • Validation against schema (if provided) passes or errors are captured
  • Structured output is ready for downstream capabilities
  • Malformed or unexpected data is handled gracefully

Compatible schemas:

  • schemas/output_schema.yaml
  • reference/event_schema.yaml

Inputs

Parameter Required Type Description
input_source Yes string|object Source of incoming data: file path, URL, inline data, or stream identifier
expected_format No enum Expected data format: json, xml, yaml, text, csv, binary. Default: auto-detect
validation_schema No string Schema to validate parsed data against
encoding No string Character encoding. Default: utf-8
max_size No string Maximum input size to accept (e.g., "10MB"). Default: "100MB"

Procedure

  1. Identify input source: Determine where data is coming from

    • File path: Read from local filesystem
    • URL: Fetch from network endpoint
    • Inline: Extract from request payload
    • Stream: Connect to data stream

  2. Validate source accessibility: Verify source can be read

    • Check file exists and is readable
    • Verify URL is reachable
    • Confirm stream is connected
    • Check size does not exceed max_size

  3. Detect format: Identify data format if not specified

    • Check content-type headers or file extension
    • Inspect data prefix for format signatures
    • Fall back to text if ambiguous

  4. Parse input: Transform raw data into structured form

    • JSON: Parse to object/array using safe parser
    • XML: Parse to DOM or object representation
    • YAML: Parse to object using safe loader
    • CSV: Parse to array of records
    • Text: Split into lines/tokens as appropriate
    • Binary: Extract structured fields per schema

  5. Validate structure: Check parsed data against schema

    • If validation_schema provided: validate and collect errors
    • If no schema: perform basic sanity checks
    • Flag missing required fields
    • Flag type mismatches

  6. Extract events/messages: Identify discrete units in the data

    • Single message: wrap as single-element array
    • Batch: extract individual events
    • Stream chunk: identify complete messages

  7. Ground evidence: Record provenance for ingested data

    • Source URI or path
    • Timestamp of receipt
    • Size and checksum if applicable

  8. Format output: Structure results according to output contract

Output Contract

Return a structured object:

yaml
received:
  source: string  # Where data came from
  source_type: file | url | inline | stream
  timestamp: string  # ISO timestamp of receipt
  format_detected: json | xml | yaml | csv | text | binary
  size_bytes: integer  # Size of received data
  encoding: string  # Character encoding used
messages:
  - id: string  # Unique message identifier
    type: string | null  # Event/message type if identifiable
    payload: object  # Parsed message content
    timestamp: string | null  # Message timestamp if present
    metadata: object | null  # Additional message metadata
parsed_count: integer  # Number of messages/events parsed
validation:
  schema_ref: string | null  # Schema used for validation
  valid: boolean  # Whether all messages passed validation
  errors:
    - message_id: string
      field: string
      error: string
      severity: error | warning
conflicts:
  - type: string  # Type of conflict/anomaly
    description: string
    affected_messages: array[string]
confidence: number  # 0.0-1.0 based on parse success and validation
evidence_anchors: array[string]  # Source references
assumptions: array[string]  # Explicit assumptions

Field Definitions

Field Type Description
received.source string Origin of the data
messages array[object] Parsed messages/events
validation.valid boolean Whether validation passed
validation.errors array Validation error details
conflicts array Anomalies or conflicts in data
confidence number Lower if parse errors or validation failures

Examples

Example 1: Receiving JSON API Response

Input:

yaml
input_source: "https://api.example.com/events"
expected_format: json
validation_schema: "schemas/event_v2.yaml"

Output:

yaml
received:
  source: "https://api.example.com/events"
  source_type: url
  timestamp: "2024-01-16T12:00:00Z"
  format_detected: json
  size_bytes: 4256
  encoding: "utf-8"
messages:
  - id: "evt_001"
    type: "order.created"
    payload:
      order_id: "ORD-12345"
      customer_id: "CUST-789"
      total: 129.99
      currency: "USD"
    timestamp: "2024-01-16T11:58:30Z"
    metadata:
      source_system: "ecommerce"
      version: "2.1"
  - id: "evt_002"
    type: "order.paid"
    payload:
      order_id: "ORD-12345"
      payment_method: "credit_card"
      transaction_id: "TXN-ABC123"
    timestamp: "2024-01-16T11:59:00Z"
    metadata:
      source_system: "payments"
      version: "1.0"
parsed_count: 2
validation:
  schema_ref: "schemas/event_v2.yaml"
  valid: true
  errors: []
conflicts: []
confidence: 1.0
evidence_anchors:
  - "url:https://api.example.com/events"
  - "schema:schemas/event_v2.yaml"
assumptions:
  - "API response is authoritative source of truth"
  - "Timestamps are in UTC"

Evidence pattern: Source URL recorded; schema validation confirmed; each message given unique ID.

Example 2: Receiving Malformed Data

Input:

yaml
input_source: "/var/log/sensor_data.json"
expected_format: json
validation_schema: "schemas/sensor_reading.yaml"

Output:

yaml
received:
  source: "/var/log/sensor_data.json"
  source_type: file
  timestamp: "2024-01-16T14:30:00Z"
  format_detected: json
  size_bytes: 1024
  encoding: "utf-8"
messages:
  - id: "msg_001"
    type: "sensor_reading"
    payload:
      sensor_id: "SENS-001"
      temperature: 23.5
      humidity: null
      timestamp: "2024-01-16T14:29:00Z"
    timestamp: "2024-01-16T14:29:00Z"
    metadata: null
  - id: "msg_002"
    type: "sensor_reading"
    payload:
      sensor_id: "SENS-002"
      temperature: "ERROR"
      humidity: 65.2
      timestamp: null
    timestamp: null
    metadata: null
parsed_count: 2
validation:
  schema_ref: "schemas/sensor_reading.yaml"
  valid: false
  errors:
    - message_id: "msg_001"
      field: "humidity"
      error: "Required field is null"
      severity: warning
    - message_id: "msg_002"
      field: "temperature"
      error: "Expected number, got string 'ERROR'"
      severity: error
    - message_id: "msg_002"
      field: "timestamp"
      error: "Required field is null"
      severity: error
conflicts:
  - type: "data_quality"
    description: "Sensor SENS-002 reporting error state instead of reading"
    affected_messages: ["msg_002"]
confidence: 0.5
evidence_anchors:
  - "file:/var/log/sensor_data.json"
  - "schema:schemas/sensor_reading.yaml"
assumptions:
  - "File contains most recent sensor batch"
  - "'ERROR' string indicates sensor malfunction"
next_actions:
  - "Investigate sensor SENS-002 for malfunction"
  - "Check why SENS-001 humidity is null"

Example 3: Receiving Webhook Payload

Input:

yaml
input_source:
  inline: |
    <?xml version="1.0"?>
    <notification type="alert">
      <severity>high</severity>
      <message>CPU usage exceeded threshold</message>
      <host>server-01</host>
    </notification>
expected_format: xml

Output:

yaml
received:
  source: "inline"
  source_type: inline
  timestamp: "2024-01-16T15:00:00Z"
  format_detected: xml
  size_bytes: 198
  encoding: "utf-8"
messages:
  - id: "msg_001"
    type: "notification.alert"
    payload:
      severity: "high"
      message: "CPU usage exceeded threshold"
      host: "server-01"
    timestamp: null
    metadata:
      xml_root: "notification"
      xml_attributes:
        type: "alert"
parsed_count: 1
validation:
  schema_ref: null
  valid: true
  errors: []
conflicts: []
confidence: 0.95
evidence_anchors:
  - "inline:xml:notification"
assumptions:
  - "XML is well-formed and complete"
  - "No schema validation requested"

Verification

  • Source is accessible and data retrieved successfully
  • Format detection matches actual data format
  • All messages parsed without critical errors
  • Validation errors logged with actionable detail
  • confidence reflects parse success rate

Verification tools: Read (to access file sources), Grep (to search for patterns in text data)

Safety Constraints

  • mutation: false
  • requires_checkpoint: false
  • requires_approval: false
  • risk: low

Capability-specific rules:

  • Do not execute or run arbitrary code from received data
  • Use safe parsing methods (JSON.parse, safe YAML loader)
  • Validate max_size before loading large files into memory
  • Sanitize file paths to prevent directory traversal
  • Do not store credentials or secrets found in payloads
  • Log but do not propagate malformed/suspicious data without flagging

Composition Patterns

Commonly follows:

  • External triggers (webhooks, API calls, file drops)
  • schedule - Scheduled data ingestion

Commonly precedes:

  • transform - Normalize received data for processing
  • validate - Deeper validation beyond schema
  • integrate - Merge received data with existing state
  • synchronize - Combine with other data sources
  • recall - Check received data against prior context

Anti-patterns:

  • Never process received data without validation
  • Never trust received data for authentication decisions without verification
  • Avoid processing unbounded streams without size limits

Workflow references:

  • See reference/composition_patterns.md#digital-twin-sync-loop for receive as entry point
  • First step in most data ingestion pipelines

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