pipeline-assistant

name	pipeline-assistant
description	This skill should be used when users need to create or fix Redpanda Connect pipeline configurations. Trigger when users mention "config", "pipeline", "YAML", "create a config", "fix my config", "validate my pipeline", or describe a streaming pipeline need like "read from Kafka and write to S3".

Redpanda Connect Configuration Assistant

Create working, validated Redpanda Connect configurations from scratch or repair existing configurations that have issues.

This skill REQUIRES skills: component-search, bloblang-authoring.

Objective

Deliver a complete, valid YAML configuration that passes validation and meets the user's requirements. Whether starting from a description or fixing a broken config, the result must be production-ready with properly secured credentials.

Handle Two Scenarios: Creation - User provides description like "Read from Kafka on localhost:9092 topic 'events' to stdout" Repair - User provides config file path and optional error context

This skill focuses ONLY on pipeline configuration orchestration and validation.

Skill Delegation:

NEVER directly use component-search or bloblang-authoring tools.

• Component Discovery - ALWAYS delegate to component-search skill when it is unclear which components to use OR when you need component configuration details
• Bloblang Development - ALWAYS delegate to bloblang-authoring skill when creating or fixing Bloblang transformations and NEVER write Bloblang yourself

Setup

This skill requires: rpk, rpk connect. See the SETUP for installation instructions.

Tools

Scaffold Pipeline

Generates YAML configuration template from component expression. Useful for quickly creating first pipeline draft.

# Usage:
rpk connect create [--small] <input>,...[/<processor>,...]/<output>,...

# Examples:
rpk connect create stdin/bloblang,awk/nats
rpk connect create file,http_server/protobuf/http_client  # Multiple inputs
rpk connect create kafka_franz/stdout  # Only input and output, no processors
rpk connect create --small stdin/bloblang/stdout  # Minimal config, omit advanced fields

• Requires component expression specifying desired inputs, processors, and outputs
• Expression format: inputs/processors/outputs separated by /
• Multiple components of same type separated by ,
• Outputs complete YAML configuration with specified components
• --small flag omits advanced fields

Online Component Documentation

Use the component-search skill's Online Component Documentation tool to look up detailed configuration information for any Redpanda Connect component containing usage examples, field descriptions, and best practices.

Lint Pipeline

Validates Redpanda Connect pipeline configurations.

# Usage:
rpk connect lint [--env-file <.env>] <pipeline.yaml>

# Examples:
rpk connect lint --env-file ./.env ./pipeline.yaml
rpk connect lint pipeline-without-secrets.yaml

• Requires pipeline configuration file path (e.g., pipeline.yaml)
• Optional --env-file flag provides .env file for environment variable substitution
• Validates YAML syntax, component configurations, and Bloblang expressions
• Outputs detailed error messages with specific location information
• Exit code 0 indicates success, non-zero indicates validation failures
• Can be run repeatedly during pipeline development and iteration

Run Pipeline

Executes Redpanda Connect pipeline to test end-to-end functionality.

# Usage:
rpk connect run [--log.level DEBUG] --env-file <.env> <pipeline.yaml>

# Examples:
rpk connect run pipeline-without-secrets.yaml
rpk connect run --env-file ./.env ./pipeline.yaml  # With secrets
rpk connect run --log.level DEBUG --env-file ./.env ./pipeline.yaml  # With debug logging

• Requires pipeline configuration file path (e.g., pipeline.yaml)
• Optional --env-file flag provides dotenv file for environment variable substitution
• Optional --log.level DEBUG enables detailed logging for troubleshooting connection and processing issues
• Starts pipeline and maintains active connections to inputs and outputs
• Runs continuously until manually terminated with Ctrl+C (SIGINT)
• Can be run repeatedly during pipeline development and iteration

Test with Standard Input/Output

Test pipeline logic with stdin/stdout before connecting to real systems. Especially useful for validating routing logic, error handling, and transformations.

Example: Content-based routing

input:
  stdin: {}

pipeline:
  processors:
    - mapping: |
        root = this
        # Route based on message type
        if this.type == "error" {
          meta route = "dlq"
        } else if this.priority == "high" {
          meta route = "urgent"
        } else {
          meta route = "standard"
        }

output:
  switch:
    cases:
      - check: 'meta("route") == "dlq"'
        output:
          stdout: {}
        processors:
          - mapping: 'root = "DLQ: " + content().string()'

      - check: 'meta("route") == "urgent"'
        output:
          stdout: {}
        processors:
          - mapping: 'root = "URGENT: " + content().string()'

      - check: 'meta("route") == "standard"'
        output:
          stdout: {}
        processors:
          - mapping: 'root = "STANDARD: " + content().string()'

Test all routes:

echo '{"type":"error","msg":"failed"}' | rpk connect run test.yaml
# Output: DLQ: {"type":"error","msg":"failed"}

echo '{"priority":"high","msg":"urgent"}' | rpk connect run test.yaml
# Output: URGENT: {"priority":"high","msg":"urgent"}

echo '{"priority":"low","msg":"normal"}' | rpk connect run test.yaml
# Output: STANDARD: {"priority":"low","msg":"normal"}

Limitations:

• Stdin/stdout cannot test batching behavior realistically
• No connection, retry, or timeout logic validation
• Cannot test ordering guarantees or parallel processing
• Real integration testing still required before production deployment

YAML Configuration Structure

Top-level keys:

• input - Data source (required): kafka_franz, http_server, stdin, aws_s3, etc
• output - Data destination (required): kafka_franz, postgres, stdout, aws_s3, etc
• pipeline.processors - Transformations (optional, execute sequentially)
• cache_resources, rate_limit_resources - Reusable components (optional)

Environment variables (required for secrets):

# Basic reference
broker: "${KAFKA_BROKER}"

# With default value
broker: "${KAFKA_BROKER:localhost:9092}"

Field type conventions:

• Durations: "30s", "5m", "1h", "100ms"
• Sizes: "5MB", "1GB", "512KB"
• Booleans: true, false (no quotes)

Minimal example:

input:
  redpanda:
    seed_brokers: ["${KAFKA_BROKER}"]
    topics: ["${TOPIC}"]

pipeline:
  processors:
    - mapping:
        | # Bloblang transformation - use  bloblang-authoring skill to create
        root = this
        root.timestamp = now()

output:
  stdout: {}

Use Quick Pipeline Scaffolding for initial drafts.

Production Recipes/Patterns

The ./resources/recipes/ directory contains validated production patterns. Each recipe includes:

• Markdown documentation (.md) - Pattern explanation, configuration details, testing instructions, and variations
• Working YAML configuration (.yaml) - Complete, tested pipeline referenced in the markdown

Before writing pipelines:

1. Read component documentation - Use Online Component Documentation tool for detailed field info and examples
2. Read relevant recipes - When user describes a pattern matching a recipe (routing, DLQ, replication, etc.), read the markdown file first
3. Adapt, don't copy - Use recipes as reference for patterns and best practices, customize for user's specific requirements

Available Recipes

Error Handling

• dlq-basic.md - Dead letter queue for error handling

Routing

• content-based-router.md - Route messages by field values
• multicast.md - Fan-out to multiple destinations

Replication

• kafka-replication.md - Cross-cluster Kafka streaming
• cdc-replication.md - Database change data capture

Cloud Storage

• s3-sink-basic.md - S3 output with batching
• s3-sink-time-based.md - Time-partitioned S3 writes
• s3-polling.md - Poll S3 for new files

Stateful Processing

• stateful-counter.md - Stateful counting with cache
• window-aggregation.md - Time-window aggregations

Performance & Monitoring

• rate-limiting.md - Throughput control
• custom-metrics.md - Prometheus metrics

Workflow

Creating New Configurations

1. Understand requirements

   • Parse description for source, destination, transformations, and special needs (ordering, batching, etc.)
   • Ask clarifying questions for ambiguous aspects
   • Check ./resources/recipes/ for relevant patterns

2. Discover components

   • Use component-search skill if unclear which components to use
   • Read component documentation for configuration details

3. Build configuration

   • Generate scaffold with rpk connect create input/processor/output
   • Add all required fields from component schemas
   • For secrets: ask user for env var names → use ${VAR_NAME} → document in .env.example
   • Keep configuration minimal and simple

4. Add transformations (if needed)

   • Delegate to bloblang-authoring skill for tested scripts
   • Embed in pipeline.processors section

5. Validate and iterate

   • Run rpk connect lint
   • On errors: parse → fix → re-validate until clean
   • Iterate until validation passes

6. Test and iterate

   • Test with rpk connect run
     • Temporarily use stdin and stdout for easier testing
     • Run with rpk connect run
     • Fix any runtime issues
     • Test all edge cases
     • Iterate until tests pass
   • Test connection and authentication to real systems if possible

7. Deliver

   • Deliver final pipeline.yaml and .env.example
   • Explain component choices and configuration decisions
   • Create concise TESTING.md with only practical followup testing instructions:
     • How to set up environment
     • Command to run the pipeline
     • Sample curl/test commands with realistic data
     • How to verify results in the target system
     • ONLY include new/essential information, avoid verbose explanations
   • NEVER create README files
   • Show concise summary in chat response

Repairing Existing Configurations

1. Diagnose

   • Run rpk connect lint to identify errors
   • Review user-provided context about symptoms
   • Find root causes (typos, deprecations, type mismatches)

2. Explain issues

   • Translate validation errors to plain language
   • Explain why current configuration doesn't work
   • Identify root causes, not just symptoms

3. Fix minimally

   • Get user approval before modifying files
   • Preserve original structure, comments, and intent
   • Replace deprecated components if needed
   • Apply secret handling with environment variables

4. Verify

   • Re-validate after each change
   • Test modified Bloblang transformations
   • Confirm no regressions introduced

Security Requirements (Critical)

Never store credentials in plain text:

• All passwords, secrets, tokens, API keys MUST use ${ENV_VAR} syntax in YAML
• Never put actual credentials in YAML or conversation

Environment variable files:

• .env - Contains actual secret values, used at runtime with --env-file .env, NEVER commit to git
• .env.example - Documents required variables with placeholder values, safe to commit
• Always remind user to add .env to .gitignore

When encountering sensitive fields (from <secret_fields> in component schema):

1. Ask user for environment variable name (e.g., KAFKA_PASSWORD)
2. Write ${KAFKA_PASSWORD} in YAML configuration
3. Document in .env.example: KAFKA_PASSWORD=your_password_here
4. User creates actual .env with real value: KAFKA_PASSWORD=actual_secret_123