Circleci Automation

CircleCI Automation via Rube MCP

Automate CircleCI CI/CD operations through Composio's CircleCI toolkit via Rube MCP.

Prerequisites

• Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
• Active CircleCI connection via RUBE_MANAGE_CONNECTIONS with toolkit circleci
• Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works.

1. Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
2. Call RUBE_MANAGE_CONNECTIONS with toolkit circleci
3. If connection is not ACTIVE, follow the returned auth link to complete CircleCI authentication
4. Confirm connection status shows ACTIVE before running any workflows

Core Workflows

1. Trigger a Pipeline

When to use: User wants to start a new CI/CD pipeline run

Tool sequence:

1. CIRCLECI_TRIGGER_PIPELINE - Trigger a new pipeline on a project [Required]
2. CIRCLECI_LIST_WORKFLOWS_BY_PIPELINE_ID - Monitor resulting workflows [Optional]

Key parameters:

• project_slug: Project identifier in format gh/org/repo or bb/org/repo
• branch: Git branch to run the pipeline on
• tag: Git tag to run the pipeline on (mutually exclusive with branch)
• parameters: Pipeline parameter key-value pairs

Pitfalls:

• project_slug format is {vcs}/{org}/{repo} (e.g., gh/myorg/myrepo)
• branch and tag are mutually exclusive; providing both causes an error
• Pipeline parameters must match those defined in .circleci/config.yml
• Triggering returns a pipeline ID; workflows start asynchronously

2. Monitor Pipelines and Workflows

When to use: User wants to check the status of pipelines or workflows

Tool sequence:

1. CIRCLECI_LIST_PIPELINES_FOR_PROJECT - List recent pipelines for a project [Required]
2. CIRCLECI_LIST_WORKFLOWS_BY_PIPELINE_ID - List workflows within a pipeline [Required]
3. CIRCLECI_GET_PIPELINE_CONFIG - View the pipeline configuration used [Optional]

Key parameters:

• project_slug: Project identifier in {vcs}/{org}/{repo} format
• pipeline_id: UUID of a specific pipeline
• branch: Filter pipelines by branch name
• page_token: Pagination cursor for next page of results

Pitfalls:

• Pipeline IDs are UUIDs, not numeric IDs
• Workflows inherit the pipeline ID; a single pipeline can have multiple workflows
• Workflow states include: success, running, not_run, failed, error, failing, on_hold, canceled, unauthorized
• page_token is returned in responses for pagination; continue until absent

3. Inspect Job Details

When to use: User wants to drill into a specific job's execution details

Tool sequence:

1. CIRCLECI_LIST_WORKFLOWS_BY_PIPELINE_ID - Find workflow containing the job [Prerequisite]
2. CIRCLECI_GET_JOB_DETAILS - Get detailed job information [Required]

Key parameters:

• project_slug: Project identifier
• job_number: Numeric job number (not UUID)

Pitfalls:

• Job numbers are integers, not UUIDs (unlike pipeline and workflow IDs)
• Job details include executor type, parallelism, start/stop times, and status
• Job statuses: success, running, not_run, failed, retried, timedout, infrastructure_fail, canceled

4. Retrieve Build Artifacts

When to use: User wants to download or list artifacts produced by a job

Tool sequence:

1. CIRCLECI_GET_JOB_DETAILS - Confirm job completed successfully [Prerequisite]
2. CIRCLECI_GET_JOB_ARTIFACTS - List all artifacts from the job [Required]

Key parameters:

• project_slug: Project identifier
• job_number: Numeric job number

Pitfalls:

• Artifacts are only available after job completion
• Each artifact has a path and url for download
• Artifact URLs may require authentication headers to download
• Large artifacts may have download size limits

5. Review Test Results

When to use: User wants to check test outcomes for a specific job

Tool sequence:

1. CIRCLECI_GET_JOB_DETAILS - Verify job ran tests [Prerequisite]
2. CIRCLECI_GET_TEST_METADATA - Retrieve test results and metadata [Required]

Key parameters:

• project_slug: Project identifier
• job_number: Numeric job number

Pitfalls:

• Test metadata requires the job to have uploaded test results (JUnit XML format)
• If no test results were uploaded, the response will be empty
• Test metadata includes classname, name, result, message, and run_time fields
• Failed tests include failure messages in the message field

Common Patterns

Project Slug Format

Format: {vcs_type}/{org_name}/{repo_name}
- GitHub:    gh/myorg/myrepo
- Bitbucket: bb/myorg/myrepo

Pipeline -> Workflow -> Job Hierarchy

1. Call CIRCLECI_LIST_PIPELINES_FOR_PROJECT to get pipeline IDs
2. Call CIRCLECI_LIST_WORKFLOWS_BY_PIPELINE_ID with pipeline_id
3. Extract job numbers from workflow details
4. Call CIRCLECI_GET_JOB_DETAILS with job_number

Pagination

• Check response for next_page_token field
• Pass token as page_token in next request
• Continue until next_page_token is absent or null

Known Pitfalls

ID Formats:

• Pipeline IDs: UUIDs (e.g., 5034460f-c7c4-4c43-9457-de07e2029e7b)
• Workflow IDs: UUIDs
• Job numbers: Integers (e.g., 123)
• Do NOT mix up UUIDs and integers between different endpoints

Project Slugs:

• Must include VCS prefix: gh/ for GitHub, bb/ for Bitbucket
• Organization and repo names are case-sensitive
• Incorrect slug format causes 404 errors

Rate Limits:

• CircleCI API has per-endpoint rate limits
• Implement exponential backoff on 429 responses
• Avoid rapid polling; use reasonable intervals (5-10 seconds)

Quick Reference

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Circleci Automation"

Storing Results

After completing work, store workflow/automation decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags circleci-automation workflow

Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>

Playbook Engine

Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.