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Circleci Automation

CommunityPopular
davepoon
circleci-automation

Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for current schemas.

Overview

Publisherdavepoon
Repositorybuildwithclaude
Skill namecircleci-automation
Stars
3.5K
Forks
509
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by davepoon on GitHub. Read the source before you install it.

Installation

Install the Circleci Automation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/davepoon/buildwithclaude.git /tmp/buildwithclaude
mkdir -p .claude/skills
cp -r /tmp/buildwithclaude/plugins/all-skills/skills/circleci-automation .claude/skills/circleci-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Circleci Automation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Circleci Automation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Circleci Automation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CircleCI Automation via Rube MCP

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

Toolkit docs: composio.dev/toolkits/circleci

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

TaskTool SlugKey Params
Trigger pipelineCIRCLECI_TRIGGER_PIPELINEproject_slug, branch, parameters
List pipelinesCIRCLECI_LIST_PIPELINES_FOR_PROJECTproject_slug, branch
List workflowsCIRCLECI_LIST_WORKFLOWS_BY_PIPELINE_IDpipeline_id
Get pipeline configCIRCLECI_GET_PIPELINE_CONFIGpipeline_id
Get job detailsCIRCLECI_GET_JOB_DETAILSproject_slug, job_number
Get job artifactsCIRCLECI_GET_JOB_ARTIFACTSproject_slug, job_number
Get test metadataCIRCLECI_GET_TEST_METADATAproject_slug, job_number

Powered by Composio

Frequently asked questions

What does the Circleci Automation AI skill do?

Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for current schemas.

Why use Circleci Automation on TypingMind?

Because you install it once and use it with any model. Circleci Automation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Circleci Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davepoon/buildwithclaude/tree/main/plugins/all-skills/skills/circleci-automation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Circleci Automation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Circleci Automation?

As many as you like. As long as a model supports skills, you can use Circleci Automation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Circleci Automation AI skill free?

Yes. It is published on GitHub by davepoon under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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