Workflows and reference

Chain Harness Skills into end-to-end workflows, and review skill file anatomy, MCP tools, and the harness-skills repository structure.

Skills chain together for multi-step workflows, and each skill file follows a consistent internal structure. This page covers common workflow sequences and the reference material for authoring or extending skills.

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What you will learn in this topic

By the end of this topic, you will be able to:

• Chain skills together for multi-step workflows.

• Understand skill anatomy to read or author a skill.

• Reference the MCP tools skills use to call the Harness platform.

• Navigate the repository layout for skills, references, and examples.

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Before you begin

Before you chain skills into a workflow, ensure you have the following:

• Configured skills: An AI coding assistant set up to load skill instructions. For more information, see Set up skills.

• Harness MCP Server: A configured MCP server for tool execution.

• Skill catalog familiarity: Knowledge of which skills cover the resources in your workflow. For more information, see Skill catalog.

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End-to-end workflows

Most real tasks span several skills rather than one. Resources that depend on other resources must be created in the correct order, so the sequences below invoke skills in an order that satisfies those dependencies.

New microservice setup

Start with the resources that nothing else depends on, then work up to the pipeline and trigger that reference them.

Use these skills in order:

1. /create-connector: Git, Docker registry, and Kubernetes cluster connectors.

2. /create-secret: Credentials for connector authentication.

3. /create-service: Service definition referencing connectors.

4. /create-environment: Target environment configuration.

5. /create-infrastructure: Infrastructure definition for the target cluster.

6. /create-pipeline: CI/CD pipeline referencing the service, environment, and infrastructure.

7. /create-trigger: Webhook or schedule trigger to automate the pipeline.

Debug a failed deployment

Start debugging the failed execution, and then widen the investigation to the shared templates and delegates the pipeline depends on.

Typical sequence:

1. /run-pipeline: Identify the latest execution or reproduce the issue.

2. /debug-pipeline: Classify the failure and inspect the root cause.

3. /template-usage: Check if shared templates propagated the issue.

4. /manage-delegates: Investigate delegate capacity or connectivity if relevant.

Codebase-aware pipeline generation

Some skills read your repository directly, so they infer context from your code in addition to the detail you supply in the prompt.

The /create-pipeline skill includes codebase analysis capabilities. It scans your project files to auto-detect:

• Language and runtime from source files (package.json maps to Node.js, go.mod maps to Go).

• Build tools from build configuration (Dockerfile, webpack.config.js, pom.xml).

• Test frameworks from test configuration (jest.config.*, pytest.ini).

• Deployment targets from manifests (Chart.yaml maps to Helm, task-definition.json maps to ECS).

This allows the skill to generate pipeline YAML tailored to your project without manual configuration.

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Skill anatomy

Each skill lives in skills/<skill-name>/SKILL.md and follows a consistent structure. Knowing this structure helps you read existing skills and author your own.

skills/create-pipeline/
├── SKILL.md              # Skill definition (required)
└── references/           # Supplementary docs (optional)
    ├── native-steps.md
    ├── v0-pipeline-schema.md
    └── codebase-analysis.md

The SKILL.md file contains:

• Frontmatter: Name, description, version, MCP server dependency, and license metadata.

• Instructions: Phase-based steps with MCP tool calls and parameters.

• Examples: Real invocation scenarios and worked examples.

• Performance notes: Validation checks, tradeoffs, and optimization guidance.

• Troubleshooting: Common errors and recovery steps.

Reference files in the references/ subdirectory provide supplementary knowledge, such as schema definitions, decision trees, and template libraries, that the AI loads on demand.

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MCP tools

Skills use the Harness MCP Server, which provides 11 generic tools dispatched by resource_type.

Tool	Purpose
harness_list	List resources.
harness_get	Get resource details.
harness_create	Create a resource.
harness_update	Update a resource.
harness_delete	Delete a resource.
harness_execute	Execute an action.
harness_search	Search across resources.
harness_describe	Get resource schema.
harness_schema	Fetch JSON Schema definitions.
harness_diagnose	Diagnose issues.
harness_status	Check system status.

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Project structure

The repository groups skills, shared references, and examples into a predictable layout.

harness-skills/
├── skills/
│   ├── create-pipeline/
│   │   ├── SKILL.md
│   │   └── references/
│   ├── create-template/
│   │   └── SKILL.md
│   ├── debug-pipeline/
│   │   └── SKILL.md
│   └── ...
├── references/              # Shared repo-level playbooks
├── templates/               # Shared repo-level output templates
├── examples/
│   ├── v0/                  # v0 pipeline examples
│   ├── v1/                  # v1 pipeline examples
│   ├── templates/
│   ├── triggers/
│   ├── services/
│   ├── environments/
│   ├── connectors/
│   └── ...
├── .cursor/rules/harness.mdc
├── .github/copilot-instructions.md
├── AGENTS.md
├── CLAUDE.md
└── CONTRIBUTING.md

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Related articles

• Harness Skills repository: Browse the full skill source code and contribute new skills.

• Harness MCP Server: Configure the MCP server that skills use to execute actions against Harness.

• Contributing guide: Learn the conventions for submitting a new skill to the repository.

• Harness Skills overview: Prerequisites and editor setup.