> ## Documentation Index
> Fetch the complete documentation index at: https://bintzgavin-apastra-14.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Apastra — PromptOps for AI Teams

> Ship AI prompts with the same discipline as code. Version them, test them, catch regressions — all locally, using the agent in your IDE.

Apastra is a **file-based PromptOps framework** that treats AI prompts as versioned software assets. Prompts, test cases, scoring rules, and quality baselines are all files in your repo — and your IDE agent is the harness that runs evaluations.

No cloud platform required. No CI needed to get started. Just files and your agent.

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Install skills and run your first evaluation in 5 minutes
  </Card>

  <Card title="Core Concepts" icon="book" href="/core-concepts">
    Understand prompt specs, datasets, evaluators, suites, and baselines
  </Card>

  <Card title="Skills Reference" icon="wand-magic-sparkles" href="/skills/overview">
    Explore all available apastra skills for your IDE agent
  </Card>

  <Card title="Writing Evals" icon="flask" href="/guides/writing-evals">
    Learn to write effective evaluations that catch real regressions
  </Card>
</CardGroup>

## How it works

Your IDE agent (Claude, Cursor, Amp, Codex, and many more) reads the protocol files and executes the evaluation workflow — no external runtime, no API keys to configure.

<Steps>
  <Step title="Install skills">
    Add apastra skills to your IDE agent with a single command:

    ```bash theme={null}
    npx skills add BintzGavin/apastra --all --full-depth -y
    ```
  </Step>

  <Step title="Scaffold your first prompt">
    Ask your agent to create a prompt spec, dataset, evaluator, and test suite:

    > "Use the apastra-scaffold skill to create a prompt spec, dataset, evaluator, and suite for summarizing text"
  </Step>

  <Step title="Run your first eval">
    Ask your agent to evaluate the prompt:

    > "Use the apastra-eval skill to run the summarize-smoke suite"

    Your agent reads the suite, runs each test case through the model, scores results, and reports pass/fail.
  </Step>

  <Step title="Set a baseline">
    Lock in your current quality level so future changes are automatically compared:

    > "Use the apastra-baseline skill to set the current results as the baseline"
  </Step>
</Steps>

## What you get

<CardGroup cols={2}>
  <Card title="Prompt versioning" icon="code-branch">
    Prompt specs are YAML files with stable IDs, variable schemas, and output contracts — versioned in Git like any other code
  </Card>

  <Card title="Automated evals" icon="circle-check">
    Your IDE agent runs test suites, scores outputs, and reports pass/fail — no external platform needed
  </Card>

  <Card title="Regression detection" icon="shield-halved">
    Compare new results against known-good baselines to catch quality drops before they ship
  </Card>

  <Card title="Schema validation" icon="file-check">
    56 JSON schemas ensure all your promptops files are correctly formatted and machine-readable
  </Card>

  <Card title="Rich assertions" icon="list-check">
    Deterministic, AI-graded, and performance assertion types — from `contains` to `llm-rubric`
  </Card>

  <Card title="CI integration" icon="arrows-rotate">
    Optional GitHub Actions workflows for regression gating, promotion, and immutable releases
  </Card>
</CardGroup>

<Note>
  Apastra is local-first by design. You can use it entirely without CI or cloud infrastructure. When you're ready, the `apastra-setup-ci` skill upgrades your workflow to GitHub Actions in minutes.
</Note>