AI coding agents

quality remains deterministic when an AI coding agent invokes it. The agent runs the same local analyzers, configuration, and repository tasks as a human or CI job; checking does not call an AI service.

Read the documentation programmatically

The documentation site publishes AI-readable resources that stay synchronized with these pages during every build:

• /llms.txt is the compact documentation index.
• /llms-full.txt contains the complete documentation in one Markdown document.
• Every documentation page is also available as Markdown by replacing its trailing slash with .md, such as /commands.md.

/llm.txt is provided as a compatibility copy for tools that look for the singular filename. New integrations should prefer the standard /llms.txt.

Add repository instructions

Print a ready-to-paste AGENTS.md section:

quality instructions --format agents

Add the output to the consumer repository’s AGENTS.md. The command only prints Markdown: it never creates or modifies the instruction file.

Keeping the instructions in the consuming repository makes its quality policy visible to developers and compatible agents. Run the command again after a quality upgrade to review the current recommendation.

Use structured output

Use the compact Markdown format when an agent needs actionable context:

quality doctor --format agent
quality check --changed --format agent
quality check --format agent

The agent format groups diagnostics by file, separates code findings from environment and toolchain problems, and includes focused rerun commands. It is bounded to 50 diagnostics, 50 tool entries, and eight lines of otherwise unstructured failure output per adapter. Individual messages are also bounded and normalized to one line. Analyzer messages are explicitly identified as untrusted repository or tool output.

The format does not change which analyzers run, their exit status, the selected severity thresholds, or baseline behavior. Use JSON when an integration needs the complete versioned report instead of a compact agent context:

quality doctor --format json
quality check --format json
quality check --changed --format json

Use quality check --changed for iteration and the complete quality check before handoff. Use quality fix only when edits are intended, and inspect the resulting changes afterward.

Configure quality.yml safely

Generated configuration declares the published quality.yml JSON Schema. Editors that support the YAML language-server schema comment can validate keys and values while a human or agent edits the file.

Preview detected configuration without writing a file:

quality init --dry-run

Unknown keys are rejected by the CLI as well, so the schema improves authoring but never replaces runtime validation.

Why there is no MCP server

Coding agents with terminal access can already invoke the local quality CLI. An MCP wrapper would duplicate that interface without changing the checking path. MCP becomes useful only if quality later needs to expose remote data or operations, such as organization policies, historical reports, or cross-repository queries.