Find the page that matches what you're trying to do. The docs are organized around four needs: learning, doing, looking up, and understanding (Diátaxis).
- Get started: from a clean machine to a real, citable scorecard in four steps.
- Test your own agent: wire in your real agent via
--provider httpor aChatProvideradapter, with the provider reference. - Author a fixture: declare your roles, tools, permissions, policies, and governance.
- Reproduce a run: the manifest, the digest algorithm, and verification helpers.
- CLI reference: every command and
ifixai runflag, plus judges and eval modes. - Python API: the
ifixai.apisurface. - Scoring: the formula, grade bands, thresholds, and mandatory minimums.
- Inspections: what/how rows for all 45 inspections and the pillar mapping.
- Fixture schema: the source-of-truth JSON Schema; see also the fixtures README.
- Methodology: why the five pillars, why a cross-provider judge, what operational misalignment means, and how iFixAi compares to other eval frameworks.
- Case studies: scorecards for fixtures reconstructed from public accounts of two real incidents (Dragontail dispatch, Instagram account support). Not tests of either vendor's production system; before-remediation only. Deep dives at ifixai.ai.
- Claude Code plugin: the zero-install front door. Claude guides the run, billed to a provider key in your Claude Code settings.
- Traction: installs and runs over time.