iFixAi documentation
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).
🟢 New here → tutorials
- Get started: from a clean machine to a real, citable scorecard in four steps.
🔧 Trying to do something → how-to guides
- Test your own agent: wire in your real agent via
--provider http or a ChatProvider adapter, 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.
📖 Looking something up → reference
- CLI reference: every command and
ifixai run flag, plus judges and eval modes.
- Python API: the
ifixai.api surface.
- Scoring: the formula, grade bands, thresholds, and mandatory minimums.
- Inspections: the catalogue — name, what it tests, how we check, a pass/fail example, why it matters, and the pass bar — for all 60 open-source inspections, plus the pillar map.
- Fixture schema: the source-of-truth JSON Schema; see also the fixtures README.
💡 Wanting to understand why → explanation
- What we test, and how: the plain-language walkthrough, no code. Start here if you are evaluating the product rather than running it.
- Methodology: why the five pillars, why a cross-provider judge, what we actually measure, and how iFixAi compares to other eval frameworks.
See it in practice
- Case studies: scorecards for fixtures reconstructed from public accounts of four real incidents (Dragontail dispatch, Instagram account support, the OpenAI/Hugging Face containment breach, the AISI cyber-range incident). Not tests of any 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.