docs: add concise evaluation section with CLI workflows

README.md

@@ -84,6 +84,53 @@ Full benchmark execution code and reproducibility artifacts are available in the
 
 Evaluation note: initial_state, expected_outcome, required_effects, oracle_spec, and fault-injection metadata are published for reproducibility and are not provided to evaluated agents as privileged runtime inputs.
 
+## Evaluation
+
+The executable UndoBench evaluation harness is available in the [GitHub repository](https://github.com/tradertanmay/undobench).
+
+Install and verify the benchmark:
+
+```bash
+git clone https://github.com/tradertanmay/undobench.git
+cd undobench
+pip install -e ".[all]"
+undobench doctor
+undobench smoke
+```
+
+Evaluate a custom agent on the validation split:
+
+```bash
+undobench run \
+  --agent my_agent.py:MyAgent \
+  --split validation \
+  --recovery none \
+  --seeds 42 \
+  --output runs
+```
+
+Or evaluate an OpenAI-compatible model endpoint:
+
+```bash
+undobench run \
+  --model openai/<model-name> \
+  --split validation \
+  --recovery naive \
+  --seeds 42 \
+  --output runs
+```
+
+After a run, recompute metrics and generate a standardized report:
+
+```bash
+undobench evaluate runs/<run_id>
+undobench report runs/<run_id>
+```
+
+UndoBench reports task competence and recovery metrics including Control Pass Rate, RSR, CRSR, EOR, DER, and MER.
+
+The frozen test split is protected from accidental execution by default. See the GitHub documentation for TEST execution, custom-agent integration, HTTP agents, recovery baselines, and result submission.
+
 ## Paper & Code
 
 * [Paper (arXiv)](https://arxiv.org/abs/2610.05622)