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@@ -18,33 +18,10 @@ Test AI targets on real repo tasks and measure what actually works.
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-**Category** is derived from where the eval lives, such as folder path and file name. Use paths to organize the corpus instead of repeating category labels in every eval.
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-**Workspace / fixtures / graders** are task-owned context: repos, setup scripts, files, fixtures, isolation, deterministic checks, and LLM grading prompts.
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-**Target** is the system under test: an agent, provider, gateway, replay target, CLI wrapper, transcript provider, or future app/service wrapper. Each eval selects one `target`, either by label from `targets.yaml` or with an eval-local target object.
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-**Experiment**is the run/result grouping label being measured over that corpus, such as `with-skills` or `without-skills`. Keep suite/category and target/model names out of this label.
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-**Tags**are run/result grouping labels. `tags.experiment` is the default experiment namespace, such as `with-skills` or `without-skills`; keep suite/category and target/model names out of that tag.
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-**Evaluate options** configure runner-level behavior such as repeat policy, optional timeouts, and `max_concurrency` under `evaluate_options`.
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-**Default test** configures inherited per-test defaults such as score `threshold`.
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-**Run** is one concrete execution of an experiment against a resolved target that writes portable artifacts for readers such as Dashboard, compare, and trend.
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```mermaid
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flowchart LR
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corpus["Eval suite / imports / tests<br/>task corpus"]
-**Run** is one concrete execution of a tagged eval against a resolved target that writes portable artifacts for readers such as Dashboard, compare, and trend.
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