edd
Eval-Driven Development — split skills for scoping agents, running scenarios, building datasets, running experiments, and AI-driven dataset expansion against Opik judges
Plugin edd from marketplace edd in aboyalejandro/eval-driven-development. Commands read from the repo's manifest — 1 month ago.
What it installs
Skills (5)
eddEval-Driven Development router — picks the mode and dispatches. Asks two questions (mode 1/2, aggression 1/2/3), writes `.edd/session.json`, hands off to one of the phase skills. Use this when the user wants an eval workflow but hasn't named a phase. Skill-specific work belongs in `edd:scope-agent`,
experimentPromote branch-tagged traces into a durable Opik dataset + experiment with a UI scorecard. Outer loop of the eval pipeline. Use when the score must outlive the branch — cross-time comparison, reviewer handoff. Invoke as `edd:experiment` or when the user says "build dataset", "run experiment", "score
runFire scenarios at the agent. Mode 1 emits + tags traces for inline Claude analysis, with optional `edd score` to run judges and print the per-dimension table. Mode 2 emits + tags only — judges fire in `edd:experiment`, not here. A small smoke-judge-check (3–5 traces) verifies judges land before scal
scope-agentExtract the agent's promise inventory and write baseline regression scenarios. Produces `.edd/promises.md` (one line per skill/tool/promise) and `regressions.txt` (5–8 baseline scenarios at aggression 1). Idempotent — skip if both exist and agent source is unchanged. Invoke as `edd:scope-agent` or w
scope-evalsConfigure manual-trigger judges in Opik — derive one evaluator dimension per promise, **match against existing evaluators on the account first**, generate `_local/create_evaluators.py` only for gaps, run it. Output is `.edd/evaluator-plan.md` + judges live in Opik with `enabled=False, sampling_rate=
Maintain edd?
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