recursive-improvelisted

# recursive-improve: Agent Improvement Pipeline End-to-end pipeline: trace analysis → skill extraction → domain context → metrics → rubric → action plan → review → fixes. ## Prerequisites Traces must exist in `eval/traces/`. If they don't: - Ask the user for their traces directory - Copy `.json`, `.md`, and `.toon` files into `eval/traces/` **Skip condition:** If `eval/stage1_insights_summary.md` already exists (from a prior run or from `recursive-improve analyze`), skip Stages 0 and 1 — go directly to Stage 2. --- ## Stage 0: Trace Analysis Analyze raw execution traces to extract learnings. This stage adapts ACE's recursive reflector methodology — a structured 6-phase strategy that moves from data discovery through verified deep-dives to synthesized, evidence-backed insights. ### Inputs - `eval/traces/` — raw trace files (`.json`, `.md`, `.toon`) ### Phase 1: Discover Map the data shape and inventory. Do NOT judge outcomes yet — just catalog what you have. 1. Read 2-3 trace files. Identify: - Top-level keys and message schema (3 levels deep) - Message format: `role`, `content`, `tool_calls`, `turn_idx`, etc. - Total trace count and per-trace message counts 2. Search for **agent operating rules, policy, or instructions** embedded in the traces — these are often in large strings (>500 chars). Check: - `role: "system"` messages - `info.environment_info.policy` or similar fields - Large embedded strings in any field 3. Build an inventory table: