Iterative Debugging (Fix Loop)

For BEHAVIOR-1K (R1 Pro on Isaac Sim) use the sibling folder behavior/SKILL.md. It is a different protocol, not just different prompts: those tasks are long-horizon, so each seed grows its own policy one code block at a time, and the campaign freezes a skill library rather than a program. Everything below is for the tabletop benchmarks (LIBERO-Pro, Robosuite).

Use this skill to run a coordinated debugging campaign over any list of CaP benchmark tasks. No external baseline code or baseline output directory is used. Runs use the benchmark's own --init-mode (LIBERO: seeded, so development seeds 51–65 and the held-out partition are distinct scenes; Robosuite uses saved).

Setup

1. Complete benchmark setup for the target embodiment (docs/libero-pro.md, or docs/robosuite.md), including the pinned environment (for LIBERO: scripts/bootstrap_libero.sh → .venv-libero) and SAM3 authentication.
2. Export the benchmark environment variables (for LIBERO: LIBERO_CONFIG_PATH="$PWD/.libero", MUJOCO_GL=egl, PYOPENGL_PLATFORM=egl, EGL_PLATFORM=device, TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1).
3. Start perception services (scripts/supervise_services.sh) and a persistent coordinator session.
4. Read docs/api-reference.md, docs/run-artifacts.md, and main-agent-prompt.md.

Run Order

1. Take the task list from the user (any set of (benchmark, suite, task_id) cases), write it to a JSON file (format in scripts/gen_progress.py), and initialize the campaign root: python3 .claude/skills/iterative-debugging/scripts/init_run.py --tasks <tasks.json>. This creates RUN_ROOT=outputs/runs/<stamp>/ (and points outputs/runs/LATEST at it) — every artifact of this campaign lives under it, one folder per task at $RUN_ROOT/<benchmark>/<suite>/task_<id>/. Generate progress with python3 .claude/skills/iterative-debugging/scripts/gen_progress.py. For Robosuite, append --dev-seeds $(seq 101 125) --unseen-seeds $(seq 126 130) --heldout-count 100 to initialization. LIBERO defaults remain development 51–65, unseen-check 66–80, and held-out 1–50. Read the assigned development seeds, unseen-check seeds, and HELDOUT_COUNT from campaign.json, preserving those settings when resuming.
2. Follow main-agent-prompt.md as coordinator. You are also the curriculum: choose each round's tasks from what the library can currently teach, and record the decision in $RUN_ROOT/curriculum.md. Dispatch subagent-prompt.md once per pending task. Workers explore (skills/task-exploration.md), generate initial code, debug the assigned development seeds without reading external baseline outputs, and grade the skills they used in skill_report.json. Debugging is organized by failure mode rather than by seed, and every version is scored on all assigned development seeds. The selected program is then measured once on the assigned unseen-check seeds (LIBERO: 66–80; Robosuite: 126–130) before being handed over.
3. The coordinator (not workers) runs the Stage 2 held-out evaluation (seeds 1–$HELDOUT_COUNT) for each stage1-done task with scripts/run_validation.py.
4. When a task proposes skills, dispatch verifier-prompt.md on the same task. The verifier re-solves it from the skill library and the proposals alone — without the proposer's code — on the first few assigned development seeds (LIBERO: 51–55) within two attempts. Only verified proposals are promoted into skills/.
5. Use clean-task-slate.md before reruns.
6. Continue until every task has all $HELDOUT_COUNT held-out results, then run the phase-3 rerun sweep once: tasks that scored below threshold under a library that has since changed are re-run against the final library, into rerun_02/ beside the original attempt.