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catgo-campaign-loop

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Run and resume the CatGo md-orchestration poll loop — delegate each poll to a subagent (keep main context lean), verify convergence by force, auto-advance each converged species per-species (pipeline, not barrier), and resume a campaign from disk after context compaction / new session. Use when driving or resuming a campaign's job-watch loop. Pairs with catgo-campaign.

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How to use this skill

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/Hello-QM/catgo-LRG/blob/HEAD/server/catgo/workflow/skills/catgo-campaign-loop/SKILL.md

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catgo-campaign-loop — drive & resume the poll loop

TL;DR: Human-triggered ~10-min loop. Delegate each poll to a subagent (compact summary back). Verify convergence by force. Auto-advance each converged species to its next step (per species, not a barrier). State is on disk → any agent resumes.

RULE — delegate each poll to a subagent

Do NOT run poll/verify inline. Dispatch ONE subagent (opus) to run steps 1-3 (poll, ssh-read OUTCAR, verify, write result.md/STATUS/LESSONS) and return a compact summary only (one line per calc; no raw OUTCAR/OSZICAR/ssh dumps) — over a long run the verbose output would fill the main context toward 1M. Gates stay in the main agent (input-file gate, checkpoints): the subagent reports, the main agent shows the user + acts. The subagent must not submit/cancel jobs or touch the :8000 backend.

Each wake

  1. Read plan.md + active STATUS.md (keep working context lean).
  2. python poll.py --project <dir> --ssh <alias> — updates STATUS: queued via squeue; once a job leaves the queue, sacct gives the terminal verdict (COMPLETED→DONE; FAILED/TIMEOUT/OUT_OF_MEMORY/CANCELLED→FAILED; exit_code recorded).
  3. For finished calcs: a scheduler DONE ≠ "the science succeeded" — open the remote outputs and verify real convergence by FORCES: max atom < |EDIFFG| (force, NOT dE; the "kinetic energy error for atom" EATOM line is benign). Write energy_eV + max_force_eVA into result.md; on real failure (DONE-but-unconverged, or FAILED) record cause + fix in LESSONS.md.
  4. Auto-advance each newly-converged calc to its NEXT plan step — per species, PIPELINE, not a barrier. A converged geo_opt immediately triggers that species' next step (e.g. freq in a Gibbs study) from its CONTCAR; don't wait for siblings, don't wait for a user reminder. Render next-step inputs → input-file gate → submit_calc.py. ⛔ INPUT-FILE GATE (hard rule): "auto-advance" means auto-PREP, NOT auto-submit. Sync the converged CONTCAR and the next-step INCAR to the LOCAL folder, tell the user the exact LOCAL paths of INCAR + CONTCAR, and WAIT — the user checks/edits the files on disk. Submit ONLY after the user confirms. Do NOT push to the CatGO viewer as a substitute, and NEVER auto-submit. (YOLO waives.)
  5. Stage/decision point → python aggregate.py --project <dir> --plot → summary → checkpoint.
  6. Group meeting → python make_report.py --project <dir> --occasion groupmeeting.
  7. Unhandleable problem → write it to STATUS/LESSONS and stop (surface to the user).

Resuming (fresh agent / after compaction)

State lives ON DISK, not in context — a campaign survives compaction, a new session, or a different agent. To resume with zero conversation history:

  1. Invoke the catgo-campaign skill; identify the project dir.
  2. Read in order: README.md → plan.md (+ each calc/<stage>/plan.md) → cluster.md → every calc/**/STATUS.md → result.md files → LESSONS.md = done / running / next.
  3. Continue the loop (delegate each poll to a subagent). Keep the discipline: flush results/STATUS/LESSONS/plan to files as it happens — never hold campaign state only in context.

Unattended (fully-ended session)

ScheduleWakeup dies with the session. For a campaign that must advance without you, register a cron routine that wakes a fresh agent on a schedule to poll the project (it resumes from disk). Otherwise the user says "resume " in a new session.