CogniFold: Skill for Claude Code

LongMemEval Autonomous Iteration

When to use

• User says "iterate LongMemEval" / "run the longmemeval loop" / "continue R10"
• After a fresh clone, before any iteration: walk §0 setup
• Any time the autonomous loop is mid-cycle and needs to resume

Hard rules (never violate)

1. Branch lock: only commit/push on longmemeval-iter. Verify git branch --show-current returns longmemeval-iter before any git commit. Never touch main / iter / public-release / etc.
2. Judge lock: --judge-model openai:gpt-4o always. Substituting breaks comparability with Mastra / Hindsight numbers.
3. Symbolic stack on: --symbolic-resolver --symbolic-temporal --symbolic-bypass must all stay enabled (~5 pp on the score).
4. Full N=500 each round: no stratified < 133, no sampled subsets. Resume makes incremental cost ≈ wall-clock of one batch anyway.
5. Cluster-then-diagnose-then-propose: every fix must follow the protocol in references/iteration-rules.md. Skipping this step is the #1 historical cause of regressions.

Setup (one-time per fresh machine)

Run scripts/check_setup.sh — it verifies branch, push credentials, remote, model config in scripts/parallel_longmemeval.sh, and that history_max_effort.md + .max_effort_round exist (creates them if not). Halt and surface any failures.

The loop

loop forever:
    ROUND = read+bump .max_effort_round

    # (1) Baseline: full N=500 run
    bash scripts/parallel_longmemeval.sh <N_PARALLEL> 133 500
    # N_PARALLEL from references/model-config.md Tier table

    # (2) Measure
    metrics = json.load("benchmarks/longmemeval/output/metrics.json")
    correct = metrics["correct"]

    # (3) Terminate if ≥475 AND confirmation rerun also ≥475
    if correct >= 475:
        run confirmation rerun (rm hypothesis.jsonl, re-run full N)
        if confirmed correct2 >= 475:
            commit FINAL + push + EXIT
        # else fall through with corrected (lower) baseline

    # (4) Snapshot pre-fix state
    cp -r output/ output_v${ROUND}/
    append baseline metric to history_max_effort.md
    git add + commit + git push origin longmemeval-iter

    # (5) Analyze failures per references/iteration-rules.md §A-B-C
    # (6) Propose fix (estimate trigger isolation; not a gate)
    # (7) Drop test_set qids, re-run with SAME N_PARALLEL (see scripts/drop_qids.py)
    # (8) Compute net = fixes - regressions vs output_v${ROUND}/
    # (9) Apply references/iteration-rules.md decision table:
    #     net ≥ +1                   → keep
    #     net ∈ {0, -1} + reusable   → keep (infra)
    #     net ≤ -2                   → revert (restore verdicts + git revert)
    # (10) Commit + push the post-fix state
    # Loop back to (1)