Operational pipeline to start, monitor, STOP, and recover the MathBench missing-lemma loop running in multi-node fleet mode on the MBZUAI cluster (watcher + slurmx fleet REPLs + login-node RPC host + serial adjudication + phase-2 import reconciliation). Use when launching/resuming the fleet run, stopping it cleanly, recovering after a crash, or applying the hard-won operational rules (correct kill sequence, git safety on the shared checkout, shell/conda/timeout fixes, re-collection prerequisite). For the single-machine AoA×PutnamBench eval use `aoa-putnam-eval` instead; for the design rationale of the loop see `MISSING_LEMMA_LOOP.md`.
How to refresh the local semantic embedding database (~1.5 GB of LMDB at ~/.cache/Isabelle_Semantic_Embedding). The development channel is the Hugging Face Hub tarball via manage_data.py (get/update). End users instead pull it anonymously from Cloudflare R2 with `semantics_manage.py pull`. Use when setting up or refreshing the semantic DB on a machine, or when deformalizations / vector stores are stale or missing. Do NOT run R2 `push` — it overwrites the shared remote and is a human-only action.
How to repackage/upload and download/unpack the Isabelle + paired AFP distribution, published together as contrib/Isabelle2025-2_and_afp-2026-05-13.tar.zst on the Hugging Face Hub (via manage_data.py update/get). Use when publishing a rebuilt Isabelle+AFP tarball (after isabelle scala_build, patches, etc.) or installing/refreshing the distribution on another machine.
How to read Isabelle introduction and elimination rules
Playbook for adding a new import to MathBench_ProverBase and reconciling any syntax conflicts (constant/type short-name resolution and notation) so that PutnamBench problems still parse to identical goal terms. Use when adding/changing imports of the MathBench_Prover session, or when investigating MathBench-vs-PutnamBench environment divergences.
How to read constants and theorems generated by Isabelle datatype and codatatype definitions
Essential reference for all Isabelle/ML development — system library, data structures, exception handling, and coding patterns. Load this skill whenever writing or modifying Isabelle/ML code.
Isabelle/ML coding style guide and conventions based on official implementation documentation