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Use this skill to continue CP2K AIMD from existing result directories without losing restart context or overwriting previous outputs.
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Based on SOC occupation classification
| name | cp2k-aimd-restart |
| description | Use this skill to continue CP2K AIMD from existing result directories without losing restart context or overwriting previous outputs. |
| allowed-tools | cp2k_aimd_prepare cp2k_output_summary md_trajectory_summary remote_submission remote_submission_batch get_avail_remote_task execute |
Use this skill when a CP2K AIMD calculation needs continuation from a prior stage. Restart setup belongs to dynamics_worker.
cp2k_output_summary and md_trajectory_summary to confirm the prior stage state.cp2k_aimd_prepare(recipe="restart").remote_submission(task_name="cp2k_execute").cp2k_aimd_preparecp2k_output_summarymd_trajectory_summaryremote_submissionremote_submission_batchget_avail_remote_taskexecutesettings.restart_file and settings.structure_file when auto-discovery would be ambiguous.ensemble, temperature, pressure, timestep_fs, trajectory_stride, energy_stride, and restart_stride.cp2k_summary.json, job.out, .ener, trajectory, and restart files before reporting.Return:
references/cp2k_aimd_restart_reference.mdProposal-first scientific writing pipeline. Three modes (compose/revise/hybrid) with four-layer QA pipeline. Enforces evidence-before-prose, argument-before-sections, and contracts-before-paragraphs.
Use this skill for managed MLFF molecular dynamics, restart-safe trajectory continuation, ensemble selection, and trajectory-health analysis when mlff_md is available.
Use only after a DPDispatcher-backed managed execution tool returns a failure with receipt/context fields, an ambiguous transport error, or evidence of a possible orphan job. Do not use for a pending synchronous call or ordinary success.
Use this skill before remote_submission or remote_submission_batch to build and verify canonical stage directories for registered DPDispatcher tasks, including deterministic MLFF SP/relax, MD, and NEB input layouts.
Use this skill for managed MLFF NEB optimization after a complete locally interpolated fixed-image path has been validated.
Use this skill for MLFF single-point screening, ranking, and geometry relaxation when choosing among enabled MACE, FairChem UMA, MatterSim, or ORB-v3 backends.