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JiamingShi-JsPhysJiamingShi-JsPhys/chelate-shell/skills/multi-dim-manager/SKILL.md

multi-dim-manager

Skill10 starsChanged 6 days agoMITView on GitHub

Runs a parameter sweep over many rounds and keeps a hash-based audit trail. Snapshots the declared critical files before and after each round, records parameters, hashes, timestamps and status, and rolls any past round back non-destructively. Fires when a workflow repeats over a Cartesian parameter space or when rollback to an earlier round must stay possible.

1 other project uses a version of this.

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The file

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2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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  • Distinctive
  • Very specific
  1. Distinctive:

    Run this exact form — do not read the source tree:

    line 29
  2. Very specific:

    Always dryrun first: it prints the full round plan with no side effect.

    line 35

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

multi-dim-manager

Orchestrates repeated rounds: expands the parameter grid, snapshots declared critical paths, records an auditable round log, and restores any past round. It never computes your domain result — the per-round computation belongs to your plugin.

  • Stage: workspace · Sandbox: no · Dependencies: stdlib only (PyYAML only for .yaml configs; JSON configs need nothing)

Quick Execution

Run this exact form — do not read the source tree:

python run.py --root <WS> --action dryrun -- --config config.example.yaml
  • Always dryrun first: it prints the full round plan with no side effect.
  • --config is a workspace-relative or absolute config file (JSON or YAML).
  • The gate lands in <WS>/process/gates/multi-dim-manager_<ts>.json.

When to use

  • A workflow repeats over a grid of parameters (rounds, seeds, conditions).
  • Per-round snapshots of declared critical files must exist before and after each round.
  • A full audit trail — parameters, hashes, timestamps, status — must be reproducible.
  • The user must be able to roll back to any past round without destroying the live state.

When NOT to use

  • A single run with no parameter space — a plain script is enough.
  • You want the manager to compute the result: it only orchestrates.

Actions

--actionDoesExtra arguments
dryrunPrint the round plan, no side effect--config <cfg>
runExecute the sweep--config <cfg>
rollbackRestore a past round into a new directory--config <cfg> --round N [--force]
summaryRegenerate the round summaries--config <cfg>
verifyCheck one round's recorded outcome--config <cfg> --round N

Hard constraints

  • Never compute the result yourself. Domain math belongs in the plugin.
  • Snapshot only declared critical paths. A full workdir copy is not what this does.
  • Rollback is non-destructive by default. Without --force the live state is only read.

Outputs

  • <WS>/process/gates/multi-dim-manager_<ts>.json — the gate result.
  • Per-round snapshot directories and the round log under process/.

Failure handling

  • A plugin exiting 1 marks the round failed; exit 2 marks it error.
  • A malformed config (missing space, bad YAML, no {OUT}) aborts before any round runs.
  • Rolling back to an unrecorded round, or one without a post-snapshot, is refused with a message.

Limitations

  • Determinism covers orchestration only; the plugin's own output is its responsibility.
  • Files not declared as critical paths are never snapshotted.
  • .yaml configs need PyYAML; JSON configs run on the standard library.

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