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lllllllamalllllllama/RigorPilot-Skills/skills/explore-code/SKILL.md

explore-code

Skill495 starsChanged 6 months agoMITView on GitHub

Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.

1 other project uses a version of this.

Adopted by

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The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 6 are distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use this as the Rigor Improve implementation leaf skill.

    line 8
  2. Very specific:

    Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md.

    line 11
  3. Distinctive:

    When the researcher explicitly authorizes exploratory code changes on an isolated branch or worktree.

    line 17
  4. Distinctive:

    When the task is source-anchored module transplant, backbone adaptation, LoRA or adapter insertion, or low-risk module combination.

    line 18
  5. Distinctive:

    When summary-level recording is sufficient and the result is a candidate, not a trusted conclusion.

    line 19
  6. Distinctive:

    When the request is for trusted baseline work, conservative debugging, or normal training execution.

    line 23
  7. Distinctive:

    When the user did not explicitly authorize exploratory modifications.

    line 24
  8. Distinctive:

    When the task is a broad refactor or a from-scratch idea implementation.

    line 25
  9. Very specific:

    Use ai-research-explore instead when the task spans both current_research coordination and exploratory runs.

    line 31
  10. Very specific:

    Use references/explore-policy.md, ../ai-research-reproduction/references/research-rigor-principles.md, scripts/plan_code_changes.py, and …

    line 47

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

explore-code

Use this as the Rigor Improve implementation leaf skill. The installed slug remains explore-code for compatibility.

Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md; this skill should guide bounded candidate code work without over-prescribing implementation details.

When to apply

  • When the researcher explicitly authorizes exploratory code changes on an isolated branch or worktree.
  • When the task is source-anchored module transplant, backbone adaptation, LoRA or adapter insertion, or low-risk module combination.
  • When summary-level recording is sufficient and the result is a candidate, not a trusted conclusion.

When not to apply

  • When the request is for trusted baseline work, conservative debugging, or normal training execution.
  • When the user did not explicitly authorize exploratory modifications.
  • When the task is a broad refactor or a from-scratch idea implementation.

Clear boundaries

  • This skill owns exploratory code modifications only.
  • It must keep work isolated from the trusted baseline.
  • Use ai-research-explore instead when the task spans both current_research coordination and exploratory runs.
  • It may hand off execution to minimal-run-and-audit or run-train.
  • It should favor source-anchored copying and minimal adaptation over freeform rewrites.
  • It should record why a candidate change is meaningful, how to roll it back, and why it remains a candidate rather than a verified contribution.

Output expectations

  • explore_outputs/CHANGESET.md
  • explore_outputs/SCIENTIFIC_CHANGELOG.md
  • explore_outputs/COMPARABILITY_REPORT.md
  • explore_outputs/TOP_RUNS.md
  • explore_outputs/status.json

Notes

Use references/explore-policy.md, ../ai-research-reproduction/references/research-rigor-principles.md, scripts/plan_code_changes.py, and scripts/write_outputs.py.

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