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Governed Skill Evolution from Persistent Agent Experience

This repository contains a prospective ablation and cross-model transfer study inspired by WikiSkill. It evaluates three ways to retain experience while evolving a procedural Skill for permission-safe repository planning:

  • no persistent history;
  • flat chronological history;
  • a persistent Pattern Registry and forward-chained Skill Impact Ledger.

The completed study contains three replicate sequences, three evolution iterations per condition, 18 frozen cases, two model versions, and 87 completed model calls. Persistent Wiki did not outperform flat history on task quality in this setting. It used fewer input tokens and produced a larger target-model Skill gain, but had lower final task quality and more rollbacks.

Condition Task quality Input tokens Rule lines Rollbacks Target-model Skill gain
No Wiki 87.4158 127016.7 28.3 2.33 12.7970
Flat history 89.6189 145269.3 41.0 1.67 9.6585
Persistent Wiki 87.3322 134773.7 32.7 2.00 11.6270

The result is intentionally bounded: one synthetic deterministic grader, three replicates, and two Codex model versions in one model family. It is not a provider comparison, safety certification, or general claim about Agent performance.

Repository contents

  • paper/paper.pdf: publication-ready paper.
  • paper/paper.md: readable source.
  • results/: sanitized row-level results and verified ledger summaries.
  • scripts/build_paper.py: offline PDF builder.
  • scripts/verify_artifacts.py: result, digest, and publication checks.
  • REPRODUCIBILITY.md: exact design and evidence boundaries.

The executable study harness, frozen datasets, schemas, final Skills, Pattern Registries, and full sanitized impact ledger are maintained in Runtime Evolution Workbench.

Citation

See CITATION.cff.

License

Source code and machine-readable file protocols are Apache-2.0. The manuscript, documentation, result tables, figures, and release metadata are CC BY 4.0. See LICENSE and CONTENT-LICENSE.md.