Pattern Mining

Pattern Mining

Decide whether repeated code demonstrates a reusable rule or only a plausible hypothesis. Similar names and syntax are not enough; the abstraction must survive examples it was not designed around.

Constraints

• To prevent lineage copies from faking recurrence, use independently implemented exemplars with repository anchors.
• Because the candidate must generalize, form it without seeing the holdout and back-apply every holdout-driven refinement.
• To keep weak evidence from becoming architecture, route hypotheses to no-action; only a fully proven promotion may reach operationalize.

Workflow

1. State the candidate pattern and collect independently implemented exemplars with repository anchors. Use research when coverage is unclear.
2. From the exemplars, separate required invariants, legitimate variation points, and incidental similarity.
3. Require at least three distinct exemplars before promotion is possible. Form the candidate abstraction without using the holdout.
4. Test it against every exemplar, then a separate holdout. Back-apply the refined abstraction to the original exemplars so the holdout fix cannot silently break them.
5. Emit outcome: promote only when the exemplar floor, holdout, and back-application all pass. Route that evidence to operationalize, which decides whether the eventual shape is a skill, gate, library, template, or no action.
6. Otherwise emit outcome: hypothesis with route: no-action. Keep the evidence bounded and name what additional observation would retest it.

Diff/align across exemplars

Invariants are extracted mechanically, not remembered. Lay the exemplars side by side, align them structurally (same role, same position in the flow — not same variable names), and diff: what survives every alignment is a candidate invariant; what varies by site is a variation point; what varies with no functional consequence is incidental. Work pairwise before generalizing — an