pattern-mining

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Test repeated implementation shapes against independent exemplars and a holdout before routing an earned abstraction. Triggers: "mine a recurring code pattern", "is this abstraction earned", "extract invariants from implementations".

AI & Automation 446 stars 41 forks Updated today Apache-2.0

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Skill Content

# 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...

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Author
boshu2
Repository
boshu2/agentops
Created
10 months ago
Last Updated
today
Language
Go
License
Apache-2.0

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