Add feature-flag engine with targeting and rollout#276
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decision_table is one-shot DMN and ab_locator is locator A/B; neither is a product feature-flag store with sticky percentage rollout. Add an OpenFeature-shaped engine: targeting rules, weighted variants, a kill switch, and consistent-hash bucketing so a subject always lands in the same variant. Pure stdlib and deterministic. Wired through the facade, AC_evaluate_flag/AC_flag_enabled executor commands, MCP tools and the Script Builder.
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 60 |
| Duplication | 1 |
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Summary
decision_tableis a one-shot DMN evaluator andab_locatormeasures locator outcomes — neither is a product feature-flag store with sticky percentage rollout. This adds an OpenFeature-shaped flag engine.FlagStore.from_dict/from_file— load flag definitions.evaluate_flag(store, key, context)→{value, variant, reason}with reasonsTARGETING_MATCH/SPLIT/DEFAULT/DISABLED/ERROR.is_enabled(...)boolean shortcut;percentage_bucket/assign_variantfor direct use.Targeting operators:
eq/ne/lt/le/gt/ge/in/not_in/contains/semver_*. Percentage rollout is a consistent-hash bucket ofsha256("{key}.{salt}.{context_key}"), so a subject is sticky (always the same variant). Kill switch viaenabled: false. Pure stdlib (hashlib+re+json), deterministic.Five-layer wiring
je_auto_control/utils/feature_flags/__init__.py+__all__AC_evaluate_flag,AC_flag_enabledac_evaluate_flag,ac_flag_enabledTests & docs
test/unit_test/headless/test_feature_flags_batch.py(12 tests: targeting, rollout SPLIT, kill switch, unknown, sticky ~50% distribution, semver)Lint clean: ruff / pylint / bandit / radon.