Decode Minified Js Gates

Bulk-extract every candidate flag from a binary namespace, build an extraction inventory with occurrence counts and call-type tags, cross- reference against a documented set, and track completeness across probe campaigns until the undocumented remainder reaches zero. Covers namespace prefix harvesting, gate-vs-telemetry disambiguation at the call-site level, completeness metrics, DEFAULT-TRUE population reporting, and a final completion confirmation scan. Use upstream of probe-feature-flag- state when you need a complete catalog rather than a sample, or when a prior wave-based campaign needs a verifiable end condition.

Probe the runtime state of a named feature flag in a CLI binary. Covers the four-pronged evidence protocol (binary strings, live invocation, on-disk state, platform cache), the four-state classification (LIVE / DARK / INDETERMINATE / UNKNOWN), gate-vs-event disambiguation, conjunction-gate handling, and skill-substitution scenarios where a flag appears DARK but the capability is delivered by other means. Use when verifying whether a documented or inferred capability has rolled out, when auditing dark-launched features, or when a prior probe's conclusions need refreshing against a new binary version.

Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings. Triggers: "azure-appconfiguration", "AzureAppConfigurationClient", "feature flags", "configuration", "key-value settings".

Implement cuttlefish-inspired adaptive interfaces — polymorphic APIs, context-aware behavior, feature flags, and attack surface reduction. Covers environmental assessment, chromatophore mapping, dynamic interface generation, behavioral polymorphism, and pattern disruption for systems that must present different faces to different observers. Use when a system must present different interfaces to different consumers, when reducing attack surface by exposing only what each observer needs, when implementing feature flags or progressive rollouts at the interface level, or when adapting behavior to environmental context without core changes.

A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request

Operational discipline for feature flags as production infrastructure. Flag types, naming, targeting rules, rollout strategy, lifecycle, governance, stale flag management, and the technical debt patterns that bite teams who weren't deliberate about it.