claude

experimentation-growth-engineering

Experimentation & growth-engineering team — agents (experimentation-architect, feature-flag-engineer, product-analytics-instrumentation-engineer) for the APPARATUS that lets a team learn fast and ship safely: feature flags + progressive delivery (kill switches, targeting, flag lifecycle/debt), A/B-test plumbing (assignment, exposure logging, SRM checks, sequential-peeking guards, the results read), and trustworthy event instrumentation (a tracking plan, a consistent event schema, identity stitching, a CDP). The engineering of experimentation — statistical VALIDITY (power/MDE, significance) is applied-statistics'. skills, a decision-tree knowledge bank (flag-vs-config + ship-decision trees + a dated 2026 map), best-practices, templates, commands, an advisory hook. Seams: significance -> applied-statistics, hypothesis/outcome -> product-management, rollout -> devops-cicd, pipelines -> data-platform. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.3.5MITupdated today
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Plugin experimentation-growth-engineering from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.

What it installs

Skills (5)

  • ab-test-plumbing

    Build trustworthy A/B-test plumbing: deterministic sticky assignment, exposure logging (who actually saw the variant), Sample-Ratio-Mismatch checks before reading results, pre-registered metric/MDE/duration, and guardrail metrics — leaving significance to applied-statistics.

  • event-instrumentation

    Instrument trustworthy product analytics: a tracking plan first, a consistent typed event/property schema (object_action naming, versioned), identity stitching (anonymous -> known), CDP routing (instrument once), and event-quality validation as code.

  • feature-flags

    Use feature flags safely: match the flag type to its purpose (release/experiment/ops/permission), give every risky change a kill switch, roll out progressively gated by a health signal, evaluate deterministically and fail-safe, and manage the flag lifecycle to prevent debt.

  • srm-detection

    Procedure for detecting and diagnosing Sample-Ratio Mismatch in A/B experiments — covers the chi-squared test, common causes by layer, and the decision rule for whether to trust or discard a result.

  • tracking-plan-design

    Step-by-step guide for designing a product analytics tracking plan — event taxonomy, property schema conventions, identity stitching strategy, and the governance process to keep the plan as the source of truth.

Slash commands (4)

  • /experimentation-growth-engineering:design-tracking-plan

    Design a tracking plan with a consistent typed event schema, identity stitching, and CDP routing.

  • /experimentation-growth-engineering:read-experiment

    Certify an experiment's trustworthiness (SRM, exposure, no-peeking, guardrails) and route the significance verdict to applied-statistics.

  • /experimentation-growth-engineering:setup-experimentation

    Build trustworthy A/B-test plumbing: assignment, exposure logging, SRM checks, guardrails, pre-registration with applied-statistics.

  • /experimentation-growth-engineering:setup-feature-flags

    Set up feature flags typed by purpose with kill switches, progressive rollout, and a lifecycle policy.

Subagents (3)

  • experimentation-architect

    Use for the experimentation system: deterministic assignment and exposure logging, Sample-Ratio-Mismatch and trustworthiness checks, the experiment lifecycle, guardrail metrics, and platform build-vs-buy. Partners with applied-statistics (power/MDE); routes flags to feature-flag-engineer.

  • feature-flag-engineer

    Use for feature flags and safe rollout: distinguishing flag types (release/experiment/ops/permission), targeting and segmentation, kill switches, progressive rollout gated by a health signal, deterministic sticky evaluation, fail-safe SDK integration.

  • product-analytics-instrumentation-engineer

    Use for product-analytics instrumentation: a tracking plan, a consistent typed event/property schema, identity stitching (anonymous -> known), CDP routing (Segment-style), and funnel/retention instrumentation. Routes the warehouse to data-platform and metric definitions to product-management.

Hooks (1)

  • PreToolUse

    /usr/bin/env -u BASH_ENV -u ENV -u SHELLOPTS -u BASHOPTS -u LD_PRELOAD -u LD_LIBRARY_PATH -u LD_AUDIT -u DYLD_INSERT_LIBRARIES -u DYLD_LIBRARY_PATH -u PYTHONPATH -u PYTHONSTARTUP PATH=/usr/local/bin:/

View source on GitHub mcorbett51090.github.io

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