Use when writing a Storybook story for a component gated on a feature flag — boolean flags or multivariate/experiment-arm variants. Covers the `featureFlags` story parameter and why imperatively setting flags renders the flag-off branch in visual-regression snapshots while passing in jest.
npx skills add https://github.com/PostHog/posthog --skill setting-feature-flags-in-storybook
> [!WARNING]
> Never call featureFlagLogic.actions.setFeatureFlags(...) from a story. It silently
> no-ops in the visual-regression runtime (rendering the flag-OFF branch) while passing
> in jest — so unit tests stay green and the snapshot is wrong. Use the featureFlags
> story parameter instead.
Mental model: boolean flags always work (they ride the always-merged baseline);
variant values need a gate that _only the featureFlags parameter opens correctly_.
This is the stable contract — prefer it over anything in "Under the hood" below.
const meta: Meta = {
title: 'Scenes/MyScene',
parameters: {
featureFlags: [FEATURE_FLAGS.MY_FLAG, FEATURE_FLAGS.OTHER_FLAG],
},
}
Array entries are flags that evaluate to true. This is the common case.
For a flag whose value is a variant string (experiment arms, multivariate rollouts), use
the record form — the array form can only express true:
// meta-level: applies to every story in the file
parameters: { featureFlags: { [FEATURE_FLAGS.THEME_OVERRIDE]: 'intent_plus' } }
// per-story: a different arm per story
export const ControlArm: Story = {
parameters: { featureFlags: { [FEATURE_FLAGS.THEME_OVERRIDE]: 'control' } },
}
export const TreatmentArm: Story = {
parameters: { featureFlags: { [FEATURE_FLAGS.THEME_OVERRIDE]: 'intent_plus' } },
}
You can mix booleans and variants in one record: { 'some-bool-flag': true, 'my-experiment': 'test_b' }.
> [!IMPORTANT]
> A story's parameters replace the meta's (shallow merge), so a per-story
> featureFlags drops every flag set at the meta level. **Re-list any meta flags you
> still need** — silently losing one renders the flag-off branch and is easy to miss.
Setting flags imperatively (featureFlagLogic.actions.setFeatureFlags) works in jest
(NODE_ENV==='test') but not in the built visual-regression Storybook. posthog-js loads
flags and fires onFeatureFlags with an empty set, which dispatches setFeatureFlags
and wipes whatever you set imperatively — so the unit test passes while the snapshot
renders the flag-OFF branch. The featureFlags parameter avoids this by writing flags to
the always-merged baseline instead (below), so the empty callback can't clobber them.
> Do not try to fix this by disabling posthog-js flags (e.g. advanced_disable_feature_flags).
> The app shell (appLogic.showApp) only renders once receivedFeatureFlags is true,
> which is set by that same onFeatureFlags callback — suppress it and every
> Scenes-App/* story stalls behind a 3s timeout and flakes.
> This explains _why_ the parameter works, for trust and for anyone extending the harness.
> Treat "How to use it" above as the contract, not this.
withFeatureFlags → setFeatureFlags (frontend/src/mocks/browser.tsx) writes the
flags (array or record) straight to window.POSTHOG_APP_CONTEXT.persisted_feature_flags.
getPersistedFeatureFlags (frontend/src/lib/logic/featureFlagLogic.ts) reads that as
featureFlagLogic's initial value and spyOnFeatureFlags always merges it as the
baseline — so both booleans and pinned variants survive the empty onFeatureFlags
callback. The only production-side support this needs is getPersistedFeatureFlags
accepting the record form (variant values) in addition to the server's array form.
You should not need to touch any of this. If you're extending the harness, that's the seam.
The featureFlags parameter only controls the flag. If a component's render also depends
on other runtime state (localStorage, an APP_CONTEXT field, a kea logic that resolves an
entry), stage that state too — typically in a small wrapper the story renders that sets it
and mounts the logic before rendering the component. Keep that wrapper in the story file;
don't add test-only props to the production component to make it renderable.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take posthog/setting-feature-flags-in-storybook from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.