Use when designing, implementing, reviewing, testing, or cleaning up feature toggles, feature flags, kill switches, runtime configuration gates, canary controls, staged rollouts, experiments, or temporary compatibility switches in Java enterprise systems. This should trigger for requests such as Design a feature toggle strategy; Review this feature flag; Add a kill switch safely; Test toggle on and off paths; Clean up an expired feature toggle; Plan controlled rollout and rollback behavior. Part of Plinth Toolkit
npx skills add https://github.com/jabrena/plinth --skill 057-design-feature-toggles
Guide Java Enterprise developers through safe feature toggle design and lifecycle management. This is an interactive SKILL.
What is covered in this Skill?
Use feature toggles as explicit, owned runtime control points rather than hidden conditional complexity.
references/057-design-feature-toggles.md before applying feature toggle guidanceRead references/057-design-feature-toggles.md, then identify the runtime behavior change, rollout risk, rollback expectation, affected users, operational owner, and whether a feature toggle is the smallest safe control mechanism.
Define the toggle type, stable name, typed decision API, default state, evaluation context, fallback behavior, audit needs, metrics, logs, and cleanup trigger. Prefer one decision point per behavior over repeated raw flag checks.
Place toggle evaluation near an application service, adapter, strategy selector, or request boundary. Keep domain code explicit, avoid secret or personal data in rules, and preserve consistent decisions across a request, transaction, message, or batch item.
Check whether disabled behavior preserves the current production contract, enabled behavior has rollout guardrails, operational teams can disable the behavior quickly, and cleanup will not break old deployments, data, or integrations.
Test disabled, enabled, fallback, configuration failure, rollout targeting, and cleanup scenarios. Add metrics, logs, alerts, or dashboards that show toggle state, decision volume, error rate, latency impact, and kill-switch activation.
Report the decision, toggle contract, implementation boundary, test matrix, observability plan, rollout and rollback steps, cleanup owner, removal trigger, skipped checks, and remaining risks.
For detailed guidance, examples, and constraints, see references/057-design-feature-toggles.md.
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 jabrena/057-design-feature-toggles 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.