Results for “feature-importance”

Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.

Rank a backlog of feature ideas by impact, effort, risk, and strategic alignment to identify the top 5 to pursue.

Evaluate feature investments using revenue impact, cost structure, ROI, and strategic value to make data-driven build/don't-build decisions.

Feature Intake

Categorize, evaluate, and prioritize customer feature requests against product goals using strategic alignment, impact, effort, and risk analysis.

Filters and ranks trending Hugging Face models, datasets, and spaces by novelty and significance, providing a 'why notable' explanation for each pick.

Prioritize features for an MVP or first release. Use when the team has too many ideas and needs must-have vs later vs never-for-v1 decisions tied to the core value loop, user pain, operational shortcuts, and launch feasibility.

Organizes Flutter code by business features instead of technical layers, with self-contained folders for UI, logic, and data, plus a recommended project structure and dependencies.

Rice

RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]

Cardinality and model family jointly determine the encoding.

RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]

The pitch is often "a central place to store features," which undersells it into

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

Manage feature manifests for code traceability. Use when creating new features, updating existing features, checking feature health, or exploring the feature-to-code relationship. Activates for manifest validation, feature creation, changelog updates, and traceability queries.

Systematically repair a broken feature or module by scoping, tracing dependencies, diagnosing all issues, fixing them in order, and verifying end-to-end.

Reference guide to 9 prioritization frameworks with formulas, when-to-use guidance, and templates — RICE, ICE, Kano, MoSCoW, Opportunity Score, and more.

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

Conducts structured requirements workshops to produce feature specifications, user stories, EARS-format functional requirements, acceptance criteria, and implementation checklists.

Influence Functions in Deep Learning

Rank a topic's user-flow branches by proof priority (value × risk × frequency) right after user-flow-map, ordering the branches, gating variation breadth, and promoting or pruning flows so state-model and ux-variations grow the tree in proof order — writes only existing flow-tree ordering fields, no schema change.

Guides a structured interview and repository research to clarify feature requirements, classify scope, and produce context artifacts for approval before design.

**When:** Not important (contact info)

Write the executable feature specification — the WHAT and WHY artifact that agents and reviewers treat as source of truth. Owns both /specify and /clarify modes. Load when the user asks to write a feature spec, write a specification, write an executable spec, define functional requirements, capture acceptance criteria as Given/When/Then, or when the spec-driven-development orchestrator routes here. Also triggers on "feature spec", "executable spec", "/specify", "/clarify", "write the spec for this feature", "specification for", "spec-driven", "machine-readable spec". Output: docs/specs/YYYY-MM-DD-<slug>-feature-spec.md. Hard gate: cannot Approve while [NEEDS CLARIFICATION] markers remain.

Generate a custom checklist for the current feature based on user requirements.

Mede o tamanho estrutural da feature ativa lendo requirements, duvidas, plan e tasks do ciclo forward, e gera size.json mais size.md com T-shirt sizing deterministico baseado em tasks e ajuste de risco. Use quando o usuario digitar "/reversa-pricing-size", "reversa-pricing-size", "dimensionar feature" ou "calcular tamanho da feature". Roda depois de `/reversa-to-do` e antes de `/reversa-pricing-estimate`.

分析故事情节点，识别关键情节与转折点。适用于深度分析情节结构、评估情节发展有效性

Guides a structured feature-development workflow: explore the codebase with subagents, draft a plan for approval, implement step by step, verify with parallel checks, and record conventions.

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

Use this skill for feature flags, gradual rollout, kill switches, metrics, experiment gates, rollback. Trigger when the task involves programming work related to Feature Flag Rollout, implementation, audits, debugging, strategy, or validation.

heath-no-fluff