Results for “random-forest”

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.

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.

Draws a Tarot spread to inject randomness into decision-making when prompts are vague or multiple approaches are equally valid.

Randomly selects winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests with fair, transparent selection.

Provides on-chain verifiable randomness for coin flips, dice rolls, raffles, shuffles, and game outcomes via the DARKSOL Random Oracle API on Base.

Runs a relentless interview that asks every frontier question at once, round by round. Use when the user wants to be grilled on a design or map a design tree.

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

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.

Create foliage types and place/scatter foliage instances on landscapes and meshes (FoliageService). Use when the user asks to add trees/grass/bushes/rocks as foliage, create a foliage type from a mesh, scatter or paint foliage instances, or query/remove foliage on a surface.

Enumerate and audit Active Directory forest trust relationships using impacket for SID filtering analysis, trust key extraction, cross-forest SID history abuse detection, and inter-realm Kerberos ticket assessment.

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.

Generate heightmaps, map reference images, and water feature splines (rivers, lakes, oceans) from real-world geographic data (terrain_data tool). Use when the user asks to build terrain from a real location/coordinates, download a real-world heightmap, or add rivers/lakes/oceans from map data. Downloads Mapbox tiles server-side and produces UE5-compatible heightmaps + landscape splines.

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.

Hunt for unusual network connections by analyzing outbound traffic patterns, rare destinations, non-standard ports, and anomalous connection frequencies from endpoints.

Structured brainstorming for projects and features. Explores multiple options before implementation.

Runs an Opportunity Solution Tree workflow with a lightweight graph database and CLI, routing through outcome, opportunity, solution, and assumption phases.

Edit images with Flux 1 Kontext Pro (Black Forest Labs' precise local image-edit model) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Flux Kontext's strengths (single-reference precise local edits, strong prompt control, consistent high-fidelity outputs), the schema (single image + prompt), and when to route to Nano Banana Edit / GPT Image 2 edit / Flux 2 Klein instead. Calls `runcomfy run blackforestlabs/flux-1-kontext/pro/edit` through the local RunComfy CLI. Triggers on "flux kontext", "flux-kontext", "flux 1 kontext", "kontext", "BFL kontext", or any explicit ask to edit with this model.

Proactively hunt for adversary abuse of legitimate system binaries (LOLBins) to execute malicious payloads while evading detection.

Forces unrelated concepts together to spark novel solutions and emergent properties.

Fetch random dog images and breed information from the Dog API (dog.ceo). Use when the user wants dog pictures or information about specific dog breeds. No API key required.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Use when inferring maximum-likelihood phylogenetic trees, selecting substitution models, running bootstrap support analyses, or performing partitioned phylogenetic analyses on sequence alignments.

Generate new feature ideas for an existing product using multi-perspective ideation from PM, Designer, and Engineer viewpoints, then prioritize the best five.

Biome - Fast all-in-one toolchain for web projects (linter + formatter in Rust, 100x faster than ESLint)

从真实的帖子、文章、发布说明、文档或网站文案中构建基于源材料的写作风格档案，然后在内容、外展和社交工作流中重复使用该档案。当用户希望保持声音一致性而不使用通用的AI写作套路时使用。

Explore codebases efficiently using AST-based structural search, outline, and symbol unfolding to reduce token usage.

Generates award-level UI with GSAP motion, bento grids, and Python-driven randomization for layout variety.

Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.

Composite skill — one-pass cleanup of stale local branches, dead worktrees, merged branches, and abandoned remote PR branches. Chains `git fetch --prune` → `clean_gone` (kill [gone] branches) → worktree prune + offer-to-remove dead worktrees → list-and-delete branches merged to main and release → delete remote PR branches whose PRs merged >7 days ago. Use instead of running `clean_gone` alone — that only catches half the rot. Daily-friction composite; fires on "clean up branches", "branch hygiene", "stale worktrees", and on session start when local branch count > 30.