Results for “pattern”

DEPRECATED in v5.6.0 — see joint-multi-tf-v560 skill. Documents the v5.2.0 dual-model approach (train separate 15Min/1Hour models, combine via weighted voting). Still relevant for: (1) loading legacy v5.5.0 dual models, (2) understanding the historical aggregation layer, (3) resampling pattern via origin='start'.

Polishes UI primitives one at a time using research-backed patterns: adds loading and icon states to buttons, replaces inline skeletons with primitives, adds secondary actions to errors, ensures 48px touch targets and visible focus indicators, and applies a standard motion token ladder.

Analyzes DeFi protocol risk before deposit, decomposes headline APY into sustainable yield, emissions, and drag factors, and flags common rug-risk patterns in tokenomics, liquidity, and governance. Advisory only—no wallet access, no private key handling, no transaction signing, no on-chain execution.

Optimizes React and Next.js performance with 70+ rules across 8 priority categories, covering waterfalls, bundle size, server-side, client fetching, re-renders, rendering, JS micro-perf, and advanced patterns. Use when writing, reviewing, or refactoring React/Next.js code.

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.

Document architectural decisions in standardized ADR format. This skill should be used when making significant technical decisions, choosing between approaches, establishing patterns, or when other skills (create-plan, implement-plan, brainstorm) identify decisions that need documentation. Triggers on "document decision", "create ADR", "architectural decision", or automatically when invoked by other skills during planning and implementation.

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.

Learn how to install and migrate to Jetpack Navigation 3, and how to implement features and patterns such as deep links, multiple backstacks, scenes (dialogs, bottom sheets, list-detail, two-pane, supporting pane), conditional navigation (such as logged-in navigation vs anonymous), returning results from flows, integration with Hilt, ViewModel, Kotlin, and view interoperability.

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

Google and Meta paid media account structure evaluation. Audits campaign/ad set architecture against conversion volume minimums, budget thresholds, and targeting overlap. Identifies over-segmentation, under-segmentation, budget fragmentation, and structural anti-patterns blocking algorithmic learning. Provides consolidation roadmaps with migration plans. Use when inheriting accounts, quarterly health checks, or before scaling budgets.

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.

Expert patterns for Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance best practices. Use when "segment, analytics.js, customer data platform, cdp, tracking plan, event tracking, identify track page, data routing, segment, cdp, analytics, tracking, data-pipeline, customer-data" mentioned.

Detect PowerShell Empire framework artifacts in Windows event logs by identifying Base64 encoded launcher patterns, default user agents, staging URL structures, stager IOCs, and known Empire module signatures in Script Block Logging events.

Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective". Proactively suggest at the end of a work week or sprint. (gstack)

React and Next.js performance optimization patterns adapted from Vercel Engineering's React Best Practices (https://github.com/vercel-labs/agent-skills). Organizes 70+ rules across 8 priority categories — waterfalls, bundle size, server-side, client fetching, re-render, rendering, JS micro-perf, advanced. Use when writing, reviewing, or refactoring React/Next.js code for performance.

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.

Runs time series machine learning with the aeon library — classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search via scikit-learn compatible APIs. Use when working with temporal data, sequential patterns, or time-indexed observations (univariate or multivariate) that need specialized algorithms beyond standard ML approaches. Part of the AlterLab Academic Skills suite.

Flutter cross-platform development guide covering widget patterns, Riverpod/Bloc state management, GoRouter navigation, performance optimization, and platform-specific implementations. Includes const optimization, responsive layouts, testing strategies, and DevTools profiling. Use when: building Flutter apps, implementing state management (Riverpod/Bloc), setting up GoRouter navigation, creating custom widgets, optimizing performance, writing widget tests, cross-platform development.

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.

Composite skill — full security pass across secrets, dependencies, code paths, and OWASP risks. Chains security-audit (broad) + socket-audit (npm supply chain) + semgrep (pattern scan) + code-security (code review for vulns) in parallel, reconciles into one severity-ranked report with remediation plan. Use quarterly per active repo or before any release of security-sensitive code.

Use BEFORE generating, refactoring, reviewing, or debugging code. Trigger phrases include "write a function/script/class for X", "review this code/diff/PR", "refactor this", "debug this error", "is this implementation correct", "what's wrong with this code", "improve this code", "translate from X to Y", or any prompt with a code block the user wants you to act on. Also fires when planning architectural changes, picking algorithms or data structures, or evaluating dependency upgrades. Calls the code MCP tool to retrieve an engineering scaffold (failure pattern, procedure, correct-pattern example, verification step) before generating. Catches hallucinated APIs, lost edge cases, premature algorithm commitment, silent contract violations, refactors that change behavior masked by passing tests. Do NOT trigger for pure code reading with no action requested, simple syntax questions, file...

This skill should be used when the user asks about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state files", "read YAML frontmatter", "per-project plugin settings", or wants to make plugin behavior configurable. Documents the .claude/plugin-name.local.md pattern for storing plugin-specific configuration with YAML frontmatter and markdown content.

Expert knowledge for Azure Artifacts development including best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, views/promotion, retention, GitHub Actions CI/CD, or npm/.npmrc config, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines).

Make any repo AI-ready — analyzes your codebase and generates AGENTS.md, copilot-instructions.md, CI workflows, issue templates, and more. Mines your PR review patterns and creates files customized to your stack. USE THIS SKILL when the user asks to "make this repo ai-ready", "set up AI config", or "prepare this repo for AI contributions".

Capture actionable learnings that emerge during conversation — when the agent or user discovers that a skill, a set of skills, or a process needs to be updated based on what's happening in the current chat. Sub-skill of the learn-from orchestrator. Load when the user says "we should update the skill for this", "this should be a skill rule", "add this as a gotcha", "the skill should know about this", "update the process for this", "remember this for next time", "this is important for the skill". Also triggers when the agent notices a skill's guidance was wrong or incomplete, a process step failed or was unnecessary, a new pattern emerged, a guardrail was missing, a workaround became a pattern, or a debugging session reveals a gap.

Exploit path traversal and local/remote file inclusion (LFI/RFI) via URL parameters, cookies, and hidden fields using ../ sequences, URL encoding (%2e%2e%2f), double encoding (%252e%252e%255c), Unicode bypasses (..%c0%af), and Windows UNC paths. PHP include/require with $_GET/$_POST/$_COOKIE pattern. Target /etc/passwd, boot.ini, web.config. Tools: DotDotPwn, WFuzz, Burp Suite, ZAP.

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

You are an expert in @react-three/drei, the companion library for React Three Fiber that provides 100+ ready-made components and hooks for common 3D patterns. You help developers add orbit controls, environment maps, text rendering, HTML overlays, loaders, performance utilities, shaders, and abstractions — eliminating boilerplate and letting teams focus on creative work instead of Three.js plumbing.

Run a bounded cross-platform landscape scan before planning or implementation. Use when the real job is researching what exists, how people work around it, which solutions repeat, or how platform/tooling patterns map before deciding what to build. Produce reusable `.survey/{slug}/` artifacts, validate the artifact contract, and route planning or execution outward only after the survey is done.

Expert knowledge for Azure Osconfig development including troubleshooting, security, configuration, and integrations & coding patterns. Use when running OSConfig via IoT for commands/networking, SSH Posture Control, agent health, or Windows security baselines, and other Azure Osconfig related development tasks. Not for Azure Update Manager (use azure-update-manager), Azure Automation (use azure-automation), Azure Policy (use azure-policy).

Designs and implements backend systems including REST APIs, microservices, database architectures, authentication flows, and security hardening. Use when the user asks to "design REST APIs", "optimize database queries", "implement authentication", "build microservices", "review backend code", "set up GraphQL", "handle database migrations", or "load test APIs". Covers Node.js/Express/Fastify development, PostgreSQL optimization, API security, and backend architecture patterns.

SKILL.md is an open specification released by Anthropic in 2025 that standardizes how tools discover and auto-invoke procedural knowledge (skills, patterns, guides, rules). Instead of each tool (Claude, Cursor, VSCode, CLI) maintaining isolated skill registries, SKILL.md enables a single skill to be published once and auto-invoked across any tool that speaks the spec.

Optimize prompts via process goals (controllable behavioral instructions) rather than outcome goals (sparse end-result demands). Grounded in sports psychology meta-analysis showing process goals (d=1.36) vastly outperform outcome goals (d=0.09). Use when designing prompts, optimizing LLM steering, implementing CoT/decomposition patterns, or building automatic prompt optimization pipelines. Instantiates surrogate loss paradigm for discrete prompt space.

Serverless computing patterns. AWS Lambda, Azure Functions, GCP Cloud Functions. Deployment frameworks: Serverless Framework, AWS SAM, SST. Cold starts, event sources, and production optimization. USE WHEN: user mentions "serverless", "Lambda", "cloud function", "Azure Functions", "SAM", "SST", "Serverless Framework", "cold start", "FaaS" DO NOT USE FOR: container orchestration - use `kubernetes` or `docker`; always-on servers - use backend framework skills

Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.

This skill should be used when working with BMAD (BMad-CORE) v6-alpha projects. BMAD is a universal human-AI collaboration platform with specialized modules for software development (BMM), agent building (BMB), creative intelligence (CIS), and project management (BMD). Use this skill to understand agent workflows, command patterns, scale-adaptive methodology, and effective utilization of the four-phase development system.