Choose Rust design patterns for RTK modules that process command output. Use concrete examples to structure configuration, prevent type mixups, manage parser states, and select abstractions suited to the task without adding unnecessary architectural complexity.
- Planning the structure of a new command output filter
- Preventing mixups between values with the same underlying type
- Organizing configuration with many optional fields
rtk-ai/rtk☆ 84.2KUpdated Oct 21, 2026
Prepare market history for pattern detection and comparison. Import opening, high, low, and closing prices plus volume, validate and normalize the values, then store them by symbol and date for later retrieval. It does not place orders.
- Loading price and volume history for a symbol
- Preparing market data for pattern detection
- Normalizing data before comparing price movements
ruvnet/ruflo☆ 78.2KUpdated Oct 21, 2026
Define reusable reasoning and decision patterns for agents, embed them in executable workflows, and review their performance. Share successful approaches, including conditional decision trees, stepwise problem solving, analogies, and evaluation from multiple perspectives.
- Define conditional rules for agent decisions
- Turn problem-solving steps into executable workflows
- Solve new problems using similar past cases
ruvnet/ruflo☆ 78.2KUpdated Oct 21, 2026
Review supplied writing through twelve DHDNA dimensions, a framework for describing reasoning and expression. Receive observations supported by quotations, optional scores, and comparisons between samples, with clear limits separating textual patterns from psychological traits.
- Reflect on reasoning patterns in your writing
- Compare argumentation across selected texts
- Review how a text expresses assumptions and uncertainty
K-Dense-AI/scientific-agent-skills☆ 53.6KUpdated Oct 19, 2026
Analyze compiled programs without running them by recognizing branches, loops, data structures, and common instruction patterns. Use disassembly and decompilation, which translate machine code into readable representations, to reconstruct program logic and document findings.
- Understand an unfamiliar executable
- Reconstruct branches and loops from assembly
- Infer data structures and function parameters
wshobson/agents☆ 41.5KUpdated Oct 20, 2026
Refactor JavaScript using ES6+ features to make code easier to read and maintain. Covers asynchronous operations with async/await, modules, and functional patterns that transform data without changing the original values.
- Refactoring legacy JavaScript
- Migrating callbacks to async/await
- Building data transformation pipelines
wshobson/agents☆ 41.5KUpdated Oct 20, 2026
Plan microservices by defining service responsibilities, communication methods, and data ownership. Use patterns for gradual migration, distributed transactions, and failure isolation to design systems made of independently responsible services.
- Gradually splitting a monolithic system into services
- Defining service responsibilities and interfaces
- Designing event-based communication and distributed transactions
wshobson/agents☆ 41.5KUpdated Oct 20, 2026
Analyze a codebase to document its technologies, architecture, dependencies, and implementation patterns. Produce a Markdown reference with optional diagrams, code examples, and guidance for adding features consistently with the existing design.
- Understand an unfamiliar codebase's architecture
- Document components, dependencies, and data flow
- Create guidance for adding features consistently
github/awesome-copilot☆ 40.3KUpdated Oct 21, 2026
Detect five-point price patterns using Fibonacci ratios and generate buy, sell, or stand-aside signals. The skill identifies Gartley, Bat, Butterfly, and Crab patterns and locates potential reversal zones, where a price trend may change direction.
- Detecting harmonic patterns in price data
- Identifying potential reversal zones
- Generating buy, sell, and stand-aside signals
HKUDS/Vibe-Trading☆ 36.2KUpdated Oct 21, 2026
Analyze Windows authentication logs for repeated password guesses against Remote Desktop services. Group failures by source IP and account, check for later successful logins, and produce a JSON report highlighting attack patterns and possible compromise.
- Investigate repeated login failures on exposed RDP services
- Identify accounts targeted by password guessing
- Check for successful logins after repeated failures
mukul975/Anthropic-Cybersecurity-Skills☆ 34.8KUpdated Aug 31, 2026
Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.
- Inspecting attention patterns and intermediate values
- Testing output changes by replacing internal activations
- Analyzing attention head contributions to predictions
davila7/claude-code-templates☆ 32.8KUpdated Oct 21, 2026
Gives AI agents persistent project memory for architecture, implementation patterns, and decisions. Supports saving, searching, and retrieving knowledge, automatically syncing it with project documentation, and viewing memory usage through a dashboard.
- Save architecture decisions for later reference
- Search saved implementation patterns by type or tag
- Retrieve a specific design record by key
davila7/claude-code-templates☆ 32.8KUpdated Oct 21, 2026
Design n8n automations for incoming events, API connections, database synchronization, AI agents, scheduled tasks, and batch processing. Choose a suitable structure, plan error handling, check connections, test results, and review performance when processing large numbers of items.
- Sending form submissions to Slack
- Generating and emailing scheduled reports
- Synchronizing records between databases
czlonkowski/n8n-mcp☆ 23.1KUpdated Oct 21, 2026
Helps create, explain, debug, and optimize regular expressions for finding, extracting, or replacing text. Accounts for differences between programming languages and checks matching and nonmatching examples to identify overly broad patterns and potential performance problems.
- Create patterns for extracting text
- Explain unfamiliar regular expressions
- Fix unwanted matches and missed matches
RightNow-AI/openfang☆ 18.2KUpdated Jul 2, 2026
Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.
- Inspecting attention patterns and intermediate values
- Testing output changes by replacing internal activations
- Analyzing attention head contributions to predictions
Orchestra-Research/AI-Research-SKILLs☆ 13.8KUpdated Oct 11, 2026
Use WorldMonitor’s architecture as a reference for building dashboards. Find examples of TypeScript panels, data services, grid layouts, API proxies, and refresh scheduling, with guidance on simplifying these patterns for a personal monitoring project.
- Build a panel following WorldMonitor conventions
- Study patterns for scheduled data refreshes
- Review dashboard themes and grid layouts
HKUDS/OpenSpace☆ 7.8KUpdated Aug 13, 2026
Creates custom Semgrep rules to detect vulnerabilities, bug patterns, or coding standard violations. Builds tests for unsafe and safe examples, then validates and refines each rule to check for false alarms and missed cases.
- Creating detection rules for recurring bug patterns
- Tracking untrusted input reaching dangerous operations
- Enforcing coding standards with custom rules
trailofbits/skills☆ 7.7KUpdated Oct 21, 2026
Review MSBuild project files and build definitions for correctness and maintainability. Get prioritized findings with concrete recommendations, distinguishing confirmed defects from conditional risks and valid exceptions such as F# source ordering or required imports.
- Reviewing build files for maintainability
- Checking path portability across operating systems
- Reviewing import safety and required-file assumptions
dotnet/skills☆ 5.7KUpdated Oct 21, 2026
Review academic drafts for 24 common AI writing patterns and revise awkward wording or repetitive structures. Preserve meaning, economics terminology, citations, and LaTeX commands, with a report showing the changes and their locations.
- Polishing an academic draft before submission
- Varying repetitive transitions and paragraph structures
- Editing economics papers while preserving terms and citations
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4.7KUpdated Oct 20, 2026
Edit English and Chinese prose to reduce formulaic wording, inflated claims, vague attributions, and repetitive sentence patterns. It preserves the core meaning and intended tone while improving rhythm, directness, and the writer’s voice.
- Rewriting AI-generated drafts in a natural voice
- Checking formulaic wording before publication
- Polishing academic or professional prose
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4.7KUpdated Oct 20, 2026
Refactor React components that have accumulated many boolean options. Apply component composition, meaning building interfaces from cooperating parts, and shared state patterns to design reusable component libraries and clearer APIs, with guidance specific to React 19.
- Refactoring components with too many boolean props
- Designing reusable component library APIs
- Reviewing shared state and provider responsibilities
zebbern/claude-code-guide☆ 4.7KUpdated Oct 21, 2026
Analyze reference articles and social posts to uncover structure, persuasive techniques, and writing patterns. Turn the findings into reusable templates, opening patterns, and interview questions for planning new content.
- Compare the structure of reference articles
- Extract opening patterns from social posts
- Build a reusable newsletter template
nyldn/claude-octopus☆ 4.3KUpdated Oct 21, 2026
Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.
- Inspecting attention patterns and intermediate values
- Testing output changes by replacing internal activations
- Analyzing attention head contributions to predictions
foryourhealth111-pixel/Vibe-Skills☆ 3.8KUpdated Oct 20, 2026
Analyze time series, observations ordered over time, using aeon in Python. Build classification, prediction, and clustering workflows, detect unusual patterns or changes, and compare methods through an interface compatible with scikit-learn.
- Classifying time series by their patterns
- Forecasting future values from past observations
- Detecting anomalies and change points
foryourhealth111-pixel/Vibe-Skills☆ 3.8KUpdated Oct 20, 2026