parallel-execution
Execute multiple independent tasks simultaneously using parallel agent coordination to maximize throughput and minimize execution time. Use when tasks have no dependencies, results can be aggregated, and agents...
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Execute multiple independent tasks simultaneously using parallel agent coordination to maximize throughput and minimize execution time. Use when tasks have no dependencies, results can be aggregated, and agents...
Use and troubleshoot the Memory MCP server for episodic memory retrieval and pattern analysis. Use this skill when working with MCP server tools (query_memory, analyze_patterns, advanced_pattern_analysis), vali...
Dynamically validate codebase compliance with architectural decisions and constraints
Write high-quality Rust unit tests following best practices. Use when writing new tests, reviewing test code, or improving test quality. Emphasizes clear naming, AAA pattern, isolation, and deployment confidenc...
Systematic approach to implementing new features in Rust memory systems
Invoke for complex multi-step tasks requiring intelligent planning and multi-agent coordination. Use when tasks need decomposition, dependency mapping, parallel/sequential/swarm/iterative execution strategies,...
Systematic debugging approach for Rust async code with Tokio, Turso, and redb. Use when diagnosing runtime issues, performance problems, async deadlocks, database connection issues, or panics.
Build Rust code with proper error handling and optimization for development, testing, and production. Use when compiling the self-learning memory project or troubleshooting build errors.
Execute and troubleshoot memory-cli commands for episode management, pattern analysis, and storage operations. Use this skill when running CLI commands, debugging CLI issues, explaining command usage, or guidin...
Analyze gaps between implementation plans and actual codebase implementation for the Rust self-learning memory project
Log execution steps during episode execution for detailed tracking and learning. Use when recording tool usage, decision points, errors, or milestones during task execution.
General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. Use when you need to perform comprehensive searches across codebase, find files that are not obvious...
Diagnose, fix, and optimize GitHub Actions workflows for Rust projects. Use when setting up CI/CD, troubleshooting workflow failures, optimizing build times with caching, or ensuring best practices for testing,...
Complex research requiring deeper analysis, multi-step reasoning, and sophisticated source evaluation for technical, academic, or specialized domain queries needing expert-level analysis, high-stakes decisions,...
STRICT GitHub release gatekeeper. Blocks premature releases (from develop, incomplete CI). Verifies PR merged to main + ALL CI passed before allowing tag/release. Triggers on "release", "tag", "publish", "deplo...
Break down complex tasks into atomic, actionable goals with clear dependencies and success criteria. Use when planning multi-step projects, coordinating agents, or decomposing complex requests.
Systematic approach to diagnosing and fixing failing tests in Rust projects. Use when tests fail and you need to diagnose root causes, fix async/await issues, handle race conditions, or resolve database connect...
Comprehensive async/tokio testing patterns for episodic memory operations. Use when writing tests for async functions, time-based operations, concurrent tasks, or tokio runtime management.
Synchronize memories between Turso (durable) and redb (cache) storage layers. Use when cache appears stale, after failures, or during periodic maintenance.
Maintain high code quality through formatting, linting, and static analysis using rustfmt, clippy, and cargo audit. Use to ensure consistent code style and catch common mistakes.
Systematic approach to implementing new features in the Rust memory system following project conventions. Use when adding new functionality with proper testing and documentation.
Complete and score a learning episode to extract patterns and update heuristics. Use when finalizing a task to enable pattern extraction and future learning.
Retrieve relevant episodic context from memory for informed decision-making. Use when you need past episodes, patterns, or solutions to similar tasks.
Find and document file locations in the codebase. Use this when you need to locate implementation files, tests, configurations, or any code artifacts by feature or topic. Works with Grep, Glob, and LS tools.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.