性能
性能分析与优化
claw-compactor
aeromomo
Claw Compactor — 6-layer token compression skill for OpenClaw agents. Cuts workspace token spend by 50–97% using deterministic rule-engines plus Engram: a real-time, LLM-driven Observational Memory system. Run at session start for automatic savings reporting.
memories-mcp
webrenew
"MCP server integration for memories.sh — the persistent memory layer for AI agents. Use when: (1) Configuring the memories.sh MCP server for any client (Claude Code, Cursor, Windsurf, VS Code, v0, Claude Desktop, OpenCode, Factory), (2) Using MCP tools to store, search, retrieve memories, or manage reminder schedules, (3) Understanding get_context vs search_memories vs list_memories, (4) Working with streaming memory tools for SSE content, (5) Troubleshooting MCP connection issues, (6) Choosing between cloud MCP (HTTP) and local MCP (stdio) transports."
swift-concurrency
Joannis
'Expert guidance on Swift Concurrency best practices, patterns, and implementation. Use when developers mention: (1) Swift Concurrency, async/await, actors, or tasks, (2) modern concurrency patterns or structured concurrency, (3) migrating to Swift 6 or strict concurrency checking, (4) data races or thread safety issues, (5) refactoring closures to async/await, (6) @MainActor, Sendable, or actor isolation, (7) concurrent code architecture or performance, (8) concurrency-related linter warnings, (9) AsyncSequence, AsyncStream, or task groups, (10) nonisolated, @preconcurrency, or @unchecked Sendable.'
letta-development-guide
letta-ai
Comprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.
go-google-best-practices
metalagman
Expertise in Go programming according to the Google Go Best Practices. Focuses on actionable advice for naming, error handling, performance, testing, and general idiomatic Go to ensure high-quality, maintainable, and efficient codebases.
debate-workflow
rysweet
Structured multi-perspective debate for important architectural decisions and complex trade-offs
model-evaluation-benchmark
rysweet
Automated reproduction of comprehensive model evaluation benchmarks following the Benchmark Suite V3. Auto-activates for model benchmarking, comparison evaluation, or performance testing between AI models.
engineer-analyst
rysweet
Analyzes technical systems and problems through engineering lens using first principles, systems thinking, design methodologies, and optimization frameworks. Provides insights on feasibility, performance, reliability, scalability, and trade-offs. Use when: System design, technical feasibility, optimization, failure analysis, performance issues. Evaluates: Requirements, constraints, trade-offs, efficiency, robustness, maintainability.
computer-scientist-analyst
rysweet
Analyzes events through computer science lens using computational complexity, algorithms, data structures, systems architecture, information theory, and software engineering principles to evaluate feasibility, scalability, security. Provides insights on algorithmic efficiency, system design, computational limits, data management, and technical trade-offs. Use when: Technology evaluation, system architecture, algorithm design, scalability analysis, security assessment. Evaluates: Computational complexity, algorithmic efficiency, system architecture, scalability, data integrity, security.
n-version-workflow
rysweet
N-version programming for critical implementations - generates N independent solutions and selects the best through comparison
scrum-sage
michalparkola
AI-powered Scrum Master and Enterprise Agility Coach based on Jeff Sutherland, Taiichi Ohno, and First Principles thinking. Use when user needs help with Scrum, sprint analysis, backlog refinement, scaling advice, impediment removal, team dynamics, or agile coaching. Activate for questions about Scrum mechanics, Scrum@Scale, flow optimization, or team performance.
HumpDay code-review skill
microprediction
Repository: <https://github.com/microprediction/humpday>
performance-profiling
HeshamFS
Identify computational bottlenecks, analyze scaling behavior, estimate memory requirements, and receive optimization recommendations for any computational simulation. Use when simulations are slow, investigating parallel efficiency, planning resource allocation, or seeking performance improvements through timing analysis, scaling studies, memory profiling, or bottleneck detection.
swiftui-patterns
ahmed3elshaer
SwiftUI UI patterns, state management, navigation, animations, and performance optimization.
linkedin-profile-optimizer
BrianRWagner
Audit and rewrite your LinkedIn profile to attract the right people. Scores each section, rewrites headline and about copy, and includes an AI visibility checklist so you show up in ChatGPT, Perplexity, and Claude search. Use when someone says "optimize my LinkedIn," "LinkedIn profile help," "rewrite my about section," or "how do I show up in AI search."
echovault
mraza007
Local-first memory for coding agents. You MUST retrieve memories at session start and save memories before session end. This is not optional.
optimizing-query-by-id
AltimateAI
Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.
optimizing-query-text
AltimateAI
Optimizes Snowflake SQL query performance from provided query text. Use when optimizing Snowflake SQL for: (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions "slow query", "make faster", "improve performance", "optimize SQL", or "query tuning" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up
mcp-memory-gateway
IgorGanapolsky
The Agentic Feedback Studio & Veto Layer. Persistent agent memory, high-density context packs, and Agentic Guardrails (V2V) for Claude Code, Codex, and Gemini.
performance-check
Terryc21
'Automated performance anti-pattern scan for iOS/macOS apps. Covers memory, CPU, energy, SwiftUI, launch time, and database. Triggers: "performance check", "performance scan", "check performance".'
customer-workspace
aktsmm
Customer workspace initialization skill. Provides inbox (information accumulation), meeting minutes management, and auto-classification rules. Use for "setup customer workspace" or "add inbox feature" requests.
agent-orchestration-improve-agent
boisenoise
"Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration."
k8s-cost
rohitg00
Optimize Kubernetes costs through resource right-sizing, unused resource detection, and cluster efficiency analysis. Use for cost optimization, resource analysis, and capacity planning.
Memory Faculty — Cognition
acnlabs
No memories found â Don't fabricate memories. Say honestly: "I don't have a specific memory about that."