Results for “mem”

Node.js performance profiling with V8 CPU profiler, heap analysis, and perf_hooks. Use for identifying bottlenecks and memory leaks. USE WHEN: user mentions "Node.js performance", "profiling", "memory leak", asks about "V8 profiler", "heap snapshot", "CPU profile", "perf_hooks", "event loop lag", "Node.js optimization" DO NOT USE FOR: Java/Python profiling - use respective skills instead

Orchestrates multi-agent workflows defined in YAML piece files, delegating tasks to team members and managing state transitions.

Local RAG indexing with 97% storage reduction via anchor-based lazy recomputation. Graph-based selective embedding storage for memory-efficient semantic code search.

Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.

Redis in-memory data store. Covers data structures, caching, and pub/sub. Use for caching and real-time features. USE WHEN: user mentions "redis", "caching", "session storage", "rate limiting", "pub/sub", "sorted sets", "in-memory database", "cache invalidation" DO NOT USE FOR: relational data - use `postgresql` or `mysql` instead, document storage - use `mongodb` instead, full-text search - use `elasticsearch` instead

Brainstorm five unique, memorable product names with rationale aligned to brand values and target audience.

`task-agent`/`review-agent`: use when CPU, memory, I/O, database, network, rendering, or cost needs measured bottleneck evidence; skip without a profiling need.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Converting repository changes into detailed learning documents. Use when turning diffs into teaching materials, recording design decisions, or creating onboarding materials for new members.

Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.

Reads, searches, and sends Telegram messages via a CLI, including inbox summaries, chat history, contacts, and group member lookups.

Create a practical onboarding plan for a new team member with outcomes, first-week structure, stakeholder introductions, learning path, and manager check-ins.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Foundational C programming patterns covering type system, memory management, pointers, arrays, preprocessor, compilation, concurrency (pthreads, mutexes, atomics), serialization (binary, JSON, struct packing), and testing (Unity, CMocka, Check). Complete 8/8 pillar coverage. Use when writing C code, understanding manual memory management, working with system-level programming, or needing guidance on which specialized C skill to use. This is the entry point for C development.

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

Watches conversations continuously and compresses them into prioritized notes, consolidating and recovering missed sessions with multiple redundancy layers.

Reads, searches, and sends Telegram messages via a CLI, including inbox checks, chat history, contacts, and group member lookups.

Reads, searches, and sends Telegram messages via a CLI, including inbox summaries, chat history, contacts, and group member lookups.

Reads, searches, and sends Telegram messages via a CLI, including inbox summaries, chat history, contacts, and group member lookups.

Manages Google Chat spaces, messages, members, and custom emojis via the gws CLI.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Transforms lessons learned into domain-organized memory instructions (global or workspace). Syntax: `/remember [>domain [scope]] lesson clue` where scope is `global` (default), `user`, `workspace`, or `ws`.

`analysis-agent`/`task-agent`/`review-agent`: use when latency, throughput, bundle, memory, CPU, query, rendering, or resource cost needs a budget; skip without performance risk.

Write React hooks with clean effect boundaries and measured memoization. Use when working with `useEffect`, custom hooks, refs, transitions, or hook dependency problems in React.

Identify render hotspots, isolate expensive updates, and apply targeted React optimizations without changing UI behavior.

Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.

Install and manage Python packages using uv pip when imports fail or dependencies are missing.

Erstellt ein internes Gutachten-Gerüst für studentische Rechtsberatungsstellen, das Sachverhalt, Normen, Beweise und Anträge strukturiert und Recherchelücken markiert.

Answers questions using a structured wiki knowledge base, returning cited synthesis with wikilinks, counter-arguments, and knowledge gaps.

Maintains a PARA-structured knowledge base on disk for persistent project context, decisions, and architecture records across agent sessions.

Reads, searches, and sends Telegram messages via a CLI, including inbox summaries, chat history, contacts, and group member lookups.

Computação paralela/distribuída. Escale pandas/NumPy além da memória disponível, DataFrames/Arrays paralelos, processamento multi-arquivo, grafos de tarefas, para datasets maiores que RAM e workflows paralelos.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.