2.4K skills · sorted by relevance
Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.
Use OpenViking through OpenClaw to manage long-term memories, search archived conversations, and find imported resources or Agent Skills. Inspect recall traces to understand search results and recover full tool outputs stored outside the conversation.
Collect evidence from a compromised computer before shutdown or remediation erases it. Preserve memory, network connections, processes, and system state, then record integrity hashes and handling history to support incident investigation and evidence tracking.
Investigate Linux kernel rootkits, malware that hides inside the operating system's core. Analyze memory dumps with Volatility3, compare system views, and run live scans to identify hidden modules, altered calls, and suspicious inconsistencies.
Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.
Audit instructions and memory files loaded on every agent turn. Identify repeated rules, contradictions, outdated content, and material better moved into skills, then receive evidence-backed recommendations ranked by estimated token savings and change risk.
Set up a task list, working memory files, and a dashboard for a productivity system. Use existing todos to clarify nicknames, acronyms, and project codenames, then organize that context into reusable people, terminology, and project notes.
Explicitly save debugging discoveries, project conventions, and working preferences to Claude's persistent project memory. The skill checks for duplicate notes, keeps entries concise, and suggests moving enforceable rules into CLAUDE.md when appropriate.
Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.
Keeps work preferences, corrections, and ongoing goals in a personal Kortix folder so agents can refer to them across sessions. Users can inspect, edit, or delete the saved Markdown files and request specific details be forgotten.
Guide file storage and context retention in Kortix shared folders. Identify accessible folders, save lasting outputs, keep user and team notes across sessions, and handle conflicting file copies according to the documented sharing and synchronization rules.
Inspect Mac performance to find processes using excessive CPU, GPU, memory, or energy. Combine measurements with explanations of known system processes to diagnose slowness, heat, and startup load, then recommend actions without changing system settings.
Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.
Use Memori through MCP, a protocol connecting agents to tools, to retrieve past decisions and preferences, summarize ongoing work, and resume after conversation compression. It also guides selective long-term memory storage and usage limit checks.
Turn OpenClaw conversations, actions, and tool history into structured long-term memory. Agents can retrieve past facts, decisions, and summaries when needed, helping them continue work across sessions and recover context after it is shortened.
Install or remove memU long-term memory for the agent in use. The skill selects the host-specific guide, configures scheduled memory recording and retrieval before answers, and verifies the setup with local or cloud storage.
Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.
Optimize transformer attention, the calculation that relates tokens to one another, using Flash Attention. The skill guides PyTorch integration, GPU memory reduction, performance measurement, and output comparisons for training or inference with long sequences.
Estimate inference memory for models on Hugging Face Hub without downloading their weights. Check GPU capacity for Safetensors or GGUF files, with optional estimates for the KV cache, memory used to retain attention data during generation.
Set up a task list, workplace memory files, and a dashboard. Use existing todos to clarify nicknames, abbreviations, and project names, then save that context for future conversations with the agent.
Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.
Test whether an AI agent stores hostile instructions in persistent memory and follows them later. The skill uses marked test instructions and follow-up dialogue to document storage confirmations or changes in subsequent responses.
Investigate slow Apple apps, memory growth, launch delays, and battery drain using Instruments and Xcode diagnostics. Choose measurements for each symptom, identify the responsible code, and compare results before and after targeted fixes.
Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.