Results for “memo”

Profile a target (script, process, GPU, memory, interconnect) using external tools and code instrumentation. Produces structured performance reports with actionable recommendations. Use when user says "profile", "benchmark", "bottleneck", or wants performance analysis.

Builds, optimizes, and debugs cross-platform mobile applications with React Native and Expo, covering navigation, native modules, list performance, and platform-specific code.

Generates structured public policy briefs analyzing legislation across economic, social, legal, and implementation dimensions. Use when drafting legislative impact analyses, policy summaries, regulatory briefs, or government affairs memoranda for lawmakers, lobbyists, or civic organizations.

Summarizes WeChat group chat highlights into a structured digest using the local wx-cli binary, with per-group history, user profiles, and privacy guardrails.

Apply Miller's Law and chunking strategies to group information into meaningful units, making interfaces easier to scan, understand, and recall.

Reviews CUDA, OpenMP, and OpenCL code for parallel correctness, covering data races, memory model violations, synchronization bugs, numerical precision issues, and API-specific pitfalls.

Proactive self-monitoring of infrastructure, services, and health. Tracks disk/memory/load, service health, cron job status, recent errors. Auto-fixes safe issues. Triggers on: health check, heartbeat, monitor status, service status, infrastructure check.

Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.

Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.

Manage Apple Notes via the `memo` CLI on macOS (create, view, edit, delete, search, move, and export notes). Use when a user asks to add a note, list notes, search notes, or manage note folders.

Identify, collect, and analyze ransomware attack artifacts to determine the variant, initial access vector, encryption scope, and recovery options.

Manage Apple Notes via the `memo` CLI on macOS (create, view, edit, delete, search, move, and export notes). Use when a user asks SolanaOS to add a note, list notes, search notes, or manage note folders.

Record durable project decisions with rationale, alternatives, assumptions, status, and revisit triggers. Load when the user says record this decision, we decided, decision log, why did we choose, revisit this later, or capture architectural/process rationale.

Manage Apple Notes via the `memo` CLI on macOS (create, view, edit, delete, search, move, and export notes). Use when a user asks Clawdbot to add a note, list notes, search notes, or manage note folders.

Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples. Use when the user runs /si:extract or asks to package a recurring solution from memory into a skill.

Manage Apple Notes via the `memo` CLI on macOS (create, view, edit, delete, search, move, and export notes). Use when a user asks OpenClaw to add a note, list notes, search notes, or manage note folders.

Validates agent configuration files for Skills, Hooks, MCP, Memory, and Plugins across Claude Code, Cursor, GitHub Copilot, and Codex CLI against 230 rules, with optional auto-fixing.

Complete WhatsApp management for OpenClaw agents: per-conversation memory (groups + DMs), unanswered message tracking, loop prevention, and multi-PA coordination. Use when: tracking conversation context, recalling past decisions, finding unanswered messages, or preventing echo/duplicate message loops.

Sovereign-grade safety OS for AI coding agents. 63 hooks, 2,025 skills, L1 memory, circuit breakers, and cross-engine enforcement — blocks rm -rf, force push, pipe-to-shell, and 40+ attack vectors before they reach your repo.

Use when converting completed work into durable knowledge. This skill provides a systematic process for extracting lessons learned, patterns, and insights from completed work and storing them in memory systems (PARA, Gigabrain, OpenStinger) for future reuse.

Write correct, efficient Kusto Query Language queries with coverage of syntax, joins, dynamic types, datetime pitfalls, regex, serialization, memory management, and advanced functions.

Extract Kerberos tickets from LSASS memory, inject them into an attacker session, and perform lateral movement to access remote systems as the impersonated user.

Writes, reviews, and debugs idiomatic Rust code with memory safety and zero-cost abstractions, covering ownership patterns, lifetimes, trait hierarchies, async/await with tokio, and error handling with Result/Option.

Power management and performance optimization for Zephyr RTOS. Covers system power states (Idle, Suspend, Off), device-level power management, residency hooks, and code/data relocation for speed efficiency. Trigger when optimizing battery life, reducing latency, or managing memory constraints.

Analyzes bootkit and rootkit malware that infects MBR, VBR, or UEFI firmware for pre-OS persistence, covering boot sector analysis, UEFI module inspection, and anti-rootkit detection.

Execute advanced evasion by injecting malicious code into the memory space of a legitimate, suspended process (Process Hollowing). This skill details techniques to bypass static and dynamic analysis by masking malicious activity behind trusted processes like svchost.exe or explorer.exe.

Analyzes Perfetto traces to find the root cause of latency, memory, or jank issues in Android apps. Use when the user provides a Perfetto trace file and asks any question, ongoing investigation, or open-ended request to analyze its contents.

Runs before every git commit on heleni-memory. Checks: no credentials in tracked files, no oversized files, valid SKILL.md frontmatter for any new/modified skill. Triggers: "commit", "before push", "validate repo", or auto-runs as a git pre-commit hook.

Guide for the first days with a new human. Use when: the `init` memory block exists (pointing you here), you have no persona/communication blocks yet, or a human explicitly asks about setup. This skill is about having good conversations — not filling out forms.

Create and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and fundraising materials. Use when the user needs investor-facing documents, projections, use-of-funds tables, milestone plans, or materials that must stay internally consistent across multiple fundraising assets.

Autonomously runs iterative experiment loops to optimize code against a measurable metric. Use when the user wants to improve execution time, memory usage, test pass rate, or any numeric performance goal across repeated experiments — NOT for one-shot bug fixes or simple code review.

Decide where to save any piece of information — monday.com, local file, daily notes, or MEMORY.md. Use this skill before saving anything to ensure the right destination. Prevents local clutter and ensures the owner can access all relevant content in monday.com.

Use before starting any new work to check for existing knowledge. This skill provides a systematic search and retrieval process for finding relevant knowledge from memory systems (PARA, Gigabrain, OpenStinger, existing skills) before starting work, preventing redundant effort and leveraging prior learning.

Integrates Mem0 Platform SDK for persistent memory in AI applications. Use when building agents or chatbots that need to remember user preferences, past interactions, or personalized context across sessions. Covers Python and TypeScript SDKs, plus LangChain, CrewAI, OpenAI Agents, LlamaIndex, AutoGen, and LangGraph integrations.

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.