Results for “memo”

Build, update, and query a persistent project knowledge graph from skills, memory, docs, and code structure — stdlib Python only, no external tools. Dual-mode: skill-library (agent-loom) or application (any consumer repo). Load when the user asks for a knowledge graph, project map, skill relationships, query the graph, update the graph, or trace how components connect. Auto-runs on memory-handoff and project-setup bootstrap. Also triggers on "build the graph", "what connects to X", "map this project".

Diagnose and optimize slow-loading, high-memory, or low-FPS USD scenes using a structured workflow with profiling, validation, and mutation phases.

Diagnose and fix SwiftUI performance issues like slow rendering, janky scrolling, and high CPU/memory usage through code review and profiling guidance.

Apply the Von Restorff Effect to make the most important element visually distinct from its surroundings, improving attention and recall.

Quantize large language models to 4-bit precision using activation-aware weight quantization, reducing memory footprint and speeding up inference with minimal accuracy loss.

Process in-memory tabular data with a fast, expression-based DataFrame library that supports lazy evaluation, parallel execution, and Apache Arrow semantics.

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:review or asks what has been learned and what should be promoted or pruned.

Everything Claude Code (ECC) — agent harness configuration layer for Claude Code and other AI coding IDEs. Augments agents with skills, hooks, persistent memory, model routing, and quality gates without modifying the underlying model.

CLI interface for igf (Grapefruit) dynamic instrumentation server. Use to enumerate Frida devices, list apps, run hooks, query logs, access device file systems, inspect classes, dump memory, and perform mobile app security analysis.

Use when context window is filling up, sessions are getting slow, or you need to preserve state across sessions. For context compression, state snapshots, search-first discipline, and cross-session memory management.

Build the smallest, highest-signal context package for an AI coding task — goal, constraints, repo facts, boundaries, and a verification plan. Load when prompts are underspecified, the agent is missing key files or decisions, the user says "use the right context", "here's the repo", or when work is drifting due to missing constraints. Also triggers on "context engineering", "gather context", "what do you need from me", "before you start". Not for cross-session continuity (use memory-startup/memory-recall).

Perform coverage-guided fuzzing of compiled binaries using AFL++ to discover memory corruption, crashes, and security vulnerabilities.

Hunt for Mimikatz execution using command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

Adopts the voice and methodology of comedian Mike Myers to create memorable characters, catchphrases, and loving parodies for brand voices and creative writing.

Evaluates the agent's own work, catches mistakes, and improves permanently through self-reflection, self-criticism, and learning from corrections.

KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph).

Review L2 session facts and promote valuable ones to L1 Atomic Memory. Use at end of session or when L2 accumulates facts worth keeping permanently. Runs add-fact.sh for each promoted fact.

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.

Persists project context across Claude Code sessions. Auto-loads project summaries on startup, tracks session progress, and saves structured state via deterministic Node.js scripts.

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

Orchestrates Gemini 3 Flash to audit Linux Kernel patches for memory safety, detect cost drifts in Terraform plans, and generate hardened Kubernetes security contexts.

Scales pandas and NumPy workflows to datasets larger than memory using parallel and distributed computing, with support for dataframes, arrays, bags, and custom task graphs.

Diagnoses Linux server issues across CPU, memory, disk, I/O, and network, and manages systemd services, SSH, and Fail2Ban on Ubuntu, Debian, and AlmaLinux.

Defense-in-depth security across Rust, TypeScript, and Bash for the Pump SDK — cryptographic key handling, memory zeroization, secure file I/O, input validation, privilege management, dependency auditing, and a 60+ item security checklist.

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.

Diagnose whether an AI workflow suffers from context stuffing or benefits from context engineering, and apply structured techniques to improve reliability.

Provides structured guidance for building production-grade AI agent systems through context engineering, covering fundamentals, architectural patterns, operational excellence, and evaluation.

Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.

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

Compresses conversation transcripts into prioritized notes using an LLM observer, consolidates them when they grow, and recovers any missed sessions without a database or vector store.

Audits a codebase for scalability bottlenecks, flagging N+1 queries, missing indexes, unbounded result sets, blocking operations, and memory pressure, then outputs a prioritized fix list.

Fine-tune large language models by training less than 1% of parameters using LoRA, QLoRA, and 25+ adapter methods, enabling efficient adaptation on limited GPU memory.

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

Lints agent configuration files for Skills, Hooks, MCP, Memory, and Plugins across 10+ AI tools, flagging issues against 231 validation rules and optionally auto-fixing them.

Designs collaborative AI agent teams using the CrewAI framework, covering agent roles, task decomposition, crew orchestration, process types, memory systems, and event-driven flows.