Results for “memory-corruption”

Analyze core dumps, debug live processes, and investigate crashes, deadlocks, or memory issues in C/C++ programs using GDB with intelligent source code correlation.

Compress bloated project or global memory while preserving decisions, rationale, revisit triggers, provenance, and active user preferences. Load when memory exceeds budget, global memory is too large, session logs are repetitive, or before appending to an over-budget memory file.

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

Audit project and global memory for bloat, stale decisions, duplicates, contradictions, unsafe content, missing provenance, broken routing, and over-budget global files. Load when the user asks to audit memory, clean memory, check memory health, or verify memory quality.

Diagnoses and reduces Qdrant memory usage by analyzing resident memory, page cache, and providing optimization techniques like quantization, on-disk storage, and async_scorer.

Merges mature lessons from a domain memory file into its instruction file, preserving knowledge with minimal redundancy.

Stores, retrieves, and curates persistent facts in a structured memory map, distinguishing between user facts and reference text.

Consolidates auto-memory files in ~/.claude/projects by auditing, planning, and executing a 4-phase cleanup with user approval.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

Review governed memory proposals, policy tags, tombstones, and effective memory.

Curating cross-agent knowledge and institutional memory: extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices. Use for memory curation.

Analyzes prompt injection, retrieval poisoning, memory contamination, planner drift, and tool-boundary abuse in agentic systems, mapping trust boundaries and proving exploit chains.

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials.

Persists and recalls agent knowledge across sessions using four approaches: static KB, company wiki, persistent memory, and session brain.

clawhip × filesystem-offloaded memory

Memory (acaf-memory)

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users drop off", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", "user activation", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users stop after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.

Master memory forensics techniques including memory acquisition, process analysis, and artifact extraction using Volatility and related tools. Use when analyzing memory dumps, investigating incidents, or performing malware analysis from RAM captures.

Delete, redact, or archive project and global memories when the user says forget this, delete memory, remove that preference, do not remember, redact sensitive information, or retire stale memory.

Reclaim DRAM on NVIDIA Jetson devices by disabling unused display, camera, and DMA subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB layers for headless or no-camera deployments.

Reduces peak GPU memory in Megatron Bridge training by applying expandable segments, parallelism resizing, activation recompute, and CPU offloading constraints.

记忆解构 (aeaf-gaze)

Composite skill — query, capture, improve, and persist knowledge in one workflow. Chains recall (RAG query) → sync-memories (write durable note) → rag-curate (improve weak retrievals) → handoff (durable snapshot if session-ending). Use when the work involves "what did we decide", "remember this", "save where we are", or any closing checkpoint.

Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.

Diagnose and fix retention problems using the Fogg Behavior Model (B=MAP), covering motivation, ability, prompts, and tiny habits.

Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.

Persistent cross-session memory management. Enables agents to remember user preferences, project conventions, and past decisions across different sessions using a structured MEMORY.md index and topic files.

Scout research skill — Idea Memory

Analyze RAM dumps with Volatility 3 to detect malware, process injection, network connections, and credential theft during incident response.

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.

Structured memory system with domains, decay, and links for AI agents, replacing flat memory files with a weighted, interconnected memory palace.

Save information to persistent memory for cross-session recall. Stores preferences, conventions, decisions, and context.