Results for “memory”

Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.

The English summary is being prepared. Showing the original description.

Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.

The English summary is being prepared. Showing the original description.

Investigate memory growth and out-of-memory failures in OpenClaw tests. Compare memory measurements and heap snapshots, records of objects held in memory, to distinguish cleanup leaks from retained test modules and verify targeted fixes.

• Investigating Vitest out-of-memory failures
• Comparing heap snapshots from the same process
• Distinguishing cleanup leaks from retained test modules

Import Claude Code auto-memory into AgentDB and search past notes by meaning across sessions and projects. The skill checks import status, consolidates near-duplicate entries, and returns search results with their memory sources identified.

• Search past notes across projects
• Import all project memories for the first time
• Consolidate near-duplicate memories

Plan how an AI application should use Hindsight memory. Identify useful information to retain, define user-scoping tags and memory bank settings, and specify how retrieved facts and synthesized knowledge should reach the model.

• Plan memory that carries conversation context across sessions
• Design tags that separate each user's memories
• Plan summaries of customer preferences and recurring issues

Store project knowledge and design decisions in persistent memory that AI agents can search and read. New findings sync automatically to Markdown documentation, while usage statistics help you inspect the memory bank.

• Save architecture decisions for later tasks
• Find previously recorded implementation patterns
• Keep agent memory synced with project documentation

Give an agent rules for consulting long-term memory before work and saving decisions, corrections, and failures afterwards. It helps carry preferences and project conventions across sessions while preserving dated history and making conflicting memories visible.

• Carry preferences and project conventions across sessions
• Record user corrections for future tasks
• Preserve the history of changed decisions

Retrieve earlier decisions, preferences, and saved resources from OpenViking across sessions. Store durable information, import documents or repositories, and manage deletion, with guidance on how project memory is shared between directories and repository clones.

• Recall decisions from earlier sessions
• Save preferences and lasting decisions
• Import files, URLs, or Git repositories for later use

Diagnose OpenViking memory problems by checking plugin installation, client settings, server connections, and recent activity. The skill helps trace missing recalled context, failed saves, and authentication errors, then guides targeted fixes and checks whether they worked.

• Investigating missing recalled context
• Checking whether conversation records were saved
• Troubleshooting 401 or 403 authentication errors

Explore unfamiliar code with Codebase Memory to map architecture, trace function calls and dependencies, and assess change impact. Findings are checked against source files, with index freshness and coverage considered before drawing conclusions.

• Understanding an unfamiliar codebase
• Tracing function callers and dependencies
• Assessing the impact of code changes

Investigate malicious code placed inside legitimate Windows processes using memory analysis, event logs, and API monitoring. Identify injection methods, examine extracted code, and produce evidence reports and detection rules for suspicious process behavior.

• Investigating unexpected connections from legitimate processes
• Finding injected code in memory dumps
• Classifying process hollowing and other injection methods

Investigate Windows malware that runs in memory or abuses legitimate system tools. Use logs, registry entries, and memory dumps to trace persistence, identify suspicious activity, and produce detection rules and an analysis report.

• Investigating suspicious PowerShell activity
• Checking WMI persistence mechanisms
• Finding malicious code in memory dumps

Integrate Flash Attention into transformer models to reduce processing time and GPU memory use for long sequences. Covers PyTorch integration, advanced attention options, and benchmarks that compare speed, memory use, and output accuracy.

• Speeding up transformer training on long sequences
• Reducing GPU memory use during attention
• Adding Flash Attention to a PyTorch model

Add persistent memory to NanoClaw agents using the default Claude provider. With mnemon installed in the container, agents retrieve relevant context before responding and save new insights after each turn, retaining memory across container restarts.

• Recalling earlier conversations in a new session
• Keeping agent memory across container restarts
• Checking memory hooks and storage settings

Trace a file, function, or line through Git history to the agent session linked to its commit. It presents recorded commit details, session summaries, and important observations to help explain the change.

• Investigating why a particular line changed
• Finding the agent session behind a function change
• Checking a file's latest commit and author

Save facts, decisions, and discoveries to agentmemory for future sessions. The skill preserves the user's wording, adds specific search tags and referenced file paths, and confirms which terms can retrieve the saved knowledge.

• Preserving a decision for future sessions
• Saving a discovery with related file paths
• Updating a previously saved fact

Use Memori Cloud from Claude Code to consult stored preferences, decisions, and project history before substantive work, then save the exchange afterward. Recall can span sessions and use filters for dates and information types.

• Check previous decisions before coding
• Recall preferences and project requirements
• Review progress after time away

Search an agent's long-term memory for people, projects, and other facts. Save important corrections immediately, update outdated entries, and remove errors or duplicates while respecting which notebooks the current conversation can read or change.

• Recall a person's role or current work
• Save facts that should be remembered later
• Correct outdated or inaccurate information

Save established decisions, project facts, failed approaches, and system architecture to Atlas shared memory. The skill checks existing entries to avoid duplicates and creates self-contained records that other agents and future sessions can use.

• Carry decisions and their reasons into future sessions
• Share project facts and conventions with other agents
• Record failed approaches to avoid repeating them

Review an agent's saved context and unread coordination messages at the start of a task. Summarize relevant assignments, move handled messages aside, and prompt the agent to preserve lasting decisions and outcomes before finishing.

• Resume work using context saved in a previous session
• Check new messages and assignments for an agent
• Preserve decisions and outcomes for the next session

Recall earlier work and organize lasting preferences, project facts, outcomes, and lessons. The skill distinguishes concise memory from original records, reusable procedures that belong in skills, and recurring requests that need scheduled tasks.

• Resume earlier work using remembered context
• Save lasting preferences and project conventions
• Find exact wording and evidence in past records

Write and review .NET calls to native C/C++ libraries using P/Invoke. Covers function declarations, string conversion, memory ownership, and platform differences to help investigate crashes, memory leaks, and corrupted data.

• Create .NET declarations from C/C++ headers
• Investigate crashes and memory leaks in native calls
• Review string encoding and memory ownership

Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.

The English summary is being prepared. Showing the original description.

[omh] Project fact to remember across future sessions: capture one bounded durable project or product memory candidate through explicit remember, refuse, or defer review; for a past decision use decision-recall. Use when the user says: memory-new, new memory, project memory, product memory, remember this project, remember this product, do not save, do not save this token.

The English summary is being prepared. Showing the original description.