Results for “memory”

[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.

Use when adding Memory Events, Memory Tickets, memory-map.json, vault-references.json, PM Soul memory ownership, or Memory Curator routing to an agent repo.

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

Guides the 7-step MATLAB memory optimization workflow: baseline, profile, identify, optimize, measure, verify, report. Use when asked to reduce MATLAB memory usage, find memory bottlenecks, fix out-of-memory errors, or optimize memory-intensive code.

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

Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

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

Agent Memory Systems workflow skill. Use this skill when the user needs "Memory is the cornerstone of intelligent agents. Without it, every and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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

Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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

Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.

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

Agent-agnostic long-term project memory through `.memory-bank/` + RULES (TDD/SOLID/Clean Architecture/FSD/Mobile) + dev-toolkit commands. Use when working in a project with a `.memory-bank/` directory or when the user explicitly asks for memory-bank workflow, code rules, or dev-toolkit commands (`/mb`, plan, spec, verify, done; «память проекта», «контекст проекта», «план», «продолжай по плану»).

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

Use when a project contains docs/memory/, or when the user asks to remember, retrieve, update, compact, forget, or audit project memory. MemoryCustodian manages local plain-text project memory with minimal context loading.

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

Use ruLake as the agent's working memory. Auto-invoke /rulake-memory:memory-recall when the agent needs to look up something it might already know; auto-invoke /rulake-memory:memory-remember after the agent learns or decides something worth pinning. Closes the self-learning loop without requiring the agent author to wire it explicitly.

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

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.

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

Tidy an agent's always-loaded memory index (MEMORY.md) on macOS or Linux: trim long lines, move closed entries to an archive, fold domains into hub files, restore dropped pins and add pointers for orphan memory files. Use when the index is over budget or has orphans. Triggers: /memory-tidy, /mt, tidy memory.

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

アーキテクチャ知識グラフ(memory MCP / memory.jsonl)の初期作成。「知識グラフを初期化して」「memory グラフを作って」「アーキテクチャグラフを登録して」といった依頼、またはプロジェクト初期構築完了後の仕上げとして使用する。コードベースを調査し、モジュール構成と依存関係を entities / relations として登録する。

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

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

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

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.

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

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.

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

Audit this repo's auto-memory files and suggest promoting each one to a more persistent layer — user or repo CLAUDE.md, an existing or new skill, a settings.json hook, or a checked-in reference doc. Use when the user says "audit memory", "promote memory", "clean up memories", "memory-promote", or wants to move ephemeral memory into durable config.

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

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.

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

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

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

Build agent memory with Pinecone by storing searchable representations of conversations and documents. Retrieve relevant past context for answers, organize memory by session or user, and prototype semantic search that finds information by meaning.

• Building agent memory that persists across sessions
• Adding retrieved context to agent answers
• Organizing memory separately for each user

Set up and troubleshoot Honcho memory for Hermes so user preferences and facts carry across conversations. Configure how memory is recalled, manage separate agent identities across profiles, and adjust context size, observation settings, and reasoning frequency.

• Setting up cloud or self-hosted memory for Hermes
• Investigating missing memories or profile synchronization issues
• Sharing user context across multiple agent profiles

Design and evaluate how an AI agent remembers user preferences and history across conversations. Compare storage formats and memory frameworks, plan updates and compression, and distinguish personal memory from shared document retrieval while addressing sensitive information.

• Design user memory that persists across conversations
• Compare storage formats, Mem0, and Memobase
• Evaluate recall and cross-conversation reasoning

Use Dask to process tables, numerical arrays, and file collections in pieces across CPU cores or machines. Helps choose data structures, size memory-friendly chunks, coordinate dependent tasks, and diagnose slow or memory-heavy workflows.

• Aggregate CSV or Parquet files that exceed available memory
• Process large scientific arrays in chunks
• Clean JSON records and logs before analysis

Evaluates task complexity and selects a small set of existing skills for work spanning multiple areas. Uses agent-memory-mcp to record and retrieve successful combinations, while keeping simple tasks within the agent’s ordinary capabilities.

• Selecting skills for a complex task
• Coordinating work across UI, databases, and deployment
• Reusing successful skill combinations