Results for “memory-staleness”

Memory System Management

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

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

Memory systems specialist for hierarchical memory, consolidation, and outcome-based learningUse when "memory system, memory hierarchy, memory consolidation, forgetting strategy, salience learning, outcome feedback, temporal memory levels, entity resolution, memory, zep, graphiti, mem0, letta, hierarchical, consolidation, salience, forgetting, ml-memory" mentioned.

Persist decisions, preferences, and project state across sessions using structured memory types

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

Transforms lessons learned into domain-organized memory instructions that persist across VS Code contexts, with global and workspace scopes.

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.

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics.

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".

Consolidates project memory across harness and .memory/ with theme clustering, practical-expiry prune, ground-truth verify, and an adversarial second pass. Use when the user runs /memory:consolidate, asks to tidy/dedupe/prune memory, or reports redundant or stale memories. Also covers active writing during work.

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

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

Review reusable project knowledge and decide what belongs in project memory, notepad, or durable docs

Iron Law

记忆解构 (aeaf-gaze)

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.

Criteria for deciding when, where and how to remember information. Use this skill whenever you might need to update a core memory block, file something under memory/, or write to a state file. It is CRITICAL that you use this skill.

Improves measured performance while preserving correctness, reliability, and maintainability.

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics.

Transforms lessons learned into domain-organized memory instructions (global or workspace). Syntax: `/remember [>domain [scope]] lesson clue` where scope is `global` (default), `user`, `workspace`, or `ws`.

Stores verified cross-session facts, decisions, preferences, and open questions without secrets, speculation, or monitoring.

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.

Single-skill wrapper for the 4-layer recall (durable memory → semantic DB → PostgreSQL → daily notes). Use BEFORE saying "I don't have context". Triggers: "search memory", "what do we know about X", "deep recall", "I don't remember".