mem-record_skill

This skill automatically extracts key info from conversations and records it into the appropriate memory layer to support recall and decision making.
  • Python

79

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill zephyrwang6/myskill --skill mem-record

  • .DS_Store6.0 KB
  • SKILL.md5.4 KB

Overview

This skill captures and records key user memory items from conversations into a layered personal memory system. It automatically extracts events, decisions, preferences, emotions, and follow-up actions, then classifies them into the appropriate memory layer. It also detects repeated patterns and recommends promoting recurring items to higher-level memory files.

How this skill works

During a conversation, the skill extracts concise facts: what happened, decisions made, preferences stated, emotional reactions, and next steps. It chooses the storage layer (L1 context, L2 behavior, L3 cognitive, or suggests L4 core) based on content type and frequency rules. For repeated keywords or patterns it runs a count (grep-like) and prompts suggestions to promote items to higher layers while enforcing that L4 core changes remain manual.

When to use it

  • When the user explicitly asks to "record this" or "remember this" during a conversation.
  • When the system detects an important event, decision, or stated preference in dialogue.
  • After the user completes a significant task or makes a notable decision.
  • When a behavior or preference appears repeatedly (3+ occurrences) and may deserve consolidation.
  • When a statement touches on values or identity — to flag for manual L4 consideration.

Best practices

  • Extract concise, factual entries: event, decision, preference, emotion, and next action.
  • Record to L1 for single occurrences; suggest L2 when a pattern reaches 3+ occurrences.
  • Never auto-write to L4 core; only generate suggested content and prompt the user for manual update.
  • Tag entries with clear labels and timestamps to support future grep-style pattern detection.
  • Confirm with the user when promoting items between layers or when ambiguity exists.

Example use cases

  • User says "remember this": capture the decision, reasoning, and follow-up in L1 dated file.
  • Detecting repeated tool preference (e.g., "charts over long text") and suggesting adding to L2 behavior notes.
  • User completes a project and records outcome and feelings into the current month L1 file.
  • User expresses a life value (e.g., "growth over money"): flag for L4 and offer a suggested summary for manual review.
  • Automated check finds the third occurrence of a habit and prompts whether to consolidate into L2.

FAQ

It maps content type to layer rules: events and single decisions go to L1; repeated preferences or habits (3+ occurrences) suggest L2; recurring principles map to L3; values and identity are flagged for L4 and require manual edits.

Can this skill automatically change core (L4) memories?

No. The skill can detect and generate suggested L4 content but will never write to L4 automatically. User confirmation and manual editing are required.

How are repeated patterns detected?

The system performs keyword searches and counts occurrences in L1 files. When a threshold (typically 3) is reached it prompts a suggestion to promote the item to L2 or higher.

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mem-record skill by zephyrwang6/myskill | VeilStrat