Agent Skillsasherikov/shoggoth › memory-reflect

memory-reflect

GitHub

通过定期或按需回顾近期对话与笔记,提取关键洞察并整合至长期记忆,优化记忆质量。

shoggoth/ai/plugin/skills/memory-reflect/SKILL.md asherikov/shoggoth

Trigger Scenarios

定时任务触发 用户明确要求反思 上下文压缩后

Install

npx skills add asherikov/shoggoth --skill memory-reflect -g -y
More Options

Non-standard path

npx skills add https://github.com/asherikov/shoggoth/tree/main/shoggoth/ai/plugin/skills/memory-reflect -g -y

Use without installing

npx skills use asherikov/shoggoth@memory-reflect

指定 Agent (Claude Code)

npx skills add asherikov/shoggoth --skill memory-reflect -a claude-code -g -y

安装 repo 全部 skill

npx skills add asherikov/shoggoth --all -g -y

预览 repo 内 skill

npx skills add asherikov/shoggoth --list

SKILL.md

Frontmatter
{
    "name": "memory-reflect",
    "description": "Sleep-time memory reflection: review recent conversations and daily notes, extract insights, and consolidate into long-term memory. Use when triggered by cron, heartbeat, or explicit request to reflect on recent activity. Runs as background processing to improve memory quality over time."
}

Memory Reflect

Review recent activity and consolidate valuable insights into long-term memory.

Inspired by sleep-time compute — the idea that memory formation happens best between active sessions, not during them.

When to Run

  • Cron/heartbeat: Schedule as a periodic background task (recommended: 1-2x daily)
  • On demand: User asks to reflect, consolidate, or review recent memory
  • Post-compaction: After context window compaction events

Process

1. Gather Recent Material

Find what changed recently, then read the relevant files:

# Find recently modified notes — use json format for the complete list
# (text format truncates to ~5 items in the summary)
recent_activity(timeframe="2d", output_format="json")

# Read specific daily notes
read_note(identifier="memory/2026-02-27")
read_note(identifier="memory/2026-02-26")

# Check active tasks
search_notes(note_types=["task"], status="active")

2. Evaluate What Matters

For each piece of information, ask:

  • Is this a decision that affects future work? → Keep
  • Is this a lesson learned or mistake to avoid? → Keep
  • Is this a preference or working style insight? → Keep
  • Is this a relationship detail (who does what, contact info)? → Keep
  • Is this transient (weather checked, heartbeat ran, routine task)? → Skip
  • Is this already captured in MEMORY.md or another long-term file? → Skip

3. Update Long-Term Memory

Write consolidated insights to MEMORY.md following its existing structure:

  • Add new sections or update existing ones
  • Use concise, factual language
  • Include dates for temporal context
  • Remove or update outdated entries that the new information supersedes

4. Log the Reflection

Append a brief entry to today's daily note:

## Reflection (HH:MM)
- Reviewed: [list of files reviewed]
- Added to MEMORY.md: [brief summary of what was consolidated]
- Removed/updated: [anything cleaned up]

Guidelines

  • Be selective. The goal is distillation, not duplication. MEMORY.md should be curated wisdom, not a copy of daily notes.
  • Preserve voice. If the agent has a personality/soul file, reflections should match that voice.
  • Don't delete daily notes. They're the raw record. Reflection extracts from them; it doesn't replace them.
  • Merge, don't append. If MEMORY.md already has a section about a topic, update it in place rather than adding a duplicate entry.
  • Flag uncertainty. If something seems important but you're not sure, add it with a note like "(needs confirmation)" rather than skipping it entirely.
  • Restructure over time. If MEMORY.md is a chronological dump, restructure it into topical sections during reflection. Curated knowledge > raw logs.
  • Check for filesystem issues. Look for recursive nesting (memory/memory/memory/...), orphaned files, or bloat while gathering material.

Version History

  • 2222b87 Current 2026-07-25 05:38

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Metadata

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Version
ba8a359
Hash
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Indexed
2026-07-25 05:38

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