mnemos
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies
下記のコマンドをコピーしてターミナル(Mac/Linux)または PowerShell(Windows)に貼り付けてください。 ダウンロード → 解凍 → 配置まで全自動。
mkdir -p ~/.claude/skills && cd ~/.claude/skills && curl -L -o mnemos.zip https://jpskill.com/download/21646.zip && unzip -o mnemos.zip && rm mnemos.zip
$d = "$env:USERPROFILE\.claude\skills"; ni -Force -ItemType Directory $d | Out-Null; iwr https://jpskill.com/download/21646.zip -OutFile "$d\mnemos.zip"; Expand-Archive "$d\mnemos.zip" -DestinationPath $d -Force; ri "$d\mnemos.zip"
完了後、Claude Code を再起動 → 普通に「動画プロンプト作って」のように話しかけるだけで自動発動します。
💾 手動でダウンロードしたい(コマンドが難しい人向け)
- 1. 下の青いボタンを押して
mnemos.zipをダウンロード - 2. ZIPファイルをダブルクリックで解凍 →
mnemosフォルダができる - 3. そのフォルダを
C:\Users\あなたの名前\.claude\skills\(Win)または~/.claude/skills/(Mac)へ移動 - 4. Claude Code を再起動
⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。
🎯 このSkillでできること
下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。
📦 インストール方法 (3ステップ)
- 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
- 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
- 3. 展開してできたフォルダを、ホームフォルダの
.claude/skills/に置く- · macOS / Linux:
~/.claude/skills/ - · Windows:
%USERPROFILE%\.claude\skills\
- · macOS / Linux:
Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。
詳しい使い方ガイドを見る →- 最終更新
- 2026-05-18
- 取得日時
- 2026-05-18
- 同梱ファイル
- 1
📖 Claude が読む原文 SKILL.md(中身を展開)
この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。
Mnemos — Task-Scoped Memory Lifecycle
What It Does
Mnemos prevents lossy context compaction from destroying the structured knowledge you need most. It treats your working memory as a typed graph (MnemoGraph) where different types of knowledge have different eviction policies:
- GoalNodes and ConstraintNodes are NEVER evicted — they survive all compaction
- ResultNodes are compressed (summary kept) before eviction
- ContextNodes are evictable when their activation weight drops
- CheckpointNodes persist to disk for session resume
Fatigue Model
Mnemos monitors 4 dimensions of "agent fatigue" — all passively observed from hook data, no manual input needed:
| Dimension | Weight | Signal Source | What It Measures |
|---|---|---|---|
| Token utilization | 0.40 | Statusline JSON | How full the context window is |
| Scope scatter | 0.25 | PreToolUse file paths | How many directories the agent is bouncing between |
| Re-read ratio | 0.20 | PreToolUse Read calls | How often the agent re-reads files it already read (context loss) |
| Error density | 0.15 | PostToolUse outcomes | What fraction of tool calls are failing (agent struggling) |
Fatigue states and actions:
| State | Score | Action |
|---|---|---|
| FLOW | 0.0–0.4 | Normal operation |
| COMPRESS | 0.4–0.6 | Micro-consolidation runs (compress 3 ResultNodes, evict 1 cold ContextNode) |
| PRE-SLEEP | 0.6–0.75 | Checkpoint written, consolidation runs |
| REM | 0.75–0.9 | Emergency checkpoint, consider wrapping up |
| EMERGENCY | 0.9+ | Checkpoint written, hand off immediately |
How To Use
Automatic (hooks handle everything):
- Statusline writes
fatigue.jsonon every API call - PreToolUse hook reads fatigue before every edit, auto-checkpoints at 0.60+
- PreCompact hook writes emergency checkpoint, compaction marker, and tells summarizer what to preserve
- SessionStart "compact" fires immediately after compaction, re-injects full checkpoint (primary restore)
- SessionStart "startup|resume" loads last checkpoint on new/resumed sessions
- PreToolUse fallback (no matcher) detects compaction marker if SessionStart didn't fire
- Stop hook writes final checkpoint for next session
Post-Compaction Recovery (Three-Layer Defense):
When Claude Code compacts the context (~83% full), Mnemos uses three layers:
- Layer 1 (PreCompact): Outputs strong preservation instructions with inline checkpoint content for the summarizer. Writes
.mnemos/just-compactedmarker. - Layer 2 (SessionStart "compact"): PRIMARY re-injection. Fires immediately when Claude resumes after compaction — before any agent action. Consumes the marker and injects the full checkpoint into the fresh context. This is the recommended approach per the RFC (Wake State Reconstruction).
- Layer 3 (PreToolUse fallback): If SessionStart doesn't fire (older versions, edge cases), the first tool call triggers
mnemos-post-compact-inject.shwhich detects the marker and injects. Safety net only.
The result: after compaction, you'll see a "CONTEXT RESTORED AFTER COMPACTION" block with your goal, constraints, what you were working on, and progress. Resume from there.
Manual CLI:
mnemos init # Initialize .mnemos/
mnemos status # Show node counts + fatigue
mnemos fatigue # Detailed fatigue breakdown
mnemos checkpoint --force # Write checkpoint now
mnemos resume # Output checkpoint for context
mnemos consolidate # Run micro-consolidation
mnemos nodes --type goal # List active GoalNodes
mnemos add goal "Build auth" # Add a GoalNode
mnemos bridge-icpg # Import iCPG ReasonNodes
Agent Instructions
When working on a task:
- Create a GoalNode at the start:
mnemos add goal "what you're trying to achieve" --task-id session-1 - Add ConstraintNodes for invariants:
mnemos add constraint "API backward compatibility" --scope src/api/ - Check fatigue before long operations:
mnemos fatigue - Checkpoint at sub-goal boundaries:
mnemos checkpoint - On session resume: the SessionStart hook automatically loads your checkpoint
iCPG Integration
Mnemos bridges with iCPG (Intent-Augmented Code Property Graph):
mnemos bridge-icpgimports active ReasonNodes as GoalNodes- Postconditions/invariants become ConstraintNodes
- Checkpoint includes iCPG state (active intent, unresolved drift)
Storage
Everything lives in .mnemos/ (gitignored):
mnemo.db— SQLite MnemoGraphfatigue.json— Live token metrics (updated per API call by statusline)signals.jsonl— Behavioral signal log (appended by PreToolUse + PostToolUse hooks)checkpoint-latest.json— Most recent checkpointcheckpoints/— Archived checkpoints