菜单
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
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Universal coding patterns, constraints, TDD workflow, atomic todos
Multi-model validation council — auto-validate plans, architecture changes, and PRs via validate-plan/review before executing
Mandatory code reviews via /code-review before commits and deploys
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基于 SOC 职业分类
| name | mnemos |
| description | 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 |
| when-to-use | When you need durable working memory across compactions — checkpoint decisions, preserve task handoffs, or audit what was remembered |
| user-invocable | false |
| effort | high |
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:
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 |
fatigue.json on every API callWhen Claude Code compacts the context (~83% full), Mnemos uses three layers:
.mnemos/just-compacted marker.mnemos-post-compact-inject.sh which 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.
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
mnemos ingest-claude --all # Ingest Claude Code transcripts (see below)
mnemos haze --recent 10 # Show per-session haziness scores
Mnemos can ingest Claude Code session transcripts (the per-session JSONL under
~/.claude/projects/) and score each session's haziness — a measure of how
much the agent struggled. The Stop hook does this automatically on session
exit; it is also available manually.
What's stored: only structural fields (roles, tool names, file paths, error flags, timestamps) plus a redacted, 200-char preview of each turn. Full content is never persisted, and secrets (API keys, tokens, PEM blocks, JWTs, credentials) are redacted before anything touches disk.
Haziness is a weighted score over five dimensions, each in [0,1]:
| Dimension | Weight | What it measures |
|---|---|---|
| correction_density | 0.30 | User corrections per eligible user turn |
| redo_ratio | 0.25 | Edits re-touched after an error |
| first_try_error_rate | 0.20 | Edits followed by errors within 3 turns |
| orphan_tool_use_rate | 0.15 | Tool calls with no matching result |
| backtrack_norm | 0.10 | git revert/reset --hard/restore calls |
The composite maps to a band: clear < 0.25 ≤ cloudy < 0.50 ≤ hazy < 0.75 ≤ lost.
mnemos ingest-claude --all # ingest every transcript + score
mnemos ingest-claude --session <id> # one session by id
mnemos ingest-claude --transcript <f> # a specific JSONL file
mnemos haze --recent 10 # table of recent sessions
mnemos haze --session <id> # per-dimension breakdown
Ingestion is idempotent (resumes via last_line_offset). Opt out per project
with touch .mnemos/claude-log.disabled.
When working on a task:
mnemos add goal "what you're trying to achieve" --task-id session-1mnemos add constraint "API backward compatibility" --scope src/api/mnemos fatiguemnemos checkpointMnemos bridges with iCPG (Intent-Augmented Code Property Graph):
mnemos bridge-icpg imports active ReasonNodes as GoalNodesEverything 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