--- name: knowledge-management description: Organize and classify OpenClaw knowledge entries into local folders by content type (Research, Decision, Insight, Lesson, Pattern, Project, Reference, Tutorial). homepage: https://github.com/ClaireAICodes/openclaw-skill-knowledge-management metadata: { "openclaw": { "emoji": "📚", "requires": { "bins": ["km"] } } } ---

Knowledge Management Skill (Local Storage)

Organize your OpenClaw memory files into a structured local knowledge base. Automatically parses MEMORY.md and daily memory files, classifies entries by content type, and stores each as a timestamped markdown file in the appropriate folder.

Available Tools

Core Commands

• km sync [options] - Sync memory entries to local files
• km classify [options] - Parse and classify without storing (JSON output)
• km summarize [options] - Generate index files for each content type
• km cleanup [options] - Remove orphaned files
• km list_types - List all available content types

Setup

No API keys needed! The skill uses two locations:

• Input Workspace: Where MEMORY.md and memory/ daily files are read from.
• Output Directory: Where organized folders (Research/, Decision/, etc.) are written.

Both are detected automatically:

Input Workspace (source files)

1. OPENCLAWORKSPACE environment variable
2. --workspace CLI argument
3. Current working directory (if it contains MEMORY.md)
4. Default: ~/.openclaw/workspace

Output Directory (organized files)

1. --output-dir CLI argument (relative to workspace or absolute)
2. Default: /memory/KM

The skill will create the output directory and all content-type folders automatically.

If you want to pre-create:

mkdir -p ~/.openclaw/workspace/memory/KM/{Research,Decision,Insight,Lesson,Pattern,Project,Reference,Tutorial}

Usage Examples

Default locations (input at workspace root, output in memory/KM)

# From any directory (workspace auto-detected)
km sync --days_back 7 --cleanup

Custom input workspace and output directory

km sync --workspace /custom/input/workspace --output-dir /custom/output/KM --days_back 7

Using environment variables

export OPENCLAWORKSPACE=/custom/input/workspace
km sync --output-dir /custom/output/KM --days_back 7

Dry run (preview only)

km sync --dry_run --days_back 1

Classify entries and export JSON

km classify --days_back 3 > entries.json

Generate index files (default: output directory)

km summarize
# or specify different location
km summarize --output_dir ~/some/other/folder

Preview orphan cleanup

km cleanup --dry_run

List content types

km list_types

Storage Structure

Assuming default configuration:

• Input workspace: ~/.openclaw/workspace
• Output directory: ~/.openclaw/workspace/memory/KM

~/.openclaw/workspace/
├── MEMORY.md                (source file - you edit this)
├── memory/                  (daily memory files)
│   ├── 2025-02-11.md
│   ├── 2025-02-12.md
│   └── ...
└── memory/KM/               (organized output by the skill)
    ├── local-sync-state.json
    ├── local-sync-log.md
    ├── Research/
    │   ├── 20260215T1448_Title_Here_HASH.md
    │   └── ...
    ├── Decision/
    ├── Insight/
    ├── Lesson/
    ├── Pattern/
    ├── Project/
    ├── Reference/
    ├── Tutorial/
    ├── Research_Index.md
    ├── Decision_Index.md
    └── ... (other index files)

File Naming

Format: YYYYMMDDTHHMM_Title_With_Underscores_8CHARHASH.md

The 8-character content hash suffix prevents filename collisions when titles are identical but content differs.

File Content (YAML Frontmatter)

---
title: "Protocol Name"
content_type: "Research"
domain: "OpenClaw"
certainty: "Verified"
impact: "Medium"
confidence_score: 8
tags: ["AI", "Automation"]
source: "MEMORY.md"
source_file: "MEMORY.md"
date: "2026-02-11"
content_hash: "e4b30e75d0f5a662"
---

Entry body content starts here...

How It Works

1. Parses MEMORY.md and recent daily memory/*.md files
2. Classifies each entry (content type, domain, certainty, impact, tags, confidence)
3. Computes content hash for deduplication
4. Checks sync state (memory/local-sync-state.json) to skip already synced entries
5. Writes to appropriate folder with timestamp + hash filename
6. Updates state mapping (hash → filepath)
7. Optional cleanup removes files not in state

Classification Logic

• Content Type: Keyword matching (Research, Lesson, Decision, Pattern, Tutorial, Reference, Insight)
• Domain: Contextual inference (AI Models, OpenClaw, Cost, Trading, etc.)
• Certainty: Based on language (Verified, Likely, Speculative, Opinion)
• Impact: Importance indicators (High, Medium, Low, Negligible)
• Tags: Auto-extracted from predefined keyword map
• Confidence Score: 1–10 heuristic (source credibility, length, data mentions)

Customize by editing the EntryClassifier class in index-local.js.

State Management

memory/local-sync-state.json maps content hashes to file paths:

{
  "e4b30e75d0f5a662": "/path/to/Research/202602151440_Title_e4b30e75.md"
}

This enables idempotent syncs and fast duplicate detection.

Do not edit manually unless recovering from corruption.

Cron Integration

Automate daily syncs:

openclaw cron add \
  --name "Daily Knowledge Sync" \
  --cron "0 5 * * *" \
  --tz "Asia/Singapore" \
  --session isolated \
  --message "km sync --days_back 7"

Note: By default, the skill reads MEMORY.md from ~/.openclaw/workspace and writes organized files to ~/.openclaw/workspace/memory/KM. Use --workspace or --output-dir to customize these locations.

Troubleshooting

"km: command not found"

• Run npm link in the skill directory, or add ~/workspace/bin to PATH.

No entries found

• Ensure MEMORY.md uses ## section headers and ### entry titles within recognized sections.

Files not created

• Check write permissions; run with --verbose.

Old entries not syncing

• They may already be in state. Clear memory/KM/local-sync-state.json to force re-sync (caution: may duplicate files).

Duplicate files

• Run km cleanup to remove orphans, then km sync to create missing files.

---

Version: 2.0.0 Changed: 2026-02-15 — Switched from Notion to local storage, added hash suffixes for uniqueness. Author: Claire (OpenClaw Agent) License: MIT