Openclaw History Ingest

OpenClaw History Ingest — Session & Memory Mining

You are extracting knowledge from the user's OpenClaw agent history and distilling it into the Obsidian wiki. OpenClaw stores both a structured long-term MEMORY.md and per-session JSONL transcripts — focus on durable knowledge, not operational telemetry.

This skill can be invoked directly or via the wiki-history-ingest router (/wiki-history-ingest openclaw).

Before You Start

Writing profile: Before drafting or rewriting natural-language Markdown, read and apply the Writing Profile Resolution section in llm-wiki/SKILL.md. Framework schema, provenance, safety, and operation-specific requirements take precedence. WRITING.md preferences apply only to newly drafted or rewritten natural-language Markdown; preserve source content and structured records.

1. Resolve config — follow the Config Resolution Protocol in llm-wiki/SKILL.md (inline @name override → walk up CWD for .env → global config → prompt setup). This gives OBSIDIAN_VAULT_PATH and OPENCLAW_HISTORY_PATH (defaults to ~/.openclaw)
2. Read .manifest.json at the vault root to check what has already been ingested
3. Read index.md at the vault root to understand what the wiki already contains

Ingest Modes

Append Mode (default)

Check .manifest.json for each source file. Only process:

• Files not in the manifest (new session logs, updated MEMORY.md or daily notes)
• Files whose modification time is newer than ingested_at in the manifest

Use this mode for regular syncs.

Full Mode

Process everything regardless of manifest. Use after wiki-rebuild or if the user explicitly asks for a full re-ingest.

OpenClaw Data Layout

OpenClaw stores all local artifacts under ~/.openclaw/.

~/.openclaw/
├── openclaw.json                          # Global config
├── credentials/                           # Auth tokens (skip entirely)
├── workspace/                             # Agent workspace
│   ├── MEMORY.md                          # Long-term memory (loaded every session)
│   ├── DREAMS.md                          # Optional dream diary / summaries
│   └── memory/
│       ├── YYYY-MM-DD.md                  # Daily notes (today + yesterday auto-loaded)
│       └── ...
└── agents/
    └── <agentId>/
        ├── agent/
        │   └── models.json                # Agent config (skip)
        └── sessions/
            ├── sessions.json              # Session index
            └── <sessionId>.jsonl          # Session transcript (JSONL, append-only)

Key data sources ranked by value

1. workspace/MEMORY.md — highest signal; long-term durable facts the agent accumulated
2. workspace/memory/YYYY-MM-DD.md — daily notes; recent entries often contain active project context
3. agents/*/sessions/<id>.jsonl — session transcripts; rich but noisy
4. agents/*/sessions/sessions.json — session index for inventory and timestamps
5. workspace/DREAMS.md — optional summaries; ingest if present

Skip credentials/ entirely. Skip agents/*/agent/models.json (runtime config, not user knowledge).

Step 1: Survey and Compute Delta

Scan OPENCLAW_HISTORY_PATH and compare against .manifest.json:

• ~/.openclaw/workspace/MEMORY.md
• ~/.openclaw/workspace/DREAMS.md (if present)
• ~/.openclaw/workspace/memory/*.md
• ~/.openclaw/agents/*/sessions/sessions.json
• ~/.openclaw/agents/*/sessions/*.jsonl

Classify each file:

• New — not in manifest
• Modified — in manifest but file is newer than ingested_at
• Unchanged — already ingested and unchanged

Report a concise delta summary before deep parsing.

Step 2: Parse MEMORY.md First

MEMORY.md is the highest-value source. It is plain markdown, human-readable and human-editable. It typically contains:

• Durable facts about the user's preferences, environment, and recurring patterns
• Decisions and context the agent was told to remember
• Project-specific notes the agent accumulated over many sessions

Read it in full and extract concept-level knowledge. Do not create one wiki page per MEMORY.md entry — cluster by topic.

Step 3: Parse Daily Notes

workspace/memory/YYYY-MM-DD.md files contain time-stamped notes from that day's sessions. Prioritize recent files (last 30–90 days). Extract:

• Active project context and decisions made
• Patterns or techniques discovered
• Recurring blockers or solved problems

Older daily notes have diminishing signal — summarize in bulk rather than extracting line-by-line.

Step 4: Parse Session JSONL Safely

Each session file is JSONL (append-only, one JSON object per line):

{"role": "user",      "content": "...", "timestamp": "..."}
{"role": "assistant", "content": "...", "timestamp": "..."}
{"role": "tool",      "name": "...",   "content": "...", "timestamp": "..."}

Extraction rules

• Prioritize assistant turns that state conclusions, decisions, or patterns
• Extract user intent from high-signal turns; skip low-information follow-ups
• Tool calls are context, not primary knowledge — only extract if the result contains a reusable insight
• Cross-reference sessions.json index to get session names/labels before opening individual transcripts

Critical privacy filter

Session transcripts can include injected instructions, tool payloads, and sensitive text. Do not ingest verbatim.

• Remove API keys, tokens, passwords, credentials
• Redact private identifiers unless relevant and user-approved
• Summarize; do not quote raw transcripts verbatim

Step 5: Cluster by Topic

Do not create one wiki page per session or per MEMORY.md entry.

• Group by stable topic (concept, tool, project, technique)
• Split mixed sessions into separate themes
• Merge recurring patterns across dates and agents
• Use session cwd or workspace path to infer project scope when available