Wiki Agent

Wiki Agent — Targeted Cross-Agent History Search + Ingest

You are doing a query-driven targeted ingest from one specific AI agent's raw conversation history. The user is typically working in a different agent right now and wants to pull in context from another agent's past sessions.

This is not bulk ingest. You find sessions about a specific topic, extract the relevant blobs, distill them into the wiki, and return a synthesized answer the user can act on immediately.

Command Routing

Parse the invocation to determine the target agent and optional query:

If no query is given, default to recent sessions mode: ingest the last 5 unprocessed sessions from that agent and return a summary of what was found. This is equivalent to a focused wiki-history-ingest for that agent only.

Before You Start

1. Resolve config — follow the Config Resolution Protocol in llm-wiki/SKILL.md (walk up CWD for .env → ~/.obsidian-wiki/config → prompt setup). This gives OBSIDIAN_VAULT_PATH. 2. Read $OBSIDIAN_VAULT_PATH/.manifest.json → know what's already ingested. 3. Read $OBSIDIAN_VAULT_PATH/hot.md if it exists → warm context on recent wiki activity.

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Step 1: Locate the Agent's History Root

If the history root doesn't exist, stop and tell the user: "No <agent> history found at <path>. Have you run <agent> on this machine? You can set a custom path with <CONFIG_VAR> in .env."

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Step 2: Build Session Inventory

Use the cheapest index source for each agent — don't open session files until you know which ones are relevant.

Claude

Primary index:   ~/.claude/projects/  (directories = projects, files = sessions)
Session files:   ~/.claude/projects/*/*.jsonl
Desktop index:   find ~/Library/Application Support/Claude/local-agent-mode-sessions -name "local_*.json"
Signal fields:   sessionId, cwd, startedAt, title (in local_*.json)

Build a list of sessions: {path, project_dir, modified_at, already_ingested}.

Codex

Primary index:   ~/.codex/session_index.jsonl
Session files:   ~/.codex/sessions/**/rollout-*.jsonl
Signal fields:   thread_id, name/title, updated_at (in session_index.jsonl)

Read session_index.jsonl as the inventory. Each line: {thread_id, name, updated_at}. Map thread IDs to rollout files by matching directory names.

Hermes

Primary index:   ~/.hermes/memories/*.md  (fast to scan)
Session files:   ~/.hermes/sessions/**/*.jsonl
Signal fields:   file names, memory titles, first 3 lines of each memory

Scan memory filenames first (they're often titled by topic). Fall back to session listing.

OpenClaw

Primary index:   ~/.openclaw/workspace/memory/MEMORY.md  (structured long-term memory)
Daily notes:     ~/.openclaw/workspace/memory/YYYY-MM-DD.md
Session index:   ~/.openclaw/agents/*/sessions/sessions.json
Session files:   ~/.openclaw/agents/*/sessions/*.jsonl

Read MEMORY.md sections first — it's the pre-compiled summary of everything. Daily notes give recency signal.

Copilot

Primary index:   session filenames / directory listing
Session files:   varies by client (VS Code: ~/.copilot/sessions/*.jsonl or similar)
Signal fields:   session timestamps, file names

Pi

Primary index:   ~/.pi/agent/sessions/--<cwd>--/ directories
Session files:   ~/.pi/agent/sessions/--<cwd>--/<timestamp>_<uuid>.jsonl
Signal fields:   cwd (decoded from dir name), session_info.name, timestamp in filename

Scan session directories first. Decode --<cwd>-- to get the working directory. Read the first line (session header) and any session_info entries for the session name. No separate index file — the filesystem is the index.

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Step 3: Score Sessions Against the Query

If a query was given, score each session in the inventory without opening full session files:

1. Name/title match — does the session name or thread title contain the query terms? Score: +3 2. CWD/project match — does the working directory suggest the right project? Score: +2 3. Recency — sessions from the last 90 days score higher than older ones. Score: +1 per 30-day recency bracket (max +3) 4. Already ingested — if this session was previously ingested and the wiki page already covers the query (check hot.md + index.md), flag as "covered" but still show in results

Select the top 3–5 sessions by score. If no query was given, select the 5 most recent unprocessed sessions.

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Step 4: Extract the Relevant Blob

Open each selected session file and extract only the content relevant to the query. Do not read the full session if it's large — use targeted extraction.

Per-Agent Extraction Strategy

Claude (JSONL conversation):

• Each line: {role, content, timestamp, ...}
• Search with: grep -i "<query terms>" <session.jsonl> to find the relevant lines
• Extract: the surrounding conversation window (10 lines before + 20 lines after each hit)
• Special signal: tool calls (Read/Write/Bash/Edit) reveal what was actually done — extract these even without keyword matches if they're in the relevant window

Codex (rollout JSONL):

• Each line: {type: "session_meta|turn_context|event_msg|response_item", ...}
• Filter to type: "event_msg" (user turns) and type: "response_item" (model output)
• Search with: grep -i "<query terms>" <rollout.jsonl>
• Extract: matching turns + their parent context (the turn_context preceding the match)
• Skip: session_meta events (operational metadata, not knowledge)

Hermes (memory files + session JSONL):

• For memory files: read the full file (they're short — typically <500 words each)
• For session JSONL: grep -i "<query terms>" + surrounding window
• Memory files with title matches → read fully; others → grep only

OpenClaw (MEMORY.md + daily notes + session JSONL):

• MEMORY.md: grep for section headers containing query terms → extract that section
• Daily notes: grep most recent 30 days for query terms → extract matching paragraphs
• Session JSONL: same grep-window approach as Claude
• Prefer MEMORY.md/daily notes over session JSONL (they're pre-synthesized)

Copilot (session JSONL):

• Same grep-window approach as Claude
• Look for checkpoint files if available (pre-summarized)

Pi (structured JSONL with tree layout):

• Each line is a tree entry: {type, id, parentId, timestamp, message?, ...}
• Build the active branch: map entries by id, find leaf (last entry with no children), walk parentId to root
• Search with: grep -i "<query terms>" <session.jsonl> to find matching entries
• Extract: the matching entries + their ancestors on the active branch (follow parent chain)
• Special signal: toolCall blocks inside assistant messages reveal what was actually done — extract these even without keyword matches if they're in the relevant window
• Prefer compaction and branch_summary entries when available — they're pre-synthesized summaries
• Skip thinking content blocks (noise) and model_change / thinking_level_change entries

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Step 5: Distill Blobs into Wiki Pages

For each extracted blob, determine where it belongs in the wiki:

1. Check if a wiki page already covers this — grep index.md and page frontmatter for the topic. If yes, update the existing page rather than creating a new one. 2. Determine category using standard rules (from llm-wiki/SKILL.md):

• Technique / how-to → skills/
• Abstract concept / pattern → concepts/
• Tool / library / person → entities/
• Cross-cutting insight → synthesis/

3. Write or update the page with required frontmatter:

   ---
   title: <topic>
   category: skill|concept|entity|synthesis
   tags: [tag1, tag2]
   sources: [<agent>://<path/to/session>]
   created: <date>
   updated: <date>
   confidence: high|medium|low
   lifecycle: stable|draft
   ---

Set sources with the agent prefix so memory-bridge can find it later. 4. Add cross-links to related wiki pages found in index.md.

Distillation rules (same as all ingest skills):

• Extract durable knowledge, not operational telemetry
• One wiki page per concept, not one per session
• Merge into existing pages rather than duplicating
• Keep the signal: decisions made, patterns discovered, techniques that worked, bugs explained

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Step 6: Return Synthesized Answer

After ingesting, immediately synthesize and return an answer from the newly ingested + existing wiki content:

## From <agent> history: "<query>"

**Found in:** <N> sessions (<session names/titles>)

**Key insights:**
<Synthesized answer — 3–5 bullet points of the most useful knowledge>

**Wiki pages updated/created:**
- [[page-name]] — <what was added>
- [[page-name]] — <what was added>

**Sessions ingested:**
| Session | Date | Relevance |
|---------|------|-----------|
| <name>  | <date> | <one-line why it was selected> |

**Gaps:** <What the sessions didn't cover that might be relevant>

If a query was given but no relevant sessions were found, say so explicitly: "No sessions about '<query>' found in <agent> history. The most recent sessions covered: <list topics from last 3 sessions>."

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Step 7: Update Tracking Files

Update .manifest.json for each session file processed:

{
  "<path>": {
    "ingested_at": "<now>",
    "source_type": "<agent>_conversation",
    "modified_at": "<file mtime>",
    "pages_created": [...],
    "pages_updated": [...]
  }
}

Append to log.md:

- [TIMESTAMP] WIKI-AGENT agent=<agent> query="<query>" sessions_searched=N sessions_ingested=M pages_created=X pages_updated=Y

Update hot.md with a one-line summary of what was ingested.

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Cross-Agent Use Patterns

These are the primary use cases this skill is designed for:

"I'm on Codex. What did I figure out about X in Claude?" → /wiki-claude "X" — finds Claude sessions about X, ingests them, returns the answer

"I solved a bug in Hermes last week. I need that context now in Claude Code." → /wiki-hermes "bug description" — surfaces and ingests the Hermes session

"What are all the approaches I've tried for X across all my tools?" → Run /wiki-claude "X", /wiki-codex "X", /wiki-hermes "X" in sequence — each ingests its slice, the wiki accumulates the cross-agent picture, then /memory-bridge diff shows what each tool uniquely contributed

No query — just "catch me up on recent Codex work" → /wiki-codex — ingests last 5 Codex sessions and returns a summary

"I'm on Claude Code. What did I figure out about X in Pi?" → /wiki-pi "X" — finds Pi sessions about X, ingests them, returns the answer

No query — just "catch me up on recent Pi work" → /wiki-pi — ingests last 5 Pi sessions and returns a summary

QMD Refresh After Vault Writes

QMD is a search index, not the source of truth. If $QMD_WIKI_COLLECTION is empty or unset, skip this step. Run it only after this skill has written or rewritten vault markdown. If QMD refresh fails, do not roll back the vault changes; report the QMD status separately.

Use $QMD_CLI if set; otherwise use qmd.

${QMD_CLI:-qmd} update

If the output says vectors are needed or embeddings may be stale, run:

${QMD_CLI:-qmd} embed

Verify the collection with either:

${QMD_CLI:-qmd} ls "$QMD_WIKI_COLLECTION"

or, when a specific page path is known:

${QMD_CLI:-qmd} get "qmd://$QMD_WIKI_COLLECTION/<page>.md" -l 5

Record one of:

• QMD refreshed: update + embed + verified
• QMD refreshed: update only + verified
• QMD skipped: QMD_WIKI_COLLECTION unset
• QMD skipped: qmd CLI unavailable
• QMD failed: <short error summary>