llm-wiki-kit

name: claude-history-ingest description: > Ingest Claude Code conversation history into the Obsidian wiki. Use this skill when the user wants to mine their past Claude conversations for knowledge, import their ~/.claude folder, extract insights from previous coding sessions, or says things like "process my Claude history", "add my conversations to the wiki", "what have I discussed with Claude before". Also triggers when the user mentions their .claude folder, Claude projects, session data, past conversation logs, local-agent-mode sessions, or audit logs.

Claude History Ingest — Conversation Mining

You are extracting knowledge from the user's past Claude Code conversations and distilling it into the Obsidian wiki. Conversations are rich but messy — your job is to find the signal and compile it.

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

Before You Start

1. Resolve the vault path (precedence, highest first): the OBSIDIAN_VAULT_PATH environment variable if set, else a .env in the current working directory (vault-scoped), else ~/.obsidian-wiki/config (global default). Also read CLAUDE_HISTORY_PATH (defaults to ~/.claude).

2. Read .manifest.json at the vault root to check what's already been ingested

3. Read index.md at the vault root to know what the wiki already contains

Ingest Modes

Append Mode (default)

Check .manifest.json for each source file (conversation JSONL, memory file). Only process:

• Files not in the manifest (new conversations, new memory files, new projects)

• Files whose modification time is newer than their ingested_at in the manifest

This is usually what you want — the user ran a few new sessions and wants to capture the delta.

Full Mode

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

Claude Code Data Layout

Claude Code stores data in two locations. Scan both.

Source 1: ~/.claude/ (CLI sessions)

~/.claude/
├── projects/                          # Per-project directories
│   ├── -Users-name-project-a/         # Path-derived name (slashes → dashes)
│   │   ├── <session-uuid>.jsonl       # Conversation data (JSONL)
│   │   └── memory/                    # Structured memories
│   │       ├── MEMORY.md              # Memory index
│   │       ├── user_*.md              # User profile memories
│   │       ├── feedback_*.md          # Workflow feedback memories
│   │       └── project_*.md           # Project context memories
│   ├── -Users-name-project-b/
│   │   └── ...
├── sessions/                          # Session metadata (JSON)
│   └── <pid>.json                     # {pid, sessionId, cwd, startedAt, kind, entrypoint}
├── history.jsonl                      # Global session history
├── tasks/                             # Subagent task data
├── plans/                             # Saved plans
└── settings.json

Source 2: ~/Library/Application Support/Claude/local-agent-mode-sessions/ (Desktop app agent sessions)

The Claude desktop app stores local agent mode sessions here. The structure is deeply nested:

~/Library/Application Support/Claude/local-agent-mode-sessions/
└── <outer-uuid>/
    └── <inner-uuid>/
        ├── local_<session-uuid>.json          # Session metadata
        └── local_<session-uuid>/
            ├── audit.jsonl                    # Audit log — tool calls, file reads, commands run
            └── .claude/
                └── projects/
                    └── <path-encoded-name>/   # Same path-encoding as ~/.claude/projects/
                        └── <uuid>.jsonl       # Conversation transcript (same JSONL format as CLI)

How to find all local-agent-mode sessions:

# Find all session metadata files
find ~/Library/Application\ Support/Claude/local-agent-mode-sessions -name "local_*.json" -maxdepth 4

# Find all audit logs
find ~/Library/Application\ Support/Claude/local-agent-mode-sessions -name "audit.jsonl"

# Find all conversation transcripts
find ~/Library/Application\ Support/Claude/local-agent-mode-sessions -name "*.jsonl" -path "*/.claude/projects/*"

Session metadata (local_<uuid>.json) — JSON file with fields like sessionId, cwd, startedAt, model, title. Read this first to understand the session context before opening the transcript.

Audit log (audit.jsonl) — Each line is a JSON record of one agent action: tool calls (Read, Write, Bash, Edit), file accesses, shell commands executed, MCP calls. Useful for understanding what the agent actually did — often richer signal than the conversation text alone. Fields: type, toolName, input, output, timestamp, sessionId.

Conversation transcript (.claude/projects/.../<uuid>.jsonl) — Identical format to CLI conversation JSONL. Parse the same way as ~/.claude/projects/*/*.jsonl.

Key data sources ranked by value (both locations combined):

1. Memory files (~/.claude/projects/*/memory/*.md) — Pre-distilled, already wiki-friendly. Gold.

2. Conversation JSONL (both ~/.claude/projects/*/*.jsonl and desktop app transcripts) — Full conversation transcripts. Rich but noisy.

3. Audit logs (audit.jsonl in desktop sessions) — Tool-call level record of what was done. Useful for extracting concrete actions, file patterns, and command patterns even when the conversation is sparse.

4. Session metadata (sessions/*.json and local_*.json) — Tells you which project, when, and what CWD.

Step 1: Survey and Compute Delta

Scan both data locations and compare against .manifest.json:

# --- Source 1: CLI sessions (~/.claude) ---
# Find all projects
Glob: ~/.claude/projects/*/

# Find memory files (highest value)
Glob: ~/.claude/projects/*/memory/*.md

# Find conversation JSONL files
Glob: ~/.claude/projects/*/*.jsonl

# --- Source 2: Desktop app local-agent-mode sessions ---
DESKTOP_SESSIONS="$HOME/Library/Application Support/Claude/local-agent-mode-sessions"

# Session metadata
find "$DESKTOP_SESSIONS" -name "local_*.json" -maxdepth 4

# Audit logs
find "$DESKTOP_SESSIONS" -name "audit.jsonl"

# Conversation transcripts
find "$DESKTOP_SESSIONS" -name "*.jsonl" -path "*/.claude/projects/*"

Build a unified inventory and classify each file:

• New — not in manifest → needs ingesting

• Modified — in manifest but file is newer → needs re-ingesting

• Unchanged — in manifest and not modified → skip in append mode

Report to the user: "Found X CLI projects, Y desktop sessions. Memory files: A. Conversations: B. Audit logs: C. Delta: D new, E modified."

Step 2: Ingest Memory Files First

Memory files are already structured with YAML frontmatter:

---
name: memory-name
description: one-line description
type: user|feedback|project|reference
---

Memory content here.

For each memory file:

• Read it and parse the frontmatter

• user type → feeds into an entity page about the user, or concept pages about their domain

• feedback type → feeds into skills pages (workflow patterns, what works, what doesn't)

• project type → feeds into entity pages for the project

• reference type → feeds into reference pages pointing to external resources

The MEMORY.md index file in each project is a quick summary — read it first to decide which individual memory files are worth reading in full.

Step 3: Parse Conversation JSONL

Each JSONL file is one conversation session. Each line is a JSON object:

{
  "type": "user|assistant|progress|file-history-snapshot",
  "message": {
    "role": "user|assistant",
    "content": "text string"
  },
  "uuid": "...",
  "timestamp": "2026-03-15T10:30:00.000Z",
  "sessionId": "...",
  "cwd": "/path/to/project",
  "version": "2.1.59"
}

For assistant messages, content may be an array of content blocks:

{
  "content": [
    {"type": "thinking", "text": "..."},
    {"type": "text", "text": "The actual response..."},
    {"type": "tool_use", "name": "Read", "input": {...}}
  ]
}

What to extract from conversations:

• Filter to type: "user" and type: "assistant" entries only

• For assistant entries, extract text blocks (skip thinking and tool_use — those are noise)

• The cwd field tells you which project this conversation belongs to

• The project directory name (e.g., -Users-name-Documents-projects-my-app) tells you the project path

Skip these:

• type: "progress" — internal agent progress updates

• type: "file-history-snapshot" — file state tracking

• Subagent conversations (under subagents/ subdirectories) — unless the user specifically asks

Step 3b: Parse Audit Logs (desktop sessions only)

For each audit.jsonl found under local-agent-mode-sessions/, read it line by line. Each line is a JSON record of one agent action:

{
  "type": "tool_call",
  "toolName": "Bash",
  "input": {"command": "npm test"},
  "output": "...",
  "timestamp": "2026-04-10T14:22:00Z",
  "sessionId": "..."
}

What to extract from audit logs:

• File access patterns — which files does the agent repeatedly Read or Edit? These are the high-value files in the project. Note them as project references.

• Shell commands — recurring Bash commands reveal the project's build/test/deploy workflow. Distill these into a skills/ page (e.g. "how this project is built and tested").

• Tool call sequences — if the agent always does Read → Edit → Bash in a particular order, that's a workflow pattern worth capturing.

• Error patterns — failed tool calls (non-zero exit codes, error outputs) reveal pain points, known rough edges, or recurring bugs.

• MCP tool calls — calls to MCP tools reveal which external services and APIs the project integrates with.

Skip from audit logs:

• Routine file reads with no pattern (e.g. reading config files once)

• Tool outputs that are just noise (long stack traces, verbose logs) — summarize the error class, not the full output

• Anything that looks like secrets, tokens, or credentials in command arguments or outputs

Cross-reference with the conversation transcript: The audit log tells you what happened; the conversation tells you why. When both are available for the same session, use them together — the audit log grounds the conversation in concrete actions.

Read the paired local_<uuid>.json session metadata before processing the audit log — it gives you cwd, startedAt, and title to contextualize the actions.

Step 4: Cluster by Topic

Don't create one wiki page per conversation. Instead:

• Group extracted knowledge by topic across conversations

• A single conversation about "debugging auth + setting up CI" → two separate topics

• Three conversations across different days about "React performance" → one merged topic

• The project directory name gives you a natural first-level grouping

Step 5: Distill into Wiki Pages

Each Claude project maps to a project directory in the vault. The project directory name from ~/.claude/projects/ encodes the original path — decode it to get a clean project name:

-Users/Documents/projects/my-Project   → myproject
-Users/Documents/projects/Another-app  → anotherapp

Project-specific vs. global knowledge

For each project with content, create or update the project overview page at projects/<name>/<name>.md — named after the project, not _project.md. Obsidian's graph view uses the filename as the node label, so _project.md makes every project show up as _project in the graph. Naming it <name>.md gives each project a distinct, readable node name.

Important: Distill the knowledge, not the conversation. Don't write "In a conversation on March 15, the user asked about X." Write the knowledge itself, with the conversation as a source attribution.

Write a summary: frontmatter field on every new/updated page — 1–2 sentences, ≤200 chars, answering "what is this page about?" for a reader who hasn't opened it. wiki-query's cheap retrieval path reads this field to avoid opening page bodies.

Mark provenance per the convention in llm-wiki (Provenance Markers section):

• Memory files are mostly extracted — the user wrote them by hand and they're already distilled. Treat memory-derived claims as extracted unless you're stitching together claims from multiple memory files.

• Conversation distillation is mostly inferred. You're synthesizing a coherent claim from many turns of dialogue, often filling in implicit reasoning. Apply ^[inferred] liberally to synthesized patterns, generalizations across sessions, and "what the user really meant" interpretations.

• Use ^[ambiguous] when the user changed their mind across sessions or when assistant and user contradicted each other and the resolution is unclear.

• Write a provenance: frontmatter block on every new/updated page summarizing the rough mix.

Step 6: Update Manifest, Journal, and Special Files

Update .manifest.json

For each source file processed, add/update its entry with:

• ingested_at, size_bytes, modified_at

• source_type: one of "claude_conversation", "claude_memory", "claude_audit_log", "claude_desktop_session"

• project: the decoded project name

• pages_created and pages_updated lists

Also update the projects section of the manifest:

{
  "project-name": {
    "source_path": "~/.claude/projects/-Users-...",
    "vault_path": "projects/project-name",
    "last_ingested": "TIMESTAMP",
    "conversations_ingested": 5,
    "conversations_total": 8,
    "memory_files_ingested": 3,
    "desktop_sessions_ingested": 2,
    "audit_logs_ingested": 2
  }
}

Create journal entry + update special files

Update index.md and log.md per the standard process:

- [TIMESTAMP] CLAUDE_HISTORY_INGEST projects=N conversations=M desktop_sessions=D audit_logs=A pages_updated=X pages_created=Y mode=append|full

hot.md — Read $OBSIDIAN_VAULT_PATH/hot.md (create from the template in wiki-ingest if missing). Update Recent Activity with a one-line summary — e.g. "Ingested 5 Claude conversations across 2 projects; surfaced patterns in API design and testing strategy." Keep the last 3 operations. Update Active Threads if any ongoing project is now better understood. Update updated timestamp.

Privacy

• Distill and synthesize — don't copy raw conversation text verbatim

• Skip anything that looks like secrets, API keys, passwords, tokens

• If you encounter personal/sensitive content, ask the user before including it

• The user's conversations may reference other people — be thoughtful about what goes in the wiki

Reference

See references/claude-data-format.md for more details on the data structures.