obelisk/SKILL.md

---
name: obelisk
description: >
  Search and query past Claude Code session history.
  Reactive: when the user asks "how did I fix X", "what did we do last time", "find the session where", "上次怎么修的", "之前的session", "历史记录".
  Proactive: when the user references past work you lack context for, when you're about to modify a file with complex edit history, when the user says "继续之前的" or "continue where we left off", or when understanding prior decisions would improve your current response.
allowed-tools:
  - Read
  - Bash(node:*)
  - Write
---

# obelisk

Searches and queries your Claude Code session history stored in `~/.claude/`.
A SQLite index with FTS5 full-text search covers all sessions, subagent conversations, and workflow agent runs.
You write JS query snippets that run in a sandboxed VM against the indexed data, then parse the JSON output.

## Quick Start

The base directory for this skill is provided as `$SKILL_DIR` at invocation time (shown as "Base directory for this skill: ...").

**Fast keyword search** (no script needed):

```bash
node $SKILL_DIR/scripts/runtime.mjs --search "keyword"
```

**Custom query** (write a JS snippet, run it):

1. Write a query to a temp file (e.g. `/tmp/q.mjs`)
2. Run: `node $SKILL_DIR/scripts/runtime.mjs --query /tmp/q.mjs`
3. Parse the JSON stdout and answer the user

The query file body is executed inside `(async () => { ... })()` with the API below available as globals. The last expression is returned as JSON. Use `return` to emit results.

## API

### search(text, opts?)

Full-text search across all messages (user, assistant, subagent, workflow agent).

Returns: `[{ message: {uuid, text, role, timestamp, model}, session: {id, title, project, started_at}, context: [...surrounding messages] }]`

opts: `{ limit, sessionId, project, after, before }`

### context(uuid)

Full story around a message: the message itself, parent chain, session info, subagent/workflow metadata.

Returns: `{ message, parentChain, session, subagent, workflow }`

### recent(n?)

Latest n sessions (default 10). Returns session rows with title, project, started_at, ended_at.

### sql(query, ...params)

Raw SQL. Use `?` placeholders. Returns array of row objects.

**Before writing your first SQL query, read `references/schema.md` for the full table schema, column names, and relationships.** Don't guess column names — the schema is your source of truth.

Tables: `sessions`, `messages`, `tool_calls`, `tool_results`, `subagents`, `workflows`, `workflow_agents`, `messages_fts`

### Other APIs

- `trace(uuid)` -- full parent chain from root to message
- `thread(sessionId)` -- all messages in a session, ordered by time
- `subagents(sessionId)` -- subagent metadata + message counts
- `workflows(sessionId?)` -- workflow runs (all if no sessionId)
- `workflowTree(runId)` -- workflow + its agents + all their messages
- `fileHistory(filePath)` -- every Edit/Write/Read on a file across sessions
- `failures(sessionId?)` -- tool calls that returned errors, with surrounding context
- `raw(uuid, opts?)` -- windowed access to the original JSONL line (bypasses index truncation)

### raw(uuid, opts?)

Some indexed fields (tool call inputs, tool results) are truncated to 10k chars. `raw()` reads the original JSONL line to recover the full content.

Returns: `{ text, totalLength, offset, limit, hasMore }`

opts: `{ offset: 0, limit: 10000 }` — character window into the raw JSONL line.

```js
// First window
const r = raw(messageUuid)
// r.text = first 10k chars of the original JSONL line
// r.totalLength = full line length
// r.hasMore = true if more content remains

// Scroll forward
const r2 = raw(messageUuid, { offset: 10000, limit: 10000 })
```

## Examples

### "上次怎么修 auth 的"

```js
const hits = search('auth fix')
return hits.slice(0, 5).map(h => ({
  session: h.session.title,
  date: h.session.started_at,
  message: h.message.text?.slice(0, 200)
}))
```

### "最近在做什么"

```js
return recent(10).map(s => ({ title: s.title, project: s.project, date: s.started_at }))
```

### "哪些文件被反复修改"

```js
return sql(`
  SELECT file_path, COUNT(*) as n FROM tool_calls
  WHERE name IN ('Edit','Write') AND file_path IS NOT NULL
  GROUP BY file_path HAVING n > 3 ORDER BY n DESC LIMIT 20
`)
```

### "那个 review workflow 的结果是什么"

```js
const wfs = workflows()
const review = wfs.find(w =>
  w.run_id.includes('review') ||
  JSON.parse(w.result_json || '{}').synthesis
)
return review ? JSON.parse(review.result_json) : 'No review workflow found'
```

### "上次跑 experiment 用了多少 token"

```js
const hits = search('experiment')
if (!hits.length) return 'No experiment sessions found'
const sid = hits[0].session.id
return sql('SELECT SUM(input_tokens) as input, SUM(output_tokens) as output FROM messages WHERE session_id = ?', sid)
```

### "追踪一下那个决策是怎么做的"

```js
const hits = search('the decision query here')
if (!hits.length) return 'Nothing found'
return context(hits[0].message.uuid)
```

## Notes

- First run builds the index (~5s for ~100 sessions). Subsequent runs are incremental.
- DB location: `~/.claude/obelisk.sqlite`
- Subagent and workflow agent conversations are fully indexed and searchable.
- Query scripts run in a sandboxed VM context -- no file system or network access from inside scripts.
- Text is truncated to 10k chars per message during indexing.
- FTS5 search supports standard SQLite FTS syntax: `"exact phrase"`, `term1 AND term2`, `term1 OR term2`, `term1 NOT term2`.