build(obelisk): let Claude Code search its own session history
- SQLite + FTS5 index over ~/.claude JSONL transcripts. - Agent writes JS queries at runtime — same sandbox pattern as workflows. - Covers sessions, subagents, workflow executions, tool calls, and full-text search.
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<div align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset=".github/assets/obelisk-wordmark-d.svg">
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<img src=".github/assets/obelisk-wordmark-l2.svg" alt="Obelisk" width="540">
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</picture>
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Every past session, subagent, and workflow — searchable in natural language.
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</picture>
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</div>
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<br />
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<div align="center">
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<img src=".github/assets/demo.png" alt="Obelisk in action" width="540">
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<br />
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<p>Ask in plain language. The agent writes the query, runs it, answers.</p>
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</div>
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---
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## What you can ask
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```
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/obelisk 上次那个 auth 的 bug 我怎么修的
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/obelisk 哪些文件这周被反复修改
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/obelisk 最近 workflow 跑出来什么结果
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/obelisk 我让 subagent 做过哪些代码 review
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```
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Anything Claude Code has done before — sessions, tool calls, subagents, workflows — is indexed and searchable. Ask in your own words.
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## Install
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```bash
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npx skills add tommy0103/obelisk
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```
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Or manually: copy `obelisk/` into your project's `.claude/skills/`.
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Then in any Claude Code session:
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```
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/obelisk <your question>
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```
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First run builds the index (~5 seconds for 100 sessions). After that it rebuilds incrementally.
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### Requires
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- Node.js 22+ (uses built-in node:sqlite with FTS5)
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- Claude Code with skills support.
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## What gets indexed
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| Layer | Source | What's captured |
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|-------|--------|----------------|
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| **Sessions** | `<project>/<sessionId>.jsonl` | Title, project, timestamps, git branch |
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| **Messages** | user + assistant turns | Full text, model, token usage, parent chain |
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| **Tool calls** | every tool invocation | Tool name, input, file paths touched |
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| **Subagents** | `subagents/agent-<id>.jsonl` | Agent type, description, full conversation |
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| **Workflows** | `workflows/wf_<runId>.json` | Script, structured result, agent count |
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| **Workflow agents** | `subagents/workflows/wf_<runId>/` | Per-agent transcripts linked to workflow |
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Full-text search via FTS5 covers all message text across every layer.
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## How it works
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```
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You ask a question
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↓
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Agent writes a JS query against the SQLite index
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↓
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Runs it via node runtime.mjs --query <script>
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↓
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Reads the JSON result, answers you in natural language
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```
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The core idea: **don't design a query DSL** — let the agent write code. An agent that can write workflow scripts can also write query scripts. Same sandbox, same mental model.
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The agent has a two-tier API. Most questions only need the simple layer:
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**Simple API** — taught directly in the skill prompt:
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- `search(text)` — FTS5 full-text search, returns matches with surrounding context
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- `context(uuid)` — full story around a message (parent chain, subagent/workflow metadata)
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- `sql(query, ...params)` — raw SQL for anything else
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**Advanced API** — agent reads `references/schema.md` on demand:
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- `trace()` · `thread()` · `subagents()` · `workflows()` · `workflowTree()` · `fileHistory()` · `failures()` · `recent()`
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The design is progressive disclosure: the agent doesn't see the full schema until it needs it.
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## Structure
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```
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.claude/skills/obelisk/
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├── SKILL.md # Skill definition + simple API + examples
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├── scripts/
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│ └── runtime.mjs # Indexer + query runtime (400 lines, zero deps)
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└── references/
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└── schema.md # Full table schema + advanced API + query patterns
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```
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## Design
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The index rebuilds incrementally — only new or modified JSONL files are re-parsed.
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Zero npm dependencies. Uses Node 22's built-in node:sqlite with FTS5. The entire runtime is ~400 lines.
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20K lines of scattered JSONL → something the agent can search() and sql() against in milliseconds.
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---
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## License
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MIT @tommy0103
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