obelisk/README.md

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Every past session, subagent, and workflow -- queryable by your agent.

**Humans should not browse session history. Agents should query it.**

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  <img src=".github/assets/demo.png" alt="Obelisk in action" width="540">
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  <p>Ask in plain language. The agent writes the query, runs it, answers.</p>
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## Not a session browser

Most history tools help humans find old chats.

Obelisk is built for agents. It exposes past work as structured data: sessions,
messages, tool calls, subagents, workflows, file history, failures, parent
chains, and human-approved markdown memories. The agent writes the query, runs
it locally, and answers in plain language.

You don't manage history. You ask questions about past work.

## Why Obelisk

| Session library | Obelisk |
|---|---|
| Find an old chat | Answer a question about past work |
| Human browses snippets | Agent writes and runs a query |
| Search result list | Structured context and reasoning |
| Sessions as documents | Sessions as queryable memory |
| Good for recall | Good for investigation |

## What you can ask

```
/obelisk 上次 auth bug 最后到底改了哪些文件，为什么这么改
/obelisk 这个文件最近在哪些 sessions 里被反复修改
/obelisk 找出最近失败的 tool calls，它们分别发生在哪些任务里
/obelisk 那个 review workflow 的 subagents 各自结论是什么
/obelisk 我之前有没有试过这个方案，结果为什么放弃了
```

Anything Claude Code has done before -- sessions, tool calls, subagents, workflows -- becomes structured, queryable memory. Ask in your own words.

## Install

```bash
npx skills add tommy0103/obelisk
```

Or manually: copy `obelisk/` into your project's `.claude/skills/`.

Then in any Claude Code session:

```
/obelisk <your question>
```

First run builds the index (~5 seconds for 100 sessions). After that it rebuilds incrementally.

### Requires

- Node.js 22+ (uses built-in node:sqlite with FTS5)
- Claude Code with skills support.

## How it works

```
You ask a question
  ↓
Agent writes a JS query against the SQLite index
  ↓
Runs it via node runtime.mjs --query <script>
  ↓
Reads the JSON result, answers you in natural language
```

When a retrieval produces a memory worth keeping, the agent proposes a markdown
memory file. After user approval, it registers that file with the narrow
`runtime.mjs --attune <script>` runtime, which exposes only memory mutation
helpers such as `remember()` and `forget()`.

Memory is a synthesis cache, not a replacement for raw sessions. The agent can
decide whether to use, ignore, or verify a memory during an answer. Persistent
changes still require human approval, but explicit corrections count: if you say
a memory is wrong, outdated, or should be replaced, the agent can archive or
update the exact matching record without a second confirmation.

**The core idea: don't make humans browse, tag, or organize sessions.**
Don't invent a rigid query DSL either.

Agents can write code. So Obelisk gives them a small local query runtime over
your past work.

The agent starts from a small core API, then uses structured shortcuts and
references only when the question needs them:

**Core primitives** — the main CodeAct surface:

- `search(text)` — FTS5 full-text search, returns matches with surrounding context plus message `content_type` and `is_meta`
- `context(uuid)` — full story around a message (parent chain, subagent/workflow metadata)
- `sql(query, ...params)` — read-only SQL for structured queries

**Structured shortcuts** — overview, session, memory, summary, subagent,
workflow, file-history, failure, raw-window, and parent-chain helpers over the
same SQLite data.

**References** — agent reads on demand when the task needs deeper structure:

- `references/schema.md` — full SQLite schema and API reference
- `references/query-patterns.md` — copyable CodeAct recipes for common retrieval tasks
- `references/pitfalls.md` — scope, FTS, ordering, compact/raw, and field-name traps

The design is progressive disclosure with guardrails: the main skill keeps the
core contract and high-risk pitfalls visible, while longer recipes and the full
schema stay out of the first prompt until the agent needs them.

## What gets indexed

| Layer | Source | What's captured |
|-------|--------|----------------|
| **Sessions** | `<project>/<sessionId>.jsonl` | Title, project, timestamps, git branch |
| **Messages** | user + assistant turns | Full text, model, token usage, parent chain |
| **Tool calls** | every tool invocation | Tool name, input, file paths touched |
| **Subagents** | `subagents/agent-<id>.jsonl` | Agent type, description, full conversation |
| **Workflows** | `workflows/wf_<runId>.json` | Script, structured result, agent count |
| **Workflow agents** | `subagents/workflows/wf_<runId>/` | Per-agent transcripts linked to workflow |
| **Memories** | markdown files registered by the agent after user approval | Prior conclusions linked to source sessions/messages and optional anchors |

Full-text search via FTS5 covers message text across every session layer and ranked memory recall over registered memory summaries, while the SQLite tables preserve the structure agents need for investigation.

## Structure

```
.claude/skills/obelisk/
├── SKILL.md              # Skill definition + simple API + examples
├── scripts/
│   └── runtime.mjs       # Indexer + query runtime (400 lines, zero deps)
└── references/
    ├── schema.md          # Full table schema + advanced API reference
    ├── query-patterns.md  # Copyable retrieval recipes
    └── pitfalls.md        # Scope, FTS, ordering, and compactness traps
```

## Implementation Notes

The index rebuilds incrementally — only new or modified JSONL files are re-parsed.

Zero npm dependencies. Uses Node 22's built-in node:sqlite with FTS5. The entire runtime is ~400 lines.

20K lines of scattered JSONL → something the agent can search() and sql() against in milliseconds.

---

## License

MIT @tommy0103