tommy0103
b7506ee765
Introduce a Settings view for configuring the Claude data directory (with WSL auto-detection on Windows), sidebar project grouping module, and an empty-state onboarding screen for SessionList. Refactor recap-patterns.md into per-card reference files under references/recap/ with separate retrieval and writing guides. Remove the legacy panel.html. On the data layer: incremental indexing via changedPaths, per-session live-update IPC (obelisk:session-updated), and session dirty-tracking in the renderer.
207 lines
8.7 KiB
Markdown
207 lines
8.7 KiB
Markdown
<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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[](https://github.com/tommy0103/obelisk/stargazers)
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[](https://github.com/tommy0103/obelisk/releases)
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[](LICENSE)
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Every past session, subagent, and workflow -- queryable by your agent.
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**Humans should not browse session history. Agents should query it.**
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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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## Not a session browser
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Most history tools help humans find old chats.
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Obelisk is built for agents. It exposes past work as structured data: sessions,
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messages, tool calls, subagents, workflows, file history, failures, parent
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chains, and human-approved markdown memories. The agent writes the query, runs
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it locally, and answers in plain language.
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You don't manage history. You ask questions about past work.
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## Why Obelisk
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| Session library | Obelisk |
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|---|---|
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| Find an old chat | Answer a question about past work |
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| Human browses snippets | Agent writes and runs a query |
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| Search result list | Structured context and reasoning |
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| Sessions as documents | Sessions as queryable memory |
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| Good for recall | Good for investigation |
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## What you can ask
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```
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/obelisk 上次 auth bug 最后到底改了哪些文件，为什么这么改
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/obelisk 这个文件最近在哪些 sessions 里被反复修改
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/obelisk 找出最近失败的 tool calls，它们分别发生在哪些任务里
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/obelisk 那个 review workflow 的 subagents 各自结论是什么
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/obelisk 我之前有没有试过这个方案，结果为什么放弃了
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```
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Anything Claude Code has done before -- sessions, tool calls, subagents, workflows -- becomes structured, queryable memory. 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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## 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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When a retrieval produces a memory worth keeping, the agent proposes a markdown
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memory file. After user approval, it registers that file with the narrow
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`runtime.mjs --attune <script>` runtime, which exposes only memory mutation
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helpers such as `remember()` and `forget()`.
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Memory is a synthesis cache, not a replacement for raw sessions. The agent can
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decide whether to use, ignore, or verify a memory during an answer. Persistent
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changes still require human approval, but explicit corrections count: if you say
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a memory is wrong, outdated, or should be replaced, the agent can archive or
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update the exact matching record without a second confirmation.
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**The core idea: don't make humans browse, tag, or organize sessions.**
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Don't invent a rigid query DSL either.
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Agents can write code. So Obelisk gives them a small local query runtime over
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your past work.
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The agent starts from a small core API, then uses structured shortcuts and
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references only when the question needs them:
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**Core primitives** — the main CodeAct surface:
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- `search(text)` — FTS5 full-text search, returns matches with surrounding context plus message `content_type` and `is_meta`
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- `context(uuid)` — full story around a message (parent chain, subagent/workflow metadata)
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- `sql(query, ...params)` — read-only SQL for structured queries
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**Structured shortcuts** — overview, session, memory, summary, subagent,
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workflow, file-history, failure, raw-window, and parent-chain helpers over the
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same SQLite data.
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**References** — agent reads on demand when the task needs deeper structure:
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- `references/schema.md` — full SQLite schema and API reference
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- `references/query-patterns.md` — copyable CodeAct recipes for common retrieval tasks
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- `references/retrieval-semantics.md` — query design frame for scoped and synthesis retrieval
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- `references/recap/overview.md` — optional `/obelisk recap` card-by-card entrypoint
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- `references/recap/pattern1-cover.md` and `references/recap/writing1-cover.md`
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- `references/recap/pattern2-thinking.md` and `references/recap/writing2-thinking.md`
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- `references/recap/pattern3-vibe.md` and `references/recap/writing3-vibe.md`
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- `references/recap/pattern4-workflow.md` and `references/recap/writing4-workflow.md`
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- `references/recap/pattern5-closing.md` and `references/recap/writing5-closing.md`
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- `references/pitfalls.md` — scope, FTS, ordering, compact/raw, and field-name traps
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The executable SQLite schema lives in `scripts/schema.sql`; `references/schema.md`
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is the human/agent explanation of that contract.
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The design is progressive disclosure with guardrails: the main skill keeps the
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core contract and high-risk pitfalls visible, while longer recipes and the full
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schema stay out of the first prompt until the agent needs them.
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The optional recap references are only for the explicit `/obelisk recap` intent;
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they are not part of the ordinary retrieval path. `references/recap/overview.md`
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drives a card-by-card loop: read one card's retrieval pattern, gather that
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card's evidence, read its writing reference, update the JSON, then continue.
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This keeps schema, taste, and query planning from competing in one large prompt.
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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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| **Memories** | markdown files registered by the agent after user approval | Prior conclusions linked to source sessions/messages and optional anchors |
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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.
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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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│ ├── schema.sql # Executable SQLite schema
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│ └── runtime.mjs # Indexer + query runtime (zero deps)
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└── references/
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├── schema.md # Full table schema + advanced API reference
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├── query-patterns.md # Copyable retrieval recipes
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├── retrieval-semantics.md # Query design frame for retrieval semantics
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├── recap-patterns.md # Compatibility pointer to references/recap/overview.md
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├── recap-writing.md # Compatibility pointer to per-card recap writing docs
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├── recap/
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│ ├── overview.md
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│ ├── pattern1-cover.md
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│ ├── writing1-cover.md
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│ ├── pattern2-thinking.md
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│ ├── writing2-thinking.md
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│ ├── pattern3-vibe.md
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│ ├── writing3-vibe.md
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│ ├── pattern4-workflow.md
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│ ├── writing4-workflow.md
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│ ├── pattern5-closing.md
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│ └── writing5-closing.md
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└── pitfalls.md # Scope, FTS, ordering, and compactness traps
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```
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## Implementation Notes
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The index rebuilds incrementally — only new or modified JSONL files are re-parsed.
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When the optional app is running, it is the active indexer: it watches Claude
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project files, builds in a worker thread, writes `__app_heartbeat__` plus
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`__app_last_successful_build__` into `index_state`, and the skill-side lazy
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build skips work only while both markers are fresh.
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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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