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

Two sides of the same index

Obelisk has two sides that share one SQLite index:

Skill side — a Claude Code skill that lets the agent search and query its own session history. The agent writes JS queries, runs them locally, answers in plain language.

App side — an Electron desktop app for humans to browse sessions, manage memories, view usage stats, and see weekly recap cards.

Both read from the same ~/.claude/obelisk.sqlite database. The skill indexes; the app watches and renders.

Skill: agent-first retrieval

/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 recap this week

Install

npx skills add tommy0103/obelisk

Or manually: copy the skill into .claude/skills/obelisk/.

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 in natural language

Core API: search(), context(), sql(), plus structured helpers (sessions, memories, summaries, workflows, failures, fileHistory, etc).

Memory layer

When a retrieval produces a conclusion worth keeping, the agent proposes a markdown memory file. After user approval, it registers the file with runtime.mjs --remember. Memories are recalled via memories() in future sessions — a synthesis cache, not a replacement for raw evidence.

App: A surface for human

A companion desktop app for browsing what the skill indexes.

• Sessions — browse all sessions with search, project filtering, readable tool calls (diffs, terminal output, file viewers)
• Memory — list and detail views for registered memory files
• Activity — GitHub-style heatmap, weekly/cumulative token charts
• Recap — shareable weekly/monthly recap cards with archetype theming
• Settings — data source configuration, auto-refresh, rebuild index

macOS only. Download from Releases.

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
Subagents	subagents/agent-<id>.jsonl	Agent type, description, full conversation
Workflows	workflows/wf_<runId>.json	Script, result, agent count
Workflow agents	subagents/workflows/wf_<runId>/	Per-agent transcripts
Memories	registered markdown files	Conclusions linked to source sessions

Full-text search via FTS5 covers all layers.

Structure

scripts/              # Skill runtime (zero npm deps, Node 22 built-in sqlite)
├── schema.sql        # Executable SQLite schema
├── runtime.mjs       # Indexer + query runtime
├── db.mjs            # Schema init, migrations
├── indexer.mjs       # JSONL discovery + incremental indexing
└── query.mjs         # Query API (search, sessions, memories, etc)

references/           # Agent-readable docs (progressive disclosure)
├── schema.md
├── query-patterns.md
├── retrieval-semantics.md
├── recap-patterns.md
├── recap/            # Per-card pattern + writing references
└── pitfalls.md

SKILL.md              # Skill definition + API + retrieval strategy

Implementation Notes

• Index rebuilds incrementally — only new/modified JSONL files are re-parsed
• Skill side uses Node 22 built-in node:sqlite; zero npm dependencies
• ~/.obelisk/recap/ watched for new recap JSON files (agent writes, app renders)

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License

MIT @tommy0103