obelisk/SKILL.md

name, description, allowed-tools

name	description	allowed-tools
obelisk	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.	Read Bash(node:*) Write

obelisk

Search and query Claude Code session history stored in ~/.claude/. Obelisk indexes sessions, messages, tool calls, tool results, summaries, subagents, workflows, workflow agents, parent chains, and raw JSONL lines into SQLite + FTS5.

Obelisk is a CodeAct memory layer: write a small JS query, run it locally, read the JSON, then answer. Do not turn history into a flat document or browse entire sessions by default.

Quick Start

The skill directory is provided as $SKILL_DIR at invocation time.

Fast keyword search:

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

Custom query:

1. Write a bounded JS query to a temp file, for example /tmp/q.mjs.

2. Run:

   node $SKILL_DIR/scripts/runtime.mjs --query /tmp/q.mjs
   

3. Parse JSON stdout and answer with concise evidence.

The query file runs inside (async () => { ... })(). Use return to emit JSON.

Query Routing

Before writing a query, classify the task. Progressive disclosure is useful, but skipping the relevant reference usually costs extra query rounds.

• Read references/schema.md before raw sql() unless the needed table/column relationship is already explicit here. Do this before running the SQL, not after a missing-column error.
• Read references/query-patterns.md before broad "how did X evolve / what did we do / what problems happened / what was the conclusion" synthesis, and for one-shot synthesis retrieval, workflow trees, failed tool counts or failure groups, file history synthesis, summary neighbors, subagent recall, raw windows, and empty-result handling.
• Read references/pitfalls.md when a scoped result is empty or tiny, when a query may over-fetch, when a term is hyphenated, when project scope is ambiguous, or when helper row fields are unclear.

If a helper row shape is unclear, first run a tiny scoped query and return Object.keys(row) or a compact sample. Do not invent field names.

Core API

search(text, opts?)

Full-text search across main messages, subagent messages, and workflow-agent messages.

Returns:

[{ message: { uuid, text, role, timestamp, model, cwd },
   session: { id, title, project, started_at },
   rank,
   context }]

context here means temporal neighbors: nearby messages in the same session by timestamp. It is not the parent chain. Use context(uuid) or trace(uuid) for causal/parent-chain context.

Opts: { limit, sessionId, project, after, before, cwd }.

project is a SQL LIKE filter over sessions.project, not an exact project identity. Results are already ordered by FTS5 rank; lower rank sorts earlier. Prefer returned order over manually interpreting numeric rank unless you are deliberately using FTS5 semantics.

context(uuid)

Returns the full story around one indexed message:

{ message, parentChain, session, subagent, workflow }

Use this after search() finds a promising message. It is the usual way to expand vertically from one evidence point without dumping the whole session.

sql(query, ...params)

Raw SQL with ? placeholders. Returns array rows.

Before writing non-trivial SQL, read references/schema.md. Common safe joins:

• tool_calls does not have timestamps. Join messages m ON m.uuid = tc.message_uuid.
• tool_results does not have timestamps. Join messages m ON m.uuid = tr.message_uuid.
• For project/session filters, join sessions s ON s.id = <table>.session_id.
• Prefer SQL-side GROUP BY, COUNT, MAX, ORDER BY, and LIMIT over hand-counting in the final answer.

Tables: sessions, messages, tool_calls, tool_results, summaries, subagents, workflows, workflow_agents, messages_fts.

Structured Helpers

These helpers are convenience accessors over the same SQLite structure. They do not replace sql(); use sql() when you need an exact aggregation or a join the helper does not expose.

All list helpers accept a bounded limit. Many also accept: { project, after, before, sessionId, sessions, branch }. Check the schema or a tiny sample before relying on less common filters.

• sessions(opts?) -- session rows, newest first. project is a SQL LIKE pattern.
• recent(n?) -- shorthand for recent sessions.
• summaries(opts?) -- summary rows, newest first: { id, session_id, timestamp, source, content, session_title, project }.
• subagents(opts?) -- subagent metadata plus messageCount.
• workflows(opts?) -- workflow runs, newest first.
• workflowTree(runId) -- workflow row plus parsed result and agents; may include bulky script and result_json, so project compact fields.
• fileHistory(filePath, opts?) -- Read/Edit/Write tool calls for a file, oldest first; includes many Read rows.
• failures(opts?) -- failed tool results with tool/session context, newest first.
• trace(uuid) -- parent chain from root to message.
• thread(sessionId) -- full session messages; last resort only.
• raw(uuid, opts?) -- windowed access to the original JSONL line.

Retrieval Contract

Keep queries scoped, bounded, and structural.

• Preserve explicit project/session/file/time scopes. Empty or tiny scoped results are real results; do not broaden unless the user asks.
• Treat project scope as three distinct semantics: exact sessions.project slug, exact sessions.project_path, or fuzzy LIKE search. Use sql() for exact slug/path membership; helper project means fuzzy LIKE.
• Start with cheap locators: sessions(), summaries(), search(), or a small SQL query.
• Expand incrementally with context(), trace(), neighbor SQL, or raw() windows.
• For conclusion, broad history, failure investigation, or file evolution questions, prefer one bounded query script that locates, expands, dedupes, groups, and returns compact evidence rows. Do not spend multiple conversation turns showing intermediate query results.
• Return compact evidence with stable IDs (session_id, uuid, tool_call_id, run_id, agent_id) and short snippets.
• Avoid thread() unless the user explicitly asks for a full transcript or all smaller probes are insufficient.
• Keep runtime JSON small, ideally under 10k-12k chars for synthesis tasks. Do not return all sessions, all summaries, all tool calls, complete workflow trees, full raw messages, or whole tool results.
• When counting or aggregating, compute counts in SQL or in the query script and return those counts. Do not hand-count from long rows in prose.
• For recent failures or "which tasks failed" questions, aggregate by session/task and return counts plus sparse examples. Do not return raw failure rows.
• For broad "how did X evolve / what did we do / what problems happened" history synthesis, use a bounded facet sweep from references/query-patterns.md. For concept recall, session lookup, or exact term recall, keep compact search() first.

High-frequency field contracts:

• summaries() uses source and content, not summary_type or text.
• search().context is temporal neighbor context, not causal or parent-chain context.
• fileHistory() includes Read; filter to Edit/Write for causal change history.
• workflowTree() may expose raw script and result_json; omit them unless the user asks for raw workflow details.
• fileHistory() is ordered oldest first. For recent file changes, use SQL with ORDER BY m.timestamp DESC.
• FTS5 tokenizes hyphens and treats search(text) as raw MATCH syntax. For workflow-script, search the quoted tokenized phrase such as "workflow script" or use SQL LIKE for exact hyphen matching.

Minimal Patterns

Search, then expand one promising hit:

const hits = search('auth fix', { limit: 5 });
if (!hits.length) return [];
return hits.slice(0, 3).map(h => ({
  session_id: h.session.id,
  session_title: h.session.title,
  uuid: h.message.uuid,
  snippet: h.message.text?.slice(0, 240),
}));

Check helper fields before assuming names:

const rows = summaries({ project: '%quiet-zero%', limit: 1 });
return rows.length ? Object.keys(rows[0]) : [];

Fetch message neighbors without a full thread:

const hit = search('runtime query', { limit: 1 })[0];
return sql(
  `SELECT uuid, role, timestamp, substr(text,1,240) AS snippet
   FROM messages
   WHERE session_id=? AND timestamp>=?
   ORDER BY timestamp LIMIT 6`,
  hit.session.id,
  hit.message.timestamp
);

See references/query-patterns.md for longer recipes.

Notes

• First run builds the index. Later runs update incrementally.
• DB location: ~/.claude/obelisk.sqlite.
• Query scripts run in a sandboxed VM with no filesystem or network access from inside the script.
• Indexed text and stored tool inputs/results are truncated to 10k chars. Use raw(uuid, { offset, limit }) for specific JSONL windows.