# Obelisk Query Patterns

These are copyable CodeAct patterns for `runtime.mjs --query` scripts, plus one
`--remember` registration pattern. They are not new APIs. Adapt them to the
user's scope and return compact evidence.

## Bounded Search To Context

Use `search()` to locate candidates, then expand only the strongest hits.

```js
const hits = search('"runtime query"', { project: '%quiet-zero%', limit: 8 });
return hits.slice(0, 5).map(h => {
  const c = context(h.message.uuid);
  return {
    session_id: h.session.id,
    session_title: h.session.title,
    uuid: h.message.uuid,
    timestamp: h.message.timestamp,
    snippet: h.message.text?.slice(0, 240),
    parentChain: (c?.parentChain || []).slice(-3).map(m => ({
      uuid: m.uuid,
      role: m.role,
      snippet: m.text?.slice(0, 120),
    })),
  };
});
```

## Memory Plus Session Evidence

Use this when prior conclusions may exist but the answer still depends on raw
session evidence. Keep memory as prior notes, not final authority; compare it
with session evidence in your final answer when correctness matters.

```js
const project = '%quiet-zero%';
const topic = 'markdown memory layer';
const ftsTopic = topic.replace(/[-_]/g, ' ');

const prior_memories = memories({
  project,
  query: topic,
  limit: 5,
}).map(m => ({
  id: m.id,
  path: m.path,
  session_id: m.session_id,
  message_start: m.message_start,
  message_end: m.message_end,
  created_at: m.created_at,
  summary: m.summary?.slice(0, 260),
}));

const session_evidence = search(ftsTopic, { project, limit: 8 })
  .slice(0, 6)
  .map(h => ({
    session_id: h.session.id,
    session_title: h.session.title,
    uuid: h.message.uuid,
    timestamp: h.message.timestamp,
    snippet: h.message.text?.slice(0, 220),
  }));

return {
  query_plan: {
    project,
    topic,
    memory_limit: 5,
    session_limit: 8,
  },
  prior_memories,
  session_evidence,
};
```

## Register Approved Memory

Use this only after the user approves writing memory and the markdown file
already exists. `remember()` validates the file and stores a normalized absolute
path, so keep the script small and return the registered record.

Run this script with `runtime.mjs --remember <script>`. The `--remember` runtime
exposes only `remember()`, not retrieval helpers.

```js
return remember({
  path: '.obelisk/memories/memory-layer-design.md',
  session_id: 'source-session-id',
  message_start: 'first-message-uuid',
  message_end: 'last-message-uuid',
  summary: [
    'Decision: Obelisk uses one user-facing entry that queries both memory and raw sessions.',
    'Memory records are prior notes and must be identified naturally when they influence an answer.',
    'New memory writes require human confirmation before the markdown file is written and registered.',
  ].join(' '),
});
```

## One-Shot Retrieval For Synthesis

Use this for conclusion, broad history, failure investigation, or file evolution
questions. The goal is to reduce conversation turns: keep intermediate search
results inside the query script, then return only a compact task-local evidence
view. This does not create stored semantic entities; the agent still reads the
evidence and forms the conclusion. Expect 1-2 runtime queries: one broad compact
evidence pass, and optionally one targeted detail pass by stable IDs.

```js
const project = '%quiet-zero%';
const topic = 'obelisk retrieval semantics';
const ftsTopic = topic.replace(/[-_]/g, ' ');
const facets = [
  'summary conclusion',
  'runtime query script',
  'failure problem',
  'file change',
];

const candidates = [];
for (const facet of facets) {
  for (const h of search(`${ftsTopic} ${facet}`, { project, limit: 4 })) {
    candidates.push({
      kind: 'message',
      facet,
      session_id: h.session.id,
      session_title: h.session.title,
      uuid: h.message.uuid,
      timestamp: h.message.timestamp,
      snippet: h.message.text?.slice(0, 220),
    });
  }
}

for (const s of summaries({ project, limit: 8 })) {
  if (/obelisk|retrieval|context|summary/i.test(`${s.content || ''} ${s.session_title || ''}`)) {
    candidates.push({
      kind: 'summary',
      facet: 'summary',
      summary_id: s.id,
      session_id: s.session_id,
      session_title: s.session_title,
      timestamp: s.timestamp,
      snippet: s.content?.slice(0, 240),
    });
  }
}

const seen = new Set();
const evidence = [];
for (const row of candidates.sort((a, b) => String(b.timestamp).localeCompare(String(a.timestamp)))) {
  const key = row.uuid || row.summary_id || `${row.session_id}:${row.timestamp}:${row.facet}`;
  if (seen.has(key)) continue;
  seen.add(key);
  evidence.push(row);
  if (evidence.length >= 16) break;
}

return {
  query_plan: { project, topic, facets, per_facet_limit: 4, max_evidence: 16 },
  evidence,
  omitted: Math.max(0, candidates.length - evidence.length),
};
```

## Learned Faceted Detail Pass

Use this after a broad sweep has identified candidate sessions and vocabulary.
Prefer detail facets learned from the first pass over pulling large session
windows. Fall back to small filtered windows only when the vocabulary is still
unclear, and record that reason in `query_plan`.

```js
const sessionIds = [
  'first-pass-session-id-a',
  'first-pass-session-id-b',
];

const learnedFacets = [
  { facet: 'architecture comparison', terms: ['ultrawork', 'TaskTree', 'parallel'] },
  { facet: 'key judgment', terms: ['ridiculous', 'serial', 'parallel'] },
  { facet: 'merge direction', terms: ['replan', 'merge', 'workflow'] },
  { facet: 'prompt observation', terms: ['prompt', 'guideline', 'skill'] },
];

const rows = [];
for (const { facet, terms } of learnedFacets) {
  const clauses = terms.map(() => 'm.text LIKE ?').join(' OR ');
  const params = [
    ...sessionIds,
    ...terms.map(t => `%${t}%`),
  ];
  rows.push(...sql(`
    SELECT
      ? AS facet,
      m.uuid,
      m.session_id,
      s.title AS session_title,
      m.timestamp,
      substr(m.text, 1, 220) AS snippet
    FROM messages m
    JOIN sessions s ON s.id = m.session_id
    WHERE m.session_id IN (${sessionIds.map(() => '?').join(',')})
      AND m.text IS NOT NULL
      AND (${clauses})
    ORDER BY m.timestamp
    LIMIT 3
  `, facet, ...params));
}

const seen = new Set();
const evidence = [];
for (const row of rows) {
  if (seen.has(row.uuid)) continue;
  seen.add(row.uuid);
  evidence.push(row);
  if (evidence.length >= 12) break;
}

return {
  query_plan: {
    mode: 'learned_faceted_detail',
    source: 'terms discovered in first pass',
    session_count: sessionIds.length,
    facets: learnedFacets.map(f => f.facet),
    per_facet_limit: 3,
  },
  evidence,
};
```

## Facet Sweep For Broad History

Use this only for broad synthesis questions such as "how did X evolve", "what
did we do on X", or "what problems happened". Do not use it for concept recall,
exact session lookup, exact term recall, or tasks that ask for compact search
hits.

Keep the sweep small: 3-4 facets, `limit: 3` per facet, and at most 12 compact
evidence rows.

```js
const name = 'obelisk';
const facets = [
  'runtime CLI script',
  'schema SQLite FTS',
  'skill API helper docs',
  'test failure problem',
];

const rows = [];
for (const facet of facets) {
  for (const h of search(`${name} ${facet}`, { project: '%quiet-zero%', limit: 3 })) {
    rows.push({ facet, h });
  }
}

const seen = new Set();
return rows
  .filter(({ h }) => {
    const key = h.message.uuid || `${h.session.id}:${h.message.timestamp}`;
    if (seen.has(key)) return false;
    seen.add(key);
    return true;
  })
  .slice(0, 12)
  .map(({ facet, h }) => ({
    facet,
    session_id: h.session.id,
    session_title: h.session.title,
    project: h.session.project,
    uuid: h.message.uuid,
    timestamp: h.message.timestamp,
    snippet: h.message.text?.slice(0, 180),
  }));
```

## Summary Rows And Neighbors

Use `source`, `content`, `session_id`, `project`, and `session_title`.

```js
const rows = summaries({ project: '%quiet-zero%', limit: 8 });
return rows.map(s => ({
  id: s.id,
  session_id: s.session_id,
  session_title: s.session_title,
  project: s.project,
  source: s.source,
  timestamp: s.timestamp,
  snippet: s.content?.slice(0, 240),
}));
```

To inspect messages around one summary:

```js
const s = summaries({ project: '%quiet-zero%', limit: 1 })[0];
if (!s) return { results: [] };
const before = sql(
  `SELECT uuid, role, timestamp, substr(text,1,200) AS snippet
   FROM messages
   WHERE session_id=? AND timestamp<?
   ORDER BY timestamp DESC LIMIT 3`,
  s.session_id,
  s.timestamp
);
const after = sql(
  `SELECT uuid, role, timestamp, substr(text,1,200) AS snippet
   FROM messages
   WHERE session_id=? AND timestamp>?
   ORDER BY timestamp ASC LIMIT 3`,
  s.session_id,
  s.timestamp
);
return { summary: s, before, after };
```

## File History Synthesis

`fileHistory()` contains reads as well as writes and old-to-new rows. For
"why/how did this file change", scan a bounded `Edit`/`Write` set first, then
return only compact evidence. Do not return 20 long snippets; keep runtime JSON
small enough that the final answer, not the query output, carries the prose.

```js
const rows = fileHistory('/absolute/path/to/file', { limit: 80 });
const writes = rows.filter(r => ['Edit', 'Write'].includes(r.toolCall?.name));
const reads = rows.filter(r => r.toolCall?.name === 'Read');
const targetTerms = ['summaries', 'failures', 'raw'];

const bySession = new Map();
for (const r of writes) {
  let input = {};
  try { input = JSON.parse(r.toolCall.input_json || '{}'); } catch {}
  const delta = String(input.new_string || input.content || input.old_string || '');
  const snippet = delta.slice(0, 220);
  const sid = r.session.id;
  const group = bySession.get(sid) || {
    session_id: sid,
    session_title: r.session.title,
    project: r.session.project,
    write_edit_count: 0,
    first_timestamp: r.timestamp,
    last_timestamp: r.timestamp,
    evidence: [],
  };
  group.write_edit_count++;
  group.first_timestamp = group.first_timestamp < r.timestamp ? group.first_timestamp : r.timestamp;
  group.last_timestamp = group.last_timestamp > r.timestamp ? group.last_timestamp : r.timestamp;
  if (group.evidence.length < 2) {
    group.evidence.push({
      tool: r.toolCall.name,
      tool_id: r.toolCall.id,
      timestamp: r.timestamp,
      mentions: targetTerms.filter(k => delta.toLowerCase().includes(k)),
      snippet,
    });
  }
  bySession.set(sid, group);
}

const sessions = [...bySession.values()].slice(0, 6);
const returnedEvidence = sessions.reduce((n, s) => n + s.evidence.length, 0);
return {
  counts: { reads: reads.length, writes_edits: writes.length },
  sessions,
  omitted_write_edit_rows: Math.max(0, writes.length - returnedEvidence),
};
```

## Failed Tool Counts

For precise counts, aggregate in SQL. Do not hand-count long result rows in the
final answer.

```js
const counts = sql(`
  SELECT
    tc.name AS tool_name,
    COUNT(*) AS failure_count,
    MAX(m.timestamp) AS last_failure_at
  FROM tool_results tr
  JOIN tool_calls tc ON tc.id = tr.tool_use_id
  JOIN messages m ON m.uuid = tr.message_uuid
  JOIN sessions s ON s.id = tr.session_id
  WHERE tr.is_error = 1
    AND s.project LIKE ?
  GROUP BY tc.name
  ORDER BY failure_count DESC, last_failure_at DESC
  LIMIT 20
`, '%quiet-zero%');

const examples = sql(`
  SELECT
    tr.tool_use_id,
    tc.name AS tool_name,
    m.timestamp,
    s.id AS session_id,
    s.title AS session_title,
    substr(tr.content, 1, 180) AS error_snippet
  FROM tool_results tr
  JOIN tool_calls tc ON tc.id = tr.tool_use_id
  JOIN messages m ON m.uuid = tr.message_uuid
  JOIN sessions s ON s.id = tr.session_id
  WHERE tr.is_error = 1
    AND s.project LIKE ?
  ORDER BY m.timestamp DESC
  LIMIT 8
`, '%quiet-zero%');

return { counts, examples };
```

## Failure Investigation Groups

For questions like "recent failed tool calls", "which tasks failed", or "group
failures by task/session", group structurally and return sparse examples. Use
SQL for counts; treat `failures()` as an evidence helper, not a precise counter.

```js
const project = '%quiet-zero%';

const groups = sql(`
  SELECT
    s.id AS session_id,
    s.title AS session_title,
    s.project,
    COUNT(*) AS failure_count,
    MAX(m.timestamp) AS last_failure_at
  FROM tool_results tr
  JOIN tool_calls tc ON tc.id = tr.tool_use_id
  JOIN messages m ON m.uuid = tr.message_uuid
  JOIN sessions s ON s.id = tr.session_id
  WHERE tr.is_error = 1
    AND s.project LIKE ?
  GROUP BY s.id
  ORDER BY last_failure_at DESC
  LIMIT 10
`, project);

const examples = sql(`
  SELECT
    tr.tool_use_id AS tool_call_id,
    tc.name AS tool_name,
    s.id AS session_id,
    m.timestamp,
    substr(tr.content, 1, 180) AS error_snippet
  FROM tool_results tr
  JOIN tool_calls tc ON tc.id = tr.tool_use_id
  JOIN messages m ON m.uuid = tr.message_uuid
  JOIN sessions s ON s.id = tr.session_id
  WHERE tr.is_error = 1
    AND s.project LIKE ?
  ORDER BY m.timestamp DESC
  LIMIT 12
`, project);

return { groups, examples };
```

## Workflow Tree Compact View

Find the run with `workflows()` under scope, then project `workflowTree()` into
compact fields. Do not return raw `script`, `result_json`, or the full tree.

```js
const runs = workflows({ project: '%quiet-zero%', limit: 30 });
const target = runs.find(w =>
  /session[-_ ]journal/i.test(`${w.workflow_name || ''} ${w.task_id || ''} ${w.run_id || ''}`)
);
if (!target) {
  return {
    found: false,
    candidates: runs.slice(0, 8).map(w => ({
      run_id: w.run_id,
      workflow_name: w.workflow_name,
      timestamp: w.timestamp,
      agent_count: w.agent_count,
    })),
  };
}

const tree = workflowTree(target.run_id);
return {
  run_id: target.run_id,
  workflow_name: target.workflow_name,
  status: tree?.status ?? target.status,
  timestamp: tree?.timestamp ?? target.timestamp,
  agent_count: tree?.agent_count ?? tree?.agents?.length ?? target.agent_count,
  agents: (tree?.agents || []).map(a => ({
    agent_id: a.agent_id,
    phase: a.phase,
    label: a.label,
    state: a.state,
    tokens: a.tokens,
    messageCount: a.messageCount,
  })),
};
```

## Subagent Metadata Recall

Use `subagents()` for metadata. Do not expand transcripts unless the user asks.

```js
const rows = subagents({ project: '%quiet-zero%', limit: 50 });
return rows
  .filter(r => /obelisk/i.test(`${r.description || ''} ${r.agent_type || ''}`))
  .map(r => ({
    agent_id: r.agent_id,
    agent_type: r.agent_type,
    description: r.description,
    session_id: r.session_id,
    messageCount: r.messageCount,
    total_tokens: r.total_tokens,
  }));
```

## Empty Result Without Fallback

If the user asks for an exact sentinel, scoped project, or exact file, an empty
result is valid. Report it; do not broaden automatically.

```js
const needle = 'obelisk-impossible-sentinel-20260602';
const hits = search(`"${needle.replace(/-/g, ' ')}"`, { limit: 10 });
const real = hits.filter(h => {
  const scope = `${h.session?.project || ''} ${h.message?.cwd || ''}`;
  return !/SkillOpt[-/. ]outputs|obelisk_train|obelisk-eval/i.test(scope);
});
return real.map(h => ({
  session_id: h.session.id,
  session_title: h.session.title,
  project: h.session.project,
  uuid: h.message.uuid,
  snippet: h.message.text?.slice(0, 200),
}));
```

## Raw Window

Use `raw()` only after identifying a specific message UUID.

```js
const row = sql(`
  SELECT uuid, length(text) AS indexed_len
  FROM messages
  WHERE length(text) >= 10000
  LIMIT 1
`)[0];
if (!row) return null;
const first = raw(row.uuid, { offset: 0, limit: 4000 });
return {
  uuid: row.uuid,
  indexed_len: row.indexed_len,
  totalLength: first?.totalLength,
  hasMore: first?.hasMore,
  text: first?.text?.slice(0, 500),
};
```