obelisk/skill-doc/references/query-patterns.md

Obelisk Query Patterns

These are copyable CodeAct patterns for obelisk --query scripts plus --attune memory mutation patterns. They are not new APIs. Adapt them to the user's scope and return compact evidence.

Read this before the first query for broad synthesis, progress summaries, design history, weekly/monthly reviews, or questions that ask what the user did, learned, decided, tried, or abandoned. Start with a helper-first pass; use raw sql() only when the helper surface cannot express the needed join or aggregation.

First Pass: Overview + Recall + Evidence

Use this for broad synthesis before writing custom SQL. It gives the agent a map, prior notes, and raw session evidence in one bounded result. Then run a faceted detail pass if the first pass reveals useful projects, sessions, files, or terms.

const topic = 'English topic terms translated from the user request';
const map = overview({ limit: 6 });
const project = map.current.project?.project;
const scoped = project ? { project } : {};

return {
  query_plan: {
    mode: 'first_pass',
    topic,
    project: project || null,
    limits: { sessions: 6, memories: 5, search: 8 },
  },
  orientation: map.current_project && {
    project: map.current_project.project,
    session_total: map.current_project.session_total,
    sessions: map.current_project.sessions.map(s => ({
      id: s.id,
      title: s.title,
      branch: s.git_branch,
      ended_at: s.ended_at,
    })),
    memory_total: map.current_project.memory_total,
    memories: map.current_project.memories.map(m => ({
      id: m.id,
      path: m.path,
      anchors: m.anchors,
      summary: m.summary?.slice(0, 240),
    })),
  },
  prior_memories: memories({ ...scoped, query: topic, limit: 5 }).map(m => ({
    id: m.id,
    path: m.path,
    anchors: m.anchors,
    session_id: m.session_id,
    created_at: m.created_at,
    rank: m.rank,
    summary: m.summary?.slice(0, 260),
  })),
  session_evidence: search(topic.replace(/[-_]/g, ' '), { ...scoped, 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),
    })),
};

Orient Before Retrieval

Use overview() when the current project or available scopes are unclear. Treat the result as a map, not evidence; follow up with memories(), search(), helpers, or sql() for facts.

const map = overview({ limit: 6 });
return {
  current: map.current,
  current_project: map.current_project && {
    project: map.current_project.project,
    session_total: map.current_project.session_total,
    sessions: map.current_project.sessions.map(s => ({
      id: s.id,
      title: s.title,
      branch: s.git_branch,
      ended_at: s.ended_at,
    })),
    memory_total: map.current_project.memory_total,
    memories: map.current_project.memories.map(m => ({
      id: m.id,
      path: m.path,
      anchors: m.anchors,
      summary: m.summary?.slice(0, 240),
    })),
  },
  projects: map.projects.slice(0, 8),
  totals: map.totals,
};

Bounded Search To Context

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

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. Memory query terms are English even when the user asks in another language.

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,
  anchors: m.anchors,
  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),
  rank: m.rank,
}));

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,
};

Attune 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 obelisk --attune <script>. The --attune runtime exposes only remember() and forget(), not retrieval helpers.

return remember({
  path: '.obelisk/memories/memory-layer-design.md',
  session_id: 'source-session-id',
  message_start: 'first-message-uuid',
  message_end: 'last-message-uuid',
  anchors: [{ kind: 'file', path: 'SKILL.md' }],
  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(' '),
});

Forget Approved Memory

Use this only after the user asks to archive an outdated or wrong memory. Identify the exact memory ID in a normal --query script first. If one candidate clearly matches the user's request, that request is approval to archive it; if several candidates match, ask which one to forget.

Run the mutation with obelisk --attune <script>:

return forget({
  id: 'mem-id-to-delete',
  reason: 'Outdated by newer project guidance.',
});

forget() archives the record. Active recall through memories() will omit it, and the markdown file at path is left in place.

Update Approved Memory

Use this when the user explicitly corrects an existing memory, or after the agent proposes a replacement and the user approves. An update is one combined operation: archive the old record and register the replacement markdown file. The new markdown file must already exist before running --attune.

const archived = forget({
  id: 'old-memory-id',
  reason: 'Replaced by updated memory from the current session.',
});

const created = remember({
  path: '.obelisk/memories/updated-memory.md',
  session_id: 'current-session-id',
  message_start: 'first-message-uuid',
  message_end: 'last-message-uuid',
  anchors: [{ kind: 'file', path: 'src/path/to/file.ts' }],
  summary: 'Updated summary: concise English retrieval surface for the replacement memory.',
});

return { archived, created };

If the agent only suspects a memory is stale, do not run this pattern yet. Answer from current evidence and ask whether to archive or replace the memory.

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.

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.

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.

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.

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:

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.

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.

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.

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.

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.

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.

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.

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),
};