Sift

Web search, research synthesis, fact verification, and entity extraction. The system's general research engine. Use for topic research, web lookups, fact-che...

Sift is the system's general research engine, retrieving and synthesizing information from the web across a tiered source hierarchy — internal knowledge first, then free web search, then rate-limited semantic research providers for deep work. It evaluates source reliability through cross-source agreement scoring, extracts structured entities from retrieved content, and emits enrichment candidates to Chronicle so researched knowledge accumulates over time.

When to use

• Web search and research synthesis on any topic
• Fact verification across multiple sources with consensus scoring
• Document summarization and structured entity extraction
• Comparison research across products, technologies, or options
• Deep research sessions with multi-source threading

When not to use

• OSINT investigations on individuals — use Scout
• Image-to-action processing — use Look
• Pattern analysis on the knowledge graph — use Corvus
• Communications and message drafting — use Dispatch

Sift never performs OSINT investigations on individuals. If the primary entity of a query is a person, Scout should be invoked.

Responsibility boundary

Sift owns web research, fact verification, and structured entity extraction.

Sift does not own: person-focused OSINT (Scout), image processing (Look), knowledge graph writes (Elephas), pattern analysis (Corvus), social graph (Weave).

Commands

• sift.search — execute a search query with automatic tier selection and query rewriting
• sift.research — run a multi-source research session producing a structured research journal
• sift.verify — fact-check a specific claim across multiple sources with consensus scoring
• sift.summarize — summarize a document or URL with structured entity extraction
• sift.extract — extract entities, claims, statistics, and relationships from content
• sift.thread.list — list active research threads with entity overlap detection
• sift.status — return current state: active threads, quota usage, source reputation summary
• sift.journal — write journal for the current run; called at end of every run
• sift.update — pull latest from GitHub source; preserves journals and data

Response modes

Sift classifies query depth automatically:

• quick_answer — simple factual lookups, single-source sufficient
• comparison — multi-source comparison with structured output
• research — deep multi-session investigation with threading
• document_analysis — URL or document-focused extraction

Users may override with phrases like "quick answer", "deep dive", "compare", or "summarize".

Search tier selection

• Tier 1 — Internal Knowledge: LLM knowledge, conversation context, Chronicle if available.
• Tier 2 — Free Web Search: Brave Search API, SearXNG, DuckDuckGo. Default for all queries.
• Tier 3 — Semantic Research: Exa, Tavily. Deep research with sparse sources only. Quota-limited.

Read references/search_tiers.md for provider details and escalation criteria.

Source reputation model

Sift maintains per-domain trust scores based on: cross-source agreement, contradiction frequency, historical accuracy, structured data quality, citation frequency.

Structured extraction rules

When pages are retrieved, extract: entities (with type from shared ontology), claims, statistics, relationships, citations. Each extraction includes confidence level.

Extracted entities are emitted as enrichment candidates for Elephas.

Run completion

After every Sift command that produces results:

1. Persist session, entities, sources, and decisions to local JSONL files
2. For each extracted entity or relationship with confidence >= med: write a Signal file to ~/openclaw/db/ocas-elephas/intake/{signal_id}.signal.json. Use Signal schema from spec-ocas-shared-schemas.md.
3. Write journal via sift.journal

Chronicle interaction

Sift never writes directly to Chronicle. It emits enrichment candidates via Signal files to ~/openclaw/db/ocas-elephas/intake/{signal_id}.signal.json. Elephas decides promotion.

Inter-skill interfaces

Sift writes Signal files to Elephas intake: ~/openclaw/db/ocas-elephas/intake/{signal_id}.signal.json

Sift may read from Thread (when present) for recent browsing context to improve query rewriting. This is a cooperative read, not a dependency.

See spec-ocas-interfaces.md for signal format.

Storage layout

text

~/openclaw/data/ocas-sift/
  config.json
  sessions.jsonl
  threads.jsonl
  entities.jsonl
  sources.jsonl
  decisions.jsonl
  reports/

~/openclaw/journals/ocas-sift/
  YYYY-MM-DD/
    {run_id}.json

Default config.json:

json

{
  "skill_id": "ocas-sift",
  "skill_version": "2.3.0",
  "config_version": "1",
  "created_at": "",
  "updated_at": "",
  "search": {
    "default_tier": 2,
    "tier3_daily_limit": 50
  },
  "retention": {
    "days": 30,
    "max_records": 10000
  }
}

OKRs

Universal OKRs from spec-ocas-journal.md apply to all runs.

yaml

skill_okrs:
  - name: source_accuracy
    metric: fraction of extracted facts confirmed by cross-source agreement
    direction: maximize
    target: 0.85
    evaluation_window: 30_runs
  - name: tier3_quota_compliance
    metric: fraction of days where Tier 3 usage stays within daily limit
    direction: maximize
    target: 1.0
    evaluation_window: 30_runs
  - name: entity_extraction_precision
    metric: fraction of extracted entities with valid source reference
    direction: maximize
    target: 0.90
    evaluation_window: 30_runs

Optional skill cooperation

• Elephas — emit Signal files for Chronicle promotion after every extraction
• Thread — may read recent browsing context for query rewriting (cooperative, not required)
• Weave — may use Weave for entity disambiguation
• Chronicle — may read Chronicle (read-only) for entity context

Journal outputs

• Observation Journal — search and extraction runs
• Research Journal — structured multi-source research sessions

Initialization

On first invocation of any Sift command, run sift.init:

1. Create ~/openclaw/data/ocas-sift/ and subdirectories (reports/)
2. Write default config.json with ConfigBase fields if absent
3. Create empty JSONL files: sessions.jsonl, threads.jsonl, entities.jsonl, sources.jsonl, decisions.jsonl
4. Create ~/openclaw/journals/ocas-sift/
5. Ensure ~/openclaw/db/ocas-elephas/intake/ exists (create if missing)
6. Register cron job sift:update if not already present (check openclaw cron list first)
7. Log initialization as a DecisionRecord in decisions.jsonl

Background tasks

Job name	Mechanism	Schedule	Command
sift:update	cron	0 0 * * * (midnight daily)	sift.update

text

openclaw cron add --name sift:update --schedule "0 0 * * *" --command "sift.update" --sessionTarget isolated --lightContext true --timezone America/Los_Angeles

Self-update

sift.update pulls the latest package from the source: URL in this file's frontmatter. Runs silently — no output unless the version changed or an error occurred.

1. Read source: from frontmatter → extract {owner}/{repo} from URL
2. Read local version from skill.json
3. Fetch remote version: gh api "repos/{owner}/{repo}/contents/skill.json" --jq '.content' | base64 -d | python3 -c "import sys,json;print(json.load(sys.stdin)['version'])"
4. If remote version equals local version → stop silently
5. Download and install:

   bash

   TMPDIR=$(mktemp -d)
   gh api "repos/{owner}/{repo}/tarball/main" > "$TMPDIR/archive.tar.gz"
   mkdir "$TMPDIR/extracted"
   tar xzf "$TMPDIR/archive.tar.gz" -C "$TMPDIR/extracted" --strip-components=1
   cp -R "$TMPDIR/extracted/"* ./
   rm -rf "$TMPDIR"
   

6. On failure → retry once. If second attempt fails, report the error and stop.
7. Output exactly: I updated Sift from version {old} to {new}

Visibility

public

Support file map

File	When to read
references/schemas.md	Before creating sessions, threads, or extraction records
references/search_tiers.md	Before tier selection or escalation
references/query_rewrite.md	Before query rewriting
references/journal.md	Before sift.journal; at end of every run