Ocas Sift

Sift

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.

Load-First Rule for Web-Adjacent Queries

When the user asks about products, prices, reviews, how-to advice, or any information that requires current web data, load Sift FIRST before answering from domain knowledge. This applies even if you think you already know the answer from training data.

Why this matters: On VPS/cloud environments, browser-based search (Google, Bing, DuckDuckGo) is almost entirely blocked by CAPTCHA from datacenter IPs. The web_search and web_extract MCP tools handle basic searches automatically via SearXNG. Load this skill directly for deep research, fact-checking, comparisons, or when the MCP tools' results are insufficient.

If you answer from domain knowledge without checking current sources, you may miss: product availability changes, new products on the market, updated formulations, price changes, or critical caveats (e.g., fabric-specific limitations like synthetic vs. cotton).

Pattern to follow:

1. User asks about a product/recommendation/how-to → load Sift
2. Run SearXNG: curl -s "http://localhost:8888/search?q=QUERY&format=json"
3. Fetch top result URLs with curl -sL or sift.fetch
4. THEN synthesize answer with citations

Exception: Pure factual lookups ("what is the capital of France") that don't require web data can be answered from internal knowledge.

When to use

LOAD SIFT FIRST — before any other action — when:

• The user asks ANY question requiring current web data (not in your training data)
• The user says "search for", "look up", "research", "investigate", "find out", "check if", "look into"
• The user asks about products, prices, reviews, recommendations
• The user asks "what is", "how to", "why does", "when did" about current topics
• The user asks about current events, news, or recent developments
• You need to verify a fact or claim against current sources
• You need to compare products, technologies, or options using current data
• You need to fetch/extract content from a specific URL
• You're about to use the browser for ANY information-seeking purpose
• You're about to use execute_code to scrape a search engine

In short: if you need information you don't already have loaded Sift first.

Specific use cases:

• 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
• Product/price/recommendation research
• Current events and news research

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).

Ontology types

Sift works with these types from spec-ocas-ontology.md:

• Entity/Person, Entity/AI — people and agents identified during research.
• Place — locations, venues, and organizations.
• Concept/Event, Concept/Idea — events, topics, and themes extracted from research.
• Thing/DigitalArtifact — documents, articles, and digital records.

Sift emits Signals to Elephas for entities and relationships extracted with confidence >= med. Signal payload.type is the ontology type of the primary entity. source_journal_type is "Research". Every emitted Signal must include a user_relevance field.

user_relevance field

Every Signal emitted by Sift carries a user_relevance field with one of two values:

• "user" — the signal is relevant to the user's personal knowledge graph
• "agent_only" — the signal is agent-initiated research with no demonstrated user connection

Default is "agent_only" because much of Sift's research may be agent-initiated (scheduled runs, background enrichment, cooperative queries from other skills). A signal receives user_relevance: "user" only when:

Signal example:

{
  "signal_id": "sig-sift-20260402-001",
  "source_skill": "ocas-sift",
  "source_journal_type": "Research",
  "emitted_at": "2026-04-02T14:30:00Z",
  "user_relevance": "agent_only",
  "payload": {
    "type": "Concept/Event",
    "name": "2026 Solar Eclipse",
    "confidence": "high",
    "source_refs": ["https://example.com/eclipse"]
  }
}

Sift may read Thread's active context for query rewriting and Weave's database for entity disambiguation (both cooperative read-only; see spec-ocas-interfaces.md Cooperative Query Interfaces).

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
• sift.fetch [url] — extract clean Markdown content from a URL. Runs Scrapling first (fast HTTP for static sites, headless browser mode for JS-heavy sites); falls back to Jina Reader (r.jina.ai/<url>) if Scrapling output is below content threshold. Returns Markdown with structure preserved. Use for summarizing a specific page or document the user provides. Exit codes: 0 success · 1 permanent error (escalate to donsetch fetch) · 2 transient (retry) · 3 walled (escalate to donsetch fetch or sift.webwright). See references/donsetch-integration.md for the full anti-bot escalation chain.
• donsetch fetch [url] — anti-bot / Cloudflare-blocked fetch via the locally installed donsetch binary (v3.2.3, AGPL, /usr/local/bin/donsetch). Uses real Chrome TLS, solve-and-bounce, and headless escalation. Use when sift.fetch returns exit 1/3, bot-wall / 403 / empty / CAPTCHA on CF, Akamai, DataDome, Imperva. See references/donsetch-integration.md.
• sift.webwright — execute an interactive web task using browser automation (Playwright driving the system Chrome). Requires the playwright package (pip install playwright); it uses the already-installed Chrome via channel="chrome", so no browser download is needed. Write the plan, exploration screenshots, instrumented final_script.py, execution log, and self-verification into {agent_root}/commons/data/ocas-sift/webwright/. For form filling, multi-step flows, JS-heavy sites, interactive filtering, or any task where the browser is the workspace. Read references/webwright-integration.md before first use.

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

All configured search sources fire in parallel. Results are deduplicated by URL and content hash.

• Internal knowledge — LLM knowledge, conversation context, Chronicle if available. Always runs first as a pre-check.
• Free web search (parallel fan-out) — all of the following fire simultaneously:
  • N2 MCP (n2_web_search) — SearXNG-backed, no API key required. Registered during sift.init. Also provides n2_news_search for recency-focused queries. See references/searxng-diagnostics.md for engine-health diagnostics.
  • Brave Search API — structured web results. See references/search_tiers.md for provider configuration and API keys.
  • SearXNG — self-hosted instance on http://localhost:8888. This is the primary search source on VPS environments. Always returns results when browser-based search is CAPTCHA-blocked.
  • Platform search — agent-reach on Twitter/X (via Mirror Rotator → Search Bridge), Reddit, LinkedIn, GitHub, etc.
• Google Custom Search API (CSAPI) — fallback when free web search returns insufficient results. Uses mcp_google_workspace_search_custom. Quota-limited: 1,000 queries/month free tier. Check quota before calling (csapi_quota.py check), increment after (csapi_quota.py increment).

For detailed tier-by-tier workflow, API curl examples, and cloud environment fallbacks, read references/research-workflow.md and references/vps-search-cheat-sheet.md.

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 the signal payload field in the journal entry. Use Signal schema from spec-ocas-shared-schemas.md. Every Signal must include user_relevance (see Ontology types section). Set "user" if the run was user-initiated or the entity connects to a user_relevance: "user" Chronicle entry; otherwise "agent_only".
3. Write journal via sift.journal

sift.fetch behavior

sift.fetch [url] extracts clean Markdown from a specific URL.

Do not use sift.fetch for general search — it fetches a specific known URL only.

Chronicle interaction

Sift never writes directly to Chronicle. It emits enrichment candidates via Signal files.

Inter-skill interfaces

Sift writes Signal files to Elephas (via journal signal payload): the signal payload field in the journal entry.

Pitfalls & Tips

Read references/pitfalls.md for the full list. Key highlights:

• Answer-from-knowledge trap: Don't answer product/how-to questions from training data alone. Use SearXNG + fetch. (See Load-First Rule above.)
• CAPTCHA cascade: From cloud environments most search engines block automated browsers, but not uniformly — measured from this host through the system Chrome, Google's results page serves an "unusual traffic" reCAPTCHA with zero results while Bing answers normally. Prefer SearXNG (localhost:8888) or CSAPI, and if you do drive a browser, check the page for CAPTCHA markers rather than assuming an empty result set means the subject is unfindable.
• Credential sanitizer blocks API key writes: The Hermes output sanitizer intercepts API keys. If CSAPI fails with missing key, the owner must add it manually.

Support file map

Background tasks

Self-update

sift.update pulls the latest package from GitHub. Runs silently.

Visibility

public

Optional skill cooperation

• Elephas — emit Signal files for Chronicle promotion
• Thread — may read recent browsing context for query rewriting
• Weave — may use for entity disambiguation
• Chronicle — may read for entity context
• Look — reverse image search capability