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ocas-sift奥卡斯筛选

Agent Skill

ocas-sift 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ocas-sift(奥卡斯筛选)
来源仓库:https://github.com/indigokarasu/ocas-sift
安装命令:
openclaw skills install ocas-sift
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ocas-sift

简介

通用研究引擎,支持网络搜索、事实核查和实体提取。

  • 适用于主题调研、信息综合和可信度验证。
  • 安装后调用内置工具链完成多轮检索与分析。
  • 搜索结果受网络环境和 API 配额限制,可能无法覆盖全部信息。
  • 建议结合多个来源交叉验证关键事实。ocas-sift 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
ocas-sift
source
https://github.com/indigokarasu/sift
install
openclaw skill install https://github.com/indigokarasu/sift
description
Use when searching the web, synthesizing research across multiple sources, verifying facts, summarizing documents, or extracting structured entities. The system's general research engine for topic research, web lookups, fact-checking, comparisons, and deep multi-source sessions. Trigger phrases: 'search for', 'look up', 'research this topic', 'fact check', 'compare', 'summarize this', 'what is', 'find information about', 'update sift'. Do not use for person-focused OSINT investigations (use Scout) or image processing (use Look).
metadata
{"openclaw":{"emoji":"🔬"}}

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.

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

~/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:

{
  "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.

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 nameMechanismScheduleCommand
sift:updatecron0 0 * * * (midnight daily)sift.update
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:
   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"
  1. On failure → retry once. If second attempt fails, report the error and stop.
  2. Output exactly: I updated Sift from version {old} to {new}

Visibility

public

Support file map

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

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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external-service

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