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Agent Skill · 研究检索

ocas-sift(奥卡斯筛选)

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。

ocas-sift 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 15,828。

总安装 7,579周安装 319GitHub Stars 公开资料未说明下载 2,654核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.3.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install ocas-sift;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ocas-sift 怎么安装?

在终端执行 openclaw skills install ocas-sift。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ocas-sift 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ocas-sift 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

ocas-sift 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

ocas-sift 用的人多吗?

公开数据是累计安装 7,579 次,近一周安装 319 次,下载 2,654 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ocas-sift 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
ocas-sift

奥卡斯筛选 · 本页

7,579—调用外部服务MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

OpenClaw

82.44%
按下载量换算2,188

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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