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High Quality Info Sources

作者 a1437707640-ui · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install high-quality-info-sources
功能描述
Build, curate, score, and maintain high-quality information source lists for AI, technology, business, or any topic. Use when the user asks to create a skill...
使用说明 (SKILL.md)

High-Quality Info Sources

Build a small, high-signal information radar instead of a giant attention landfill.

Core workflow

  1. Clarify the monitoring goal.
  2. Group sources by role, not by popularity.
  3. Prefer primary sources over commentary.
  4. Keep the default list small.
  5. Add a review rule so the list stays useful.

Clarify the monitoring goal

Start by identifying what the user actually wants to track:

  • breaking product/model releases
  • research progress
  • developer ecosystem changes
  • market/industry moves
  • critical or skeptical takes
  • company-specific monitoring

If the user does not specify, assume they want a balanced monitoring set with:

  • official release channels
  • technical interpreters
  • industry operators
  • critics / risk voices

Group by source role

Do not return a flat pile of links unless explicitly requested. Organize sources into roles such as:

  • Official / primary — company accounts, labs, docs, release blogs
  • Builders / operators — founders, engineers, product leads
  • Explainers — people who interpret developments clearly
  • Critics / risk voices — people who stress test hype and assumptions
  • Aggregators — useful only if they add speed or coverage without too much noise

Default ordering:

  1. official / primary
  2. builders / operators
  3. explainers
  4. critics / risk voices
  5. aggregators

Quality filter

Prefer sources that satisfy most of these:

  • close to the event
  • high signal-to-noise ratio
  • technically or operationally informed
  • consistent over time
  • not purely engagement bait
  • useful for decisions, not just amusement

Penalize sources that are:

  • mostly reposting others
  • chronically sensational
  • vague and uncheckable
  • redundant with better primary sources

Output patterns

Choose one of these depending on the request.

1. Small radar list

Use for users who want the minimum viable watchlist.

Format:

  • category
  • source name / handle
  • why it matters
  • what to watch for

Aim for 8-15 sources.

2. Extended source map

Use when the user wants broad coverage.

Format:

  • grouped categories
  • 3-8 entries per category
  • short note on each entry
  • note on which ones are must-watch vs optional

3. Monitoring system

Use when the user wants an operational workflow.

Include:

  • the core source list
  • refresh cadence
  • how to prune the list
  • how to summarize findings into notes / Notion / docs

Maintenance rules

When building a reusable source system, include these rules:

  • keep a core list and an overflow list
  • review monthly or when signal quality drops
  • remove duplicates aggressively
  • cap the default list so attention remains scarce and valuable
  • promote only sources that repeatedly produce useful first-order information

AI-specific default lens

When the user asks for AI information sources and gives no stronger constraint, combine:

  • frontier labs
  • open-source model players
  • infrastructure / hardware players
  • respected technical voices
  • skeptical / governance voices

Read references/ai-sources.md for a starter set and selection logic.

Tone and judgment

Be opinionated. A source list is a filter, not a census.

Prefer:

  • “Follow these 10 first”
  • “These 5 are optional”
  • “This one is noisy but useful for early chatter”

Avoid pretending all sources are equally good.

安全使用建议
This skill is instruction-only and internally coherent: it provides an opinionated workflow and a starter list for building curated source lists and does not request credentials or install code. Before enabling it, review the included references/ai-sources.md to ensure the named organizations and individuals fit your needs and regional context (and to avoid unwanted bias or controversial figures). If you’re concerned about autonomous behavior, keep 'user-invocable' usage or test the skill in a sandboxed chat first and review the outputs for accuracy and balance. If you want different regional or domain coverage, edit the reference list or prompt the agent to produce a tailored list rather than relying on the defaults.
功能分析
Type: OpenClaw Skill Name: high-quality-info-sources Version: 1.0.0 The skill bundle is designed to help an AI agent curate and maintain high-quality information source lists. The instructions in SKILL.md and the reference data in references/ai-sources.md are focused entirely on information organization, filtering logic, and source categorization (e.g., official sources, builders, and critics). There are no indicators of data exfiltration, unauthorized command execution, or malicious prompt injection intended to subvert the agent's behavior.
能力评估
Purpose & Capability
The name and description (curating and maintaining high-quality source lists) match the skill contents: workflow, output patterns, maintenance rules, and a reference starter set. Nothing requested or installed is unrelated to that purpose.
Instruction Scope
SKILL.md confines the agent to source selection, grouping, scoring, and maintenance workflows. It references the included references/ai-sources.md starter list but does not instruct the agent to read unrelated files, access credentials, call external endpoints, or exfiltrate data.
Install Mechanism
No install spec and no code files — instruction-only. There is nothing written to disk or downloaded during install.
Credentials
The skill declares no environment variables, credentials, or config paths. The guidance in SKILL.md likewise does not reference secrets or external service tokens.
Persistence & Privilege
always is false and there is no indication the skill requests persistent agent-wide changes or modifies other skills. Normal autonomous invocation remains possible (platform default) but is not uniquely privileged here.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install high-quality-info-sources
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /high-quality-info-sources 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release with structured guidance for building, curating, and maintaining high-quality information source lists. - Outlines a core workflow: clarify goals, group sources by role, apply quality filters, and maintain focused lists. - Provides output templates for small radar lists, extended source maps, and operational monitoring systems. - Includes clear maintenance best practices to ensure lists stay relevant, high-signal, and manageable over time. - Features an AI-centric default approach and strong emphasis on judgment, role grouping, and opinionated selection.
元数据
Slug high-quality-info-sources
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

High Quality Info Sources 是什么?

Build, curate, score, and maintain high-quality information source lists for AI, technology, business, or any topic. Use when the user asks to create a skill... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 117 次。

如何安装 High Quality Info Sources?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install high-quality-info-sources」即可一键安装,无需额外配置。

High Quality Info Sources 是免费的吗?

是的,High Quality Info Sources 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

High Quality Info Sources 支持哪些平台?

High Quality Info Sources 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 High Quality Info Sources?

由 a1437707640-ui(@a1437707640-ui)开发并维护,当前版本 v1.0.0。

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