high-quality-info-sources
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 for trusted sources, make a watchlist of people/sites/accounts to follow, filter noisy sources into a smaller high-signal set, turn a link dump into a reusable monitoring system, or design a repeatable workflow for tracking official accounts, researchers, critics, and market signals.
Best use case
high-quality-info-sources is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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 for trusted sources, make a watchlist of people/sites/accounts to follow, filter noisy sources into a smaller high-signal set, turn a link dump into a reusable monitoring system, or design a repeatable workflow for tracking official accounts, researchers, critics, and market signals.
Teams using high-quality-info-sources should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/high-quality-info-sources/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How high-quality-info-sources Compares
| Feature / Agent | high-quality-info-sources | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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 for trusted sources, make a watchlist of people/sites/accounts to follow, filter noisy sources into a smaller high-signal set, turn a link dump into a reusable monitoring system, or design a repeatable workflow for tracking official accounts, researchers, critics, and market signals.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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.
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