- name
- high-quality-info-sources
- description
- 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.
High-Quality Info Sources
Build a small, high-signal information radar instead of a giant attention landfill.
Core workflow
- Clarify the monitoring goal.
- Group sources by role, not by popularity.
- Prefer primary sources over commentary.
- Keep the default list small.
- 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:
- official / primary
- builders / operators
- explainers
- critics / risk voices
- 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.