Agent skill · Documentation

intel

Gather and shape intelligence signals from collected feeds (RSS, HackerNews, Lobsters, EDGAR) into audience-aware output. Use when you need current context on a technology, industry trend, or domain — or when you need to present signals to a specific audience. NOT for forward-looking predictions (use forecast); NOT for architecture analysis (use archobs); NOT for writing specs (use spec).

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill intel --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 10 KB
Bundled scripts: none
Path: skills/analysis/intel/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Intel (Intelligence Briefs) ## Overview Produce focused intelligence briefs on a topic by querying the `intel` CLI against locally collected feeds (RSS, HackerNews, Lobsters, EDGAR). Briefs combine trending signals, full-text search hits, and topic breakdowns into a concise, evidence-backed summary an agent or human can act on. Use this skill when you need current signal on a technology, vendor, standard, or industry trend — or when you need to present signals to a specific audience. Success looks like: a brief with ranked signals, source citations, and a clear "so what" tailored for the target audience — readable in under 2 minutes and actionable without needing to parse raw data. ## Prerequisites 1. **Build the tool**: ```bash cd tools/intelligence && npm install && npm run build ``` 2. **Make `intel` available on PATH**: ```bash npm link # from tools/intelligence/ ``` 3. **Create a config file**: ```bash mkdir -p ~/.config/intel ~/.local/share/intel cp config/feeds.example.yaml ~/.config/intel/config.yaml # Edit ~/.config/intel/config.yaml to customize feeds ``` 4. **Seed the database** (first run): ```bash intel collect --once ``` 5. **Install the collector as a background se

What's inside
Steps it walks through
  1. Overview
  2. Prerequisites
  3. Chooser
  4. Content type (what data to gather)
  5. Audience (how to present — default: practitioner)
  6. Clarifying Questions
  7. Workflow
  8. Guardrails
  9. Output Template
  10. Practitioner (default)
  11. Executive
  12. Engineering
  13. Decision
  14. Daily Digest
Ships with 1 file
  • metadata.json
Commands it runs
cd tools/intelligence && npm install && npm run build
npm link          # from tools/intelligence/
mkdir -p ~/.config/intel ~/.local/share/intel
cp config/feeds.example.yaml ~/.config/intel/config.yaml
intel collect --once
launchctl print gui/$(id -u)/com.intel.collector
tail -f ~/Library/Logs/intel-collector.log
systemctl --user status intel-collector
journalctl --user -u intel-collector -f
intel stats
More from claude-skill-registry
All skills →
About this skill
What does the intel skill do?

Gather and shape intelligence signals from collected feeds (RSS, HackerNews, Lobsters, EDGAR) into audience-aware output. Use when you need current context on a technology, industry trend, or domain — or when you need to present signals to a specific audience. NOT for forward-looking predictions (use forecast); NOT for architecture analysis (use archobs); NOT for writing specs (use spec).

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill intel --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going