From radar
Combined scan + recommend pipeline. Scans external sources for new AI tools and techniques, then matches them against your goals and usage patterns to surface personalized recommendations.
How this skill is triggered — by the user, by Claude, or both
Slash command
/radar:radarThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Combined scan + recommend pipeline. Scans external sources for new AI tools and techniques, then matches them against your goals and usage patterns to surface personalized recommendations.
Combined scan + recommend pipeline. Scans external sources for new AI tools and techniques, then matches them against your goals and usage patterns to surface personalized recommendations.
This is the default entry point. Use /radar-scan or /radar-recommend separately if you need different scheduling cadences (e.g., scan daily, recommend weekly).
$ARGUMENTS — Optional:
--days N — Lookback window for both scan and recommend (default: 7 for scan, 14 for recommend)--sources <all|feeds|manual> — Source filter for scan phase (default: all). "feeds" = structured external sources (Anthropic, HN, GitHub, YouTube, dependency changelogs). "manual" = process user-added inbox items only.--focus <category> — Category filter for recommend phase (claude-code, mcp, api, agent-sdk, prompting, tooling, workflow, general-ai)Parse from $ARGUMENTS if provided.
Execute the full /radar-scan workflow with the --sources and --days arguments.
Print a brief summary of scan results (new items catalogued, notable finds) before proceeding.
Execute the full /radar-recommend workflow with the --days and --focus arguments.
This phase uses the freshly updated catalogue from Phase 1, ensuring recommendations reflect the latest scan.
Output a combined summary:
npx claudepluginhub flippyhead/radar --plugin radarManages AI News Radar: finding high-signal AI/tech sources, adding RSS/OPML/GitHub feeds, checking source health, updating the web UI, GitHub Actions, or GitHub Pages deployment.
Scans Twitter/X feeds from Anthropic/Claude Code team members for actionable insights on features/updates, tracks state, generates reports using browser automation.
Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.