Radar Scan — Discovery Catalogue Builder

Radar Scan — Discovery Catalogue Builder

Build and maintain a catalogue of AI tools, features, and techniques from external sources. Runs independently of your personal context.

Arguments

• $ARGUMENTS — Optional:
  • --sources <all|feeds|manual> — Which sources to scan (default: all)
  • --days N — How far back to look in time-based sources (default: 7)

Parse from $ARGUMENTS if provided. Default to --sources all --days 7.

Workflow

Step 1: Check/Create Catalogue Lists

Check if the ai-brain MCP tools are available (try calling get_lists).

If brain MCP is available:

Call get_lists and look for lists with names starting with [Radar]. Create any that are missing:

• [Radar] Inbox — raw links dropped by user for enrichment
• [Radar] Claude Code — features, settings, tips
• [Radar] MCP Ecosystem — servers, plugins, integrations
• [Radar] AI Tools & Techniques — broader tools, prompting, workflows

Use create_list for each missing list.

If brain MCP is unavailable:

Use local JSON file at ~/.claude/radar-catalogue.json. Read it if it exists, or initialize with empty structure.

If ~/.claude/radar-catalogue.json does not exist but ~/.claude/scout-catalogue.json does, rename ~/.claude/scout-catalogue.json to ~/.claude/radar-catalogue.json and use it.

{
  "lists": {
    "[Radar] Inbox": { "items": [] },
    "[Radar] Claude Code": { "items": [] },
    "[Radar] MCP Ecosystem": { "items": [] },
    "[Radar] AI Tools & Techniques": { "items": [] }
  },
  "lastUpdated": null
}

Step 2: Load Existing Catalogue

Load all items from [Radar] lists to build a set of known URLs for deduplication.

Brain mode: Call get_list for each [Radar] list. Collect all item URLs into a set. Local mode: Read from the JSON file.

Step 2.5: Scan Project Dependencies

Run node "${CLAUDE_PLUGIN_ROOT}/bin/workflow-analyzer/dist/cli.js" scan-deps --since ${DAYS} --output /tmp/workflow-analyzer-deps.json. Read the output JSON. If the bundled binary is not available, fall back to npx @flippyhead/workflow-analyzer@latest scan-deps --since ${DAYS} --output /tmp/workflow-analyzer-deps.json.

If the command fails or is not available, log a warning and skip to Step 3 — dependency scanning is additive, not required.

If GITHUB_TOKEN is not set in the environment and the scan-deps output shows rateLimited > 0, print: "GitHub API rate limited — scanned [reposResolved] of [packageCount] dependencies. Set GITHUB_TOKEN for full scanning."

For each entry in the releases array:

1. Read the release.body (release notes) and repoDescription to assess relevance
2. Skip routine releases: patch version bumps, typo fixes, minor dep updates, internal refactors, CI/CD changes, documentation-only releases
3. Catalogue interesting releases: new CLI tools, MCP servers/integrations, AI/agent features, breaking changes, significant new APIs, performance improvements
4. Create catalogue items using the standard enrichment from Step 5, with source: "dependency-changelog" and an additional relevanceHint of "direct dependency"
5. Use the release.url as the item URL for deduplication against existing catalogue

Step 3: Scan Structured Sources

Skip this step if --sources manual was specified.

Limit to 10-15 items per source. If a source fails (timeout, rate limit, format change), log a warning and continue to the next source — never fail the entire run.

When a source fails, print a clear one-line message: "Source [name] unavailable: [reason]. Continuing with remaining sources."

Anthropic changelog/blog: Use WebFetch on https://docs.anthropic.com/en/docs/about-claude/models and https://www.anthropic.com/news to find recent releases and feature announcements. Extract title, URL, and a one-line description for each.

Hacker News: Use WebFetch on the Algolia API:

• https://hn.algolia.com/api/v1/search_by_date?query=claude+code&tags=story&numericFilters=created_at_i>${DAYS_AGO_TIMESTAMP}
• https://hn.algolia.com/api/v1/search_by_date?query=anthropic+mcp&tags=story&numericFilters=created_at_i>${DAYS_AGO_TIMESTAMP}
• https://hn.algolia.com/api/v1/search_by_date?query=ai+agent+tool&tags=story&numericFilters=created_at_i>${DAYS_AGO_TIMESTAMP}

Extract title, URL (use url field, fall back to HN comment URL), and points as a quality signal. Only keep items with 5+ points.

GitHub: Use WebSearch for:

• "new MCP server" site:github.com (last N days)
• "claude code plugin" site:github.com (last N days)

Extract repo name, URL, and description.

YouTube: Use WebSearch for:

• "claude code tutorial" (last N days)
• "anthropic MCP" tutorial (last N days)

Extract video title, URL, and channel name.

For each result across all sources: skip if the URL already exists in the catalogue (deduplication).

Also deduplicate across sources within this run — if the same URL was found by both HN and GitHub in this run, only catalogue it once.

Step 4: Process Inbox Items

Skip this step if --sources feeds was specified.

Brain mode: Call get_list for the [Radar] Inbox list. For each item with status "open":

1. If the item has a URL, use WebFetch to get the page content
2. Summarize what it is and why it might be useful (1-2 sentences) — set as description
3. Classify it (see Step 5 for category/tag schema)
4. Create a new item in the appropriate [Radar] category list using create_list_item with the enriched fields
5. Mark the inbox item as "done" using update_list_item

Local mode: Process items in the Inbox array, move to the appropriate category array.

Step 5: Enrich and Tag

For each new catalogue entry (from Step 3 or Step 4), set the properties field:

{
  "category": "<one of: claude-code, mcp, api, agent-sdk, prompting, tooling, workflow, general-ai>",
  "relevanceHints": ["<free-text tags describing what workflows/goals this helps with>"],
  "source": "<one of: anthropic-changelog, hackernews, github, youtube, manual, dependency-changelog>",
  "discoveredAt": "<ISO date>"
}

Choose category based on the content:

• claude-code — Claude Code features, settings, shortcuts, plugins
• mcp — MCP servers, protocols, integrations
• api — Claude API features, SDK updates
• agent-sdk — Agent building tools and frameworks
• prompting — Prompting techniques, system prompts, skill design
• tooling — Developer tools, CLI utilities, browser extensions
• workflow — Workflow patterns, automation techniques, productivity methods
• general-ai — Broader AI developments, models, research

Choose relevanceHints based on what kinds of work this would help with (e.g., "browser automation", "code review", "testing", "deployment", "project management").

Brain mode: Use create_list_item with url, description, and properties set. Local mode: Append to the appropriate list in the JSON file. Write the file when done.

Step 6: Report

Output a brief summary:

• How many new items were catalogued, by source
• How many inbox items were processed
• How many duplicates were skipped
• The 3-5 most notable new finds (title + one-line description)
• Total catalogue size across all [Radar] lists