Company Radar

Company Radar

Competitive intelligence orchestrator. Takes company names, runs parallel research across 8+ platforms, scores each on a 0-100 heat scale, and produces a structured radar report with AI briefings.

This is an orchestration skill. It delegates data collection to existing opendirectory micro-skills and coordinates their output --- it doesn't replace them.

Architecture

INPUT: Company name(s) / URL(s)
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  [1. Profile Phase] -- Web research to build company profiles
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  [2. Signal Collection] -- Parallel platform research (8 channels)
       / | | | | | \ \
      GH TW RD HN PH YC WEB MEDIA
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  [3. Scoring Engine] -- 4-dimension heat score (0-100)
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  [4. AI Synthesis] -- Executive briefing generation
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OUTPUT: Radar Report + Per-Company Deep Dives

Signal Channels and Their Opendirectory Mappings

Common Mistakes

Step 1: Setup Check

Check that required API keys are accessible for the channels the user's platform supports:

if [ -z "$TAVILY_API_KEY" ]; then echo "TAVILY_API_KEY: NOT SET -- required for web enrichment"; else echo "TAVILY_API_KEY: configured"; fi
if [ -z "$GITHUB_TOKEN" ]; then echo "GITHUB_TOKEN: not set -- GitHub API rate limited to 60 req/hr"; else echo "GITHUB_TOKEN: configured"; fi

The specific skills being orchestrated have their own API key requirements. Check each skill's SKILL.md for details. Required for full operation:

• TAVILY_API_KEY -- web search and company enrichment (get at app.tavily.com)
• GITHUB_TOKEN -- GitHub API access (get at github.com/settings/tokens)

If TAVILY_API_KEY is missing: stop and tell the user. Without it, company profiling and web enrichment cannot operate.

Step 2: Parse Input

Collect from the conversation:

• companies: list of company names/URLs to track (required, min 1, max 10 per run)
• output_preference: "full report" (default), "alert-only", "briefing-only", or "heat-score-only" (leaderboard table + scores only, no deep dives)
• timeframe: "realtime" (default) or "last-week" or "last-month"

If the user gives a single company name: still run full radar pipeline. Single-company radars are valid -- get the full profile.

If more than 10 companies: tell the user "Maximum 10 companies per radar scan. I'll run the first 10. Let me know if you want to swap any out."

Ask if any companies have specific known handles:

• GitHub org name (if different from company name)
• Twitter handle
• YC batch (if YC company)
• Product Hunt slug

This saves research time. If unknown, the profile phase will discover them.

Step 3: Company Profile Phase

For each company, build a basic profile before running platform research.

Step 3a: Initial Web Enrichment

For each company, run a Tavily search to discover:

[company name] official website twitter github linkedin producthunt yc founders

Extract from search results:

• Domain / website URL
• Description (2-3 sentences)
• Twitter handle (from twitter.com/X.com URLs in results)
• GitHub org (from github.com URLs in results)
• LinkedIn URL
• Product Hunt slug
• YC batch and URL (if applicable)
• Founder names and Twitter handles

Output format: For each company, produce a profile object following references/company-profile-format.md.

Step 3b: Confirm With User

Display the discovered profiles and ask the user to confirm or correct before proceeding.

## Discovered Company Profiles

| Company | Domain | Twitter | GitHub | YC Batch | Founders |
|---|---|---|---|---|---|
| ... | ... | ... | ... | ... | ... |

Correct any incorrect handles before I proceed to signal collection?

Wait for user confirmation. Do not skip this step -- wrong handles produce wrong signals.

Step 4: Parallel Signal Collection

Now run research across all platforms in parallel for all confirmed companies.

Signal Collection Map

For each platform, use the appropriate method. Run ALL platforms simultaneously -- do not sequence them.

GitHub Signal

Use web search (Tavily) to find GitHub org, then search for:

github.com/[org] stars forks commits

Extract:

• Total stars across repos
• Total forks
• Recent commits (last 7 days)
• Recent releases (last 30 days)
• Last push date
• Primary language
• Open issue count

Or call gh-issue-to-demand-signal skill if you want deeper demand signal analysis from GitHub Issues.

Twitter/X Signal

Use twitter-GTM-find-skill or Tavily search:

twitter.com/[handle] site:twitter.com [company] startup

Extract:

• Recent tweet count (last 24h)
• Mention volume
• Founder tweet activity
• Key topics/hashtags

Reddit Signal

Use reddit-icp-monitor approach or Tavily search:

site:reddit.com [company name] [product category]

Extract:

• Mention count
• Post scores (upvotes)
• Sentiment (positive/negative/mixed)
• Key complaints or praises
• Relevant subreddits

Hacker News Signal

Use hackernews-intel approach or HN Algolia API search:

site:news.ycombinator.com [company name]

Extract:

• Story count
• Total points
• Front-page stories
• Key topics

Product Hunt Signal

Use producthunt-launch-kit approach or Tavily search:

site:producthunt.com [company name] products

Extract:

• Recent launches
• Upvote count
• Comments/sentiment
• Launch frequency

YC Jobs Signal

Use yc-intent-radar-skill / yc-jobs-scraper approach or Tavily search:

site:workatastartup.com [company name] OR site:ycombinator.com/companies [company name]

Extract:

• Open job count
• Job titles/roles
• Department breakdown (Engineering, Sales, Marketing, etc.)
• Location/remote status