/company-radar

name: company-radar
description: Competitive intelligence orchestrator tracking companies across 8+ platforms (GitHub, Twitter, Reddit, HN, PH, YC Jobs) with heat scores and AI briefings.
author: OpenDirectory
version: 1.0.0

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

Signal Channels and Their Opendirectory Mappings

| Channel | Opendirectory Skill | What It Detects | |---|---|---| | GitHub | `gh-issue-to-demand-signal` + web search | Stars, forks, commits, releases, shipping velocity | | Twitter/X | `twitter-GTM-find-skill` | Tweets, mentions, engagement, founder activity | | Reddit | `reddit-icp-monitor`, `reddit-post-engine` | Community sentiment, pain points, buzz | | Hacker News | `hackernews-intel` | Story mentions, points, front-page signals | | Product Hunt | `producthunt-launch-kit` | Launches, votes, maker activity | | YC Jobs | `yc-intent-radar-skill` / `yc-jobs-scraper` | Job listings, hiring departments, growth signals | | Web / Press | Tavily search + `competitor-pr-finder` | News, product announcements, funding | | Pricing | `pricing-finder` | Pricing changes, tier updates, plan structure | | Market Position | `map-your-market` | ICP, competitor landscape, messaging gaps |

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Common Mistakes

| The agent will want to... | Why that's wrong | |---|---| | Run skills sequentially | All 8 signal channels are independent. Must run in parallel. | | Hallucinate GitHub star counts or hiring numbers | Every data point must trace to a specific search result or skill output. No "approx 500 stars". | | Skip the heat score computation | The radar report requires scored output, not just raw data dump. Heat score is the core differentiator. | | Output incomplete reports because a skill failed | One failing channel does not block the full report. Score what you have, note gaps. | | Use AI training knowledge for company descriptions | Every company description must come from live web research, not memory. | | Forget to score activity levels from heat scores | Heat score has explicit thresholds: High (60+), Medium (30-59), Low (1-29), Dormant (0). |

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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.

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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.

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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 platform