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Category: GTM Intelligence · Version: 1.0.0 · Author: OpenDirectory · Compatibility: All targets (claude, codex, opencode, gemini, anti-gravity, openclaw, hermes)
Competitive intelligence orchestrator for opendirectory. Takes company names, researches them across 8+ platforms in parallel, computes a 0-100 heat score, and outputs structured radar reports with AI briefings.

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Install

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What It Does

Given a list of companies to track, Company Radar:
  1. Builds a profile for each company (domain, social handles, founders, YC batch)
  2. Scans 8 channels in parallel — GitHub, Twitter/X, Reddit, Hacker News, Product Hunt, YC Jobs, Web/Press, Pricing
  3. Computes a heat score (0-100) across 4 dimensions: Authority, Shipping, Social, Growth
  4. Generates an AI executive briefing per company and a landscape-level summary
  5. Outputs a structured radar report with leaderboard, deep dives, and alerts

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Files

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Prerequisites

  • opencode with the opendirectory skill set installed
  • The following skills available in your opendirectory config:
    • reddit-icp-monitor (optional; falls back to Tavily)
    • twitter-GTM-find-skill (optional; falls back to Tavily)
    • hackernews-intel (optional; falls back to HN Algolia)
    • yc-jobs-scraper (required for YC job signals)
    • producthunt-launch-kit (optional; falls back to Tavily)
    • competitor-pr-finder (optional)
    • map-your-market (optional, for landscape analysis)
  • Tavily API key (for web search fallbacks)
  • GitHub CLI (gh) installed and authenticated (for GitHub signal collection)

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Usage

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Basic Radar Run

The skill walks through all steps: Parse input → Build profiles → Collect signals (parallel) → Heat score via scripts/heat-score-calc.mjs → Generate briefing → Assemble report

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Using the Heat Score Script Directly

After collecting signal data, you can run the scorer standalone:
Input is a JSON file with companies[] containing signal data. Output is scored JSON with per-dimension breakdowns and auto-generated alerts.

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Quick Heat Score Only

Returns just the heat score with minimal context — no full report.

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Recurring Radar

Runs the radar and compares scores with the previous run to show deltas.

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Output

The skill produces a single structured markdown report containing:
  • Executive Summary — landscape-level synthesis, ranked companies, key alerts
  • Heat Score Leaderboard — table of all tracked companies sorted by score
  • Per-Company Deep Dives — full signal detail per channel, heat score breakdown, AI executive briefing
  • Alerts Summary — notable signals grouped by severity
  • Data Quality Notes — which channels returned data per company

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Heat Score Dimensions

Total: 0-100 Activity levels: High (60+), Medium (30-59), Low (1-29), Dormant (0)

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Customization

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Add a New Channel

  1. Add the signal formulas to references/heat-score-methodology.md
  2. Add the skill mapping to references/skill-integration-map.md
  3. Add the data fields to the report template in references/radar-report-template.md
  4. Update the orchestration flow in SKILL.md Step 4

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Adjust Scoring Weights

Edit the formula tables in SKILL.md Steps 5-8 and references/heat-score-methodology.md. Each dimension caps at 25 points max.