> ## Documentation Index
> Fetch the complete documentation index at: https://www.opendirectory.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# company-radar
> Competitive intelligence orchestrator for opendirectory. Takes company names, researches them across 8+ platforms in parallel, computes a 0-100 heat score, a...
**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.
***
## Install
```bash theme={null}
npx "@opendirectory.dev/skills" install company-radar --target claude
```
```bash theme={null}
npx skills add Varnan-Tech/opendirectory --skill company-radar
```
```bash theme={null}
/plugin marketplace add Varnan-Tech/opendirectory
/plugin install opendirectory-gtm-skills@opendirectory-marketplace
```
1. Copy `https://github.com/Varnan-Tech/opendirectory/tree/main/skills/company-radar` into [download-directory.github.io](https://download-directory.github.io/) to download the ZIP.
2. Open **Claude Desktop** -> **Customize** -> **Skills** -> **+** -> **Upload a skill** and drop the folder containing `SKILL.md`.
[**Install company-radar in Manus AI**](https://manus.im/import-skills?githubUrl=https%3A%2F%2Fgithub.com%2FVarnan-Tech%2Fopendirectory%2Ftree%2Fmain%2Fskills%2Fcompany-radar\&utm_source=opendirectory)
## 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
***
## Files
| File | Purpose |
| - | - |
| `SKILL.md` | Main orchestration skill — entry point for the AI agent |
| `README.md` | Install and usage docs |
| `scripts/heat-score-calc.mjs` | Standalone heat score engine — run on collected signal data |
| `references/heat-score-methodology.md` | Full scoring formulas and examples |
| `references/radar-report-template.md` | Report output structure |
| `references/company-profile-format.md` | Company data schema |
| `references/skill-integration-map.md` | How to call each opendirectory skill for channel data |
***
## 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)
***
## Usage
### Basic Radar Run
```text theme={null}
/company-radar
Companies to track: Vercel, Netlify, Railway
```
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
### Using the Heat Score Script Directly
After collecting signal data, you can run the scorer standalone:
```bash theme={null}
# From the skill directory
node scripts/heat-score-calc.mjs --file signals.json
# Or pipe raw JSON
cat signals.json | node scripts/heat-score-calc.mjs
```
Input is a JSON file with `companies[]` containing signal data. Output is scored JSON with per-dimension breakdowns and auto-generated alerts.
### Quick Heat Score Only
```text theme={null}
/company-radar
Companies to track: Supabase
Mode: heat-score-only
```
Returns just the heat score with minimal context -- no full report.
### Recurring Radar
```text theme={null}
/company-radar
Companies: Vercel, Netlify, Railway, Supabase
Schedule: daily
```
Runs the radar and compares scores with the previous run to show deltas.
***
## 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
***
## Heat Score Dimensions
| Dimension | Max | Measures |
| - | - | - |
| Authority | 25 | GitHub stars/forks, Product Hunt validation |
| Shipping | 25 | Commit velocity, release frequency, recency |
| Social | 25 | Twitter, Reddit, HN, YouTube activity |
| Growth | 25 | Hiring volume, sentiment, traction indicators |
Total: **0-100**
Activity levels: **High** (60+), **Medium** (30-59), **Low** (1-29), **Dormant** (0)
***
## Customization
### 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
### 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.
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