Competitor radar

Skill naveedharri/benai-skills/plugins/benai-marketing/skills/competitor-radar

Install

npx -y skills add naveedharri/benai-skills --skill competitor-radar

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What its author says it does

Copied from the file, not written here

Build and weekly-refresh a branded HTML "Competitor Radar" dashboard that tracks a roster of competitors across YouTube, Instagram, LinkedIn, TikTok, community, and SEO. Use when the user wants to track competitors, monitor competitor socials/engagement/subscribers, build a competitor dashboard, or refresh the weekly competitor report. Gathers real data via connectors (YouTube, Apify, Firecrawl), renders a two-tab (Demo/Actual) dashboard in Ben AI branding, deploys it to a stable Vercel URL via git push, and posts the link to Slack.

SKILL.md

3.0 KB, as published. Nobody here has run it

Competitor Radar

Tracks a fixed roster of competitors weekly and renders one branded HTML dashboard: follower counts, posting cadence, median engagement, week-over-week subscriber growth, standout post of the week, and SEO, per platform. Two tabs: Demo (a sample niche) and Actual (real competitors, with a focus/blur toggle so only "you" shows on camera).

Files

config.json: the stable roster (who to track, handles per platform, platforms to include, Apify actor ids). Edit to add/remove competitors.
radar_data.js: the weekly artifact. WEEK_ENDING, AV (base64 avatars, stable), and DATA ({demo, actual}). The refresh step rewrites this.
assets/template.html: the fixed dashboard shell (CSS, render logic, tabs, focus mode). Never regenerate; carries a single /*__RADAR_DATA__*/ marker.
scripts/build_dashboard.py: deterministic build (template + radar_data.js to index.html). No network.

Weekly refresh workflow

Read config.json (roster) and the current radar_data.js (last week's numbers, needed for week-over-week deltas).
Gather fresh data for each creator per references/data-sources.md. Do NOT fabricate; mark missing as null.
Compute deltas per references/data-sources.md.
Rewrite radar_data.js with the new WEEK_ENDING and refreshed DATA. Handle avatars per references/data-sources.md.
Build: python3 scripts/build_dashboard.py <skill_dir> ../index.html.
Deploy and Slack the live URL per references/deploy.md.

Self-improvement

This skill is never finished. Improve it as you use it.

When the user corrects how a step was done, update the relevant reference file (references/data-sources.md, references/deploy.md) or this SKILL.md so the correction sticks. Do not just fix it for this run.
When a correction is a hard rule ("always X", "never Y"), add it as a permanent rule here.
When the user says an output was genuinely good, save it to references/examples/ so it becomes a model for future runs.
Keep the skill small: when you add something, run the deletion test and cut anything that no longer changes behavior.

Guardrails

YouTube numbers are the reliable core; always real. Never invent socials/SEO: set null and move on.
Never edit the template shell, CSS, or render JS. Only radar_data.js (and config.json when the roster changes).
Ben AI voice, no em dashes.