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competitor-scan

competitor-scan is a skill for Claude Code from naveedharri/benai-skills. It costs 135 tokens per session (996 once invoked), scanned A, original, MIT.

A setup guide for a weekly competitor dashboard that tracks selected rivals across platforms such as YouTube, social networks, communities, and search results.

In plain words
What is it for?
Use it to gather the business niche and competitor choices, optionally research additional rivals, select platforms, and create the dashboard configuration. A competitor dashboard is a recurring view of how other businesses are performing.
Why use it?
It turns an unclear request to monitor competitors into a defined list of rivals, channels, branding, schedule, and delivery location.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Part of the benai-marketing plugin — 13 skills shipped together

not rated 78repo +2 28d ago A scan SkillSpector: warn 135 tokens original MIT

Good fit Use it to gather the business niche and competitor choices, optionally research additional rivals, select platforms, and create the dashboard configuration. A competitor dashboard is a recurring view of how other businesses are performing.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/naveedharri/benai-skills/competitor-scan
View source ↗ naveedharri/benai-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add naveedharri/benai-skills --skill competitor-scan
Clone the repo
git clone --depth 1 https://github.com/naveedharri/benai-skills

Made for: Claude Code.

Or install benai-marketing, the plugin that ships this one along with the rest of its 13 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for competitor-scan

README.md
[![agentmods](https://agentmods.dev/badge/skills/naveedharri/benai-skills/competitor-scan/github.svg)](https://agentmods.dev/skills/naveedharri/benai-skills/competitor-scan)
Your own site
<a href="https://agentmods.dev/skills/naveedharri/benai-skills/competitor-scan"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/competitor-scan/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for competitor-scan

Your own site · 80×15
<a href="https://agentmods.dev/skills/naveedharri/benai-skills/competitor-scan"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/competitor-scan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 135 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 996 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Memory Poisoning · line 53
    Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.
    Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00135 $0.00996
Opus 5.5 $0.00054 $0.00398
Sonnet 5.5 $0.00027 $0.00199
Haiku 4.5 $0.00014 $0.00100

Measured 2mo ago against content hash 41f932af3398, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

competitor-scan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/benai-marketing/skills/competitor-scan/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Competitor Radar Setup

Turns "I want to track my competitors" into a live, self-refreshing, branded dashboard in one guided session. This skill sets up the machine; the competitor-radar skill is the machine.

Step 1: Discovery Q&A (interactive)

Ask, one topic at a time, adapting to answers:

  1. Niche / what they do (so competitor discovery and framing are accurate).
  2. Competitors, three modes:
    • They name them, or
    • They ask you to find and recommend competitors: search YouTube, the web, and their niche communities, propose 5-8 with a one-line why each, and let them confirm/trim, or
    • A mix (they name a few, you fill the rest).
  3. Platforms to track: which of YouTube, Instagram, LinkedIn, TikTok, community (Skool/Circle), SEO. Only track what matters to their niche (a local business cares about Google reviews + local SEO; a creator cares about YouTube + shorts platforms).
  4. Brand: colors, fonts, logo. If they have a design system or a site, extract from it; else use sensible defaults and confirm.
  5. Cadence: weekly (default, Monday) or monthly (1st). And where to post it (Slack channel, email).

Write their answers into a config.json shaped like the competitor-radar skill's config (roster + platforms + apify_actors + brand + slack_channel + live_url + deploy_repo).

Step 2: Connect the data sources

Get the scrapers connected before building. See references/data-sources.md for the platform-to-actor mapping, Firecrawl and YouTube setup, and the avatar-inlining rule.

Step 3: Build the branded dashboard

Reuse the competitor-radar skill's assets/template.html + scripts/build_dashboard.py, restyled to their brand (swap the CSS color/font tokens, keep the structure: Demo/Actual tabs, per-platform columns, expand cards, focus/blur toggle, week-over-week deltas). Gather the first week of real data via the connectors, write radar_data.js, run the build, and open it for their approval before deploying. Inline avatars per the rule in references/data-sources.md.

Read the full file on GitHub · 56 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 56 lines · 135 tokens per session scan A 41f932af3398

Subscribe to this mod's changes

competitor-scan is a skill published in the GitHub repository naveedharri/benai-skills (78 stars, last pushed 28d ago), licensed MIT. It adds 135 tokens to every session and 996 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.

Analytics and metricsClaude CodeMarketing and SEOagent-skillsaianthropicclaudeclaude-plugins
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