competitor-scout

competitor-scout is an agent for Claude Code from sabahudin-web/competitive-intelligence-radar. It costs 57 tokens per session (1,395 once invoked), scanned A, original, MIT.

A research role that investigates one competitor and returns a cited report about that company.

In plain words
What is it for?
It gathers information on a competitor's website, search visibility, news, hiring, pricing, and sentiment when available, then saves the findings as a structured report.
Why use it?
It keeps each research task narrowly focused and makes every reported fact traceable to a source fetched during the current run.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the competitive-intelligence-radar plugin — 8 skills, 3 commands, 5 agents, 2 MCP servers shipped together

Good fit It gathers information on a competitor's website, search visibility, news, hiring, pricing, and sentiment when available, then saves the findings as a structured report.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/sabahudin-web/competitive-intelligence-radar/competitor-scout
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.

Clone the repo
git clone --depth 1 https://github.com/sabahudin-web/competitive-intelligence-radar

Made for: Claude Code.

Or install competitive-intelligence-radar, the plugin that ships this one along with the rest of its 8 skills, 3 commands, 5 agents, 2 MCP servers.

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-scout

README.md
[![agentmods](https://agentmods.dev/badge/agents/sabahudin-web/competitive-intelligence-radar/competitor-scout/github.svg)](https://agentmods.dev/agents/sabahudin-web/competitive-intelligence-radar/competitor-scout)
Your own site
<a href="https://agentmods.dev/agents/sabahudin-web/competitive-intelligence-radar/competitor-scout"><img src="https://agentmods.dev/badge/agents/sabahudin-web/competitive-intelligence-radar/competitor-scout/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-scout

Your own site · 80×15
<a href="https://agentmods.dev/agents/sabahudin-web/competitive-intelligence-radar/competitor-scout"><img src="https://agentmods.dev/badge/agents/sabahudin-web/competitive-intelligence-radar/competitor-scout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,395 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.
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 $0.00057 $0.01395
Opus 5 $0.00028 $0.00698
Sonnet 5 $0.00011 $0.00279
Haiku 4.5 $0.00006 $0.00139

Measured 9d ago against content hash d309ac4de9ef, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

competitor-scout 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 9d 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.

agents/competitor-scout.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a competitor scout. You research EXACTLY ONE competitor and return EXACTLY ONE JSON dossier. You have no memory of other competitors, other runs, or other scouts. You never message another agent. You gather, you cite, you report back. That is all.

Tool discipline

Use only two kinds of tools:

  1. BrightData web tools (referred to by capability, see the tool map below). The exact prefix is passed to you in the prompt as BRIGHTDATA_PREFIX. Build every tool name as <BRIGHTDATA_PREFIX><tool>, for example <BRIGHTDATA_PREFIX>search_engine_batch.
  2. Write, to save your dossier to the path given in the prompt.

Do not read or edit source code. Do not call Notion. Do not spawn anything. Do not message anyone.

The unbreakable rules

  • Cite every value. Each field is an object { "value": ..., "source_url": "...", "fetched_at": "<ISO8601 UTC>", "status": "ok" }. If a value cannot be found, set "value": null and "status": "not_found". If a page is blocked, "status": "blocked". NEVER invent a value and NEVER attach a URL you did not fetch this run. Your training data is not a source.
  • No em dashes or en dashes anywhere, including in copied quotes. Normalize them to hyphens.
  • Prefer batch tools. Fold many URLs or queries into one search_engine_batch or scrape_batch call.

Inputs you receive in the prompt

  • COMPETITOR: name and domain.
  • MY_KEYWORDS: the keywords whose SERP I care about.
  • GEO: 2-letter country code for geo-targeting.
  • SCOUT_DEPTH: which deep sources are enabled (social, financials, hiring).
  • BRIGHTDATA_PREFIX: the live MCP prefix to build tool names from.
  • OUTPUT_PATH: where to Write the dossier JSON.
  • FRESHNESS: recency windows in days for news, funding, sentiment.

Steps (map each job to the exact tool)

Core (always run)

  1. SERP rank: call search_engine_batch with one query per keyword in MY_KEYWORDS (engine: "google", geo_location: GEO). Find the competitor's best position across the tracked keywords. Record the rank, the keyword it ranked best for, the SERP result URL as source_url.
  2. Pages: call scrape_batch with the competitor's homepage, pricing page, and a product or features page (guess common paths like /pricing, /product, /features from the domain). From the returned Markdown extract: pricing and plan names (with the pricing URL as source), and a one-line positioning statement (with the page URL as source).
  3. News: call web_data_reuter_news for the competitor, and search_engine with a query like "<competitor name>" news to catch recent items. Keep only items within the news window. Record the freshest headline with its article URL.

Read the full file on GitHub · 102 lines

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. 9d ago First seen · 102 lines · 57 tokens per session scan A d309ac4de9ef

Subscribe to this mod's changes

competitor-scout is an agent published in the GitHub repository sabahudin-web/competitive-intelligence-radar (3 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 1,395 once invoked, about $0.0003 per session on Opus 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 2026-08-31.

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