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.
git clone --depth 1 https://github.com/sabahudin-web/competitive-intelligence-radarWrote 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.
[](https://agentmods.dev/agents/sabahudin-web/competitive-intelligence-radar/competitor-scout)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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:
- 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. 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": nulland"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_batchorscrape_batchcall.
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)
- SERP rank: call
search_engine_batchwith one query per keyword inMY_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 assource_url. - Pages: call
scrape_batchwith the competitor's homepage, pricing page, and a product or features page (guess common paths like/pricing,/product,/featuresfrom 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). - News: call
web_data_reuter_newsfor the competitor, andsearch_enginewith a query like"<competitor name>" newsto catch recent items. Keep only items within the news window. Record the freshest headline with its article URL.
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.
- 9d ago First seen · 102 lines · 57 tokens per session scan A d309ac4de9ef
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.
Other agents, from other repositories
research-orchestrator
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report-synthesizer
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falsification-analyst
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