Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add jgerton/brand-toolkit/plugin install brand-toolkitWrote 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/skills/jgerton/brand-toolkit/brand-radar)<a href="https://agentmods.dev/skills/jgerton/brand-toolkit/brand-radar"><img src="https://agentmods.dev/badge/skills/jgerton/brand-toolkit/brand-radar/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/skills/jgerton/brand-toolkit/brand-radar"><img src="https://agentmods.dev/badge/skills/jgerton/brand-toolkit/brand-radar.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 | $0.00062 | $0.00965 |
| Opus 5 | $0.00031 | $0.00483 |
| Sonnet 5 | $0.00012 | $0.00193 |
| Haiku 4.5 | $0.00006 | $0.00097 |
Grade A, and why
brand-radar 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 10d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the brand intelligence radar. You capture, analyze, and log competitive and inspirational brand intelligence.
Step 0: Load Context
- Find and read
brand-brief.md - Load framework references for analysis:
${CLAUDE_PLUGIN_ROOT}/references/frameworks/dunford-positioning.md${CLAUDE_PLUGIN_ROOT}/references/frameworks/nng-voice-dimensions.md${CLAUDE_PLUGIN_ROOT}/references/anti-slop/anti-slop-checklist.md
- Note existing intelligence data
Step 1: Determine Intel Type
Competitor Intel
User provides a competitor URL or name.
- "I saw that [competitor] launched a new feature"
- "Check out example.com, they're in our space"
- "What's [competitor] doing these days?"
Inspiration Intel
User provides a brand they admire (not necessarily a competitor).
- "I love how [brand] does their messaging"
- "Check out this brand's visual identity"
- "I want our voice to feel like [brand]'s"
Market Intel
User shares a market observation.
- "I noticed a trend toward [X] in our space"
- "Lots of competitors are moving to [approach]"
Step 2: Gather Intel
For URLs
Use WebFetch to scrape the site. Analyze:
Quick scan (always):
- Homepage headline and value proposition
- Meta description
- Visual first impression (described)
- Primary CTA
Deep scan (if user requests or this is a key competitor):
- Positioning: what category, what claim, what audience
- Messaging: BrandScript elements visible
- Voice: estimated NN/g dimensions
- Visual: colors, typography, imagery style
- Anti-slop score: how generic vs distinctive is their brand?
For names without URLs
Use WebSearch to find their website and key information, then proceed with URL analysis.
For market observations
Record the observation, search for corroborating evidence, and assess implications for the user's brand.
Step 3: Analyze Through Brand Lenses
For competitor intel, produce a structured analysis:
[Competitor Name] - [URL] Scanned: [date]
Positioning: [What category are they in? What do they claim?] Target audience: [Who are they talking to?] Key differentiator: [What do they say makes them unique?] Voice: [Estimated NN/g scores, personality] Visual: [Color, type, imagery summary] Anti-slop score: [0-6, how many checks their messaging passes]
Strengths: [What they do well] Weaknesses: [Where they're generic or vulnerable] Implications for your brand: [What this means for your positioning/messaging/voice]
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.
- 10d ago First seen · 128 lines · 62 tokens per session scan A 22a5d1ff22fb
brand-radar is a skill published in the GitHub repository jgerton/brand-toolkit (61 stars, last pushed 4mo ago), licensed MIT. It adds 62 tokens to every session and 965 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 2026-08-30.
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