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 ZachArticulateV/designer-pro-and-seo/plugin install designer-pro-and-seoWrote 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/zacharticulatev/designer-pro-and-seo/design-research)<a href="https://agentmods.dev/skills/zacharticulatev/designer-pro-and-seo/design-research"><img src="https://agentmods.dev/badge/skills/zacharticulatev/designer-pro-and-seo/design-research/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/zacharticulatev/designer-pro-and-seo/design-research"><img src="https://agentmods.dev/badge/skills/zacharticulatev/designer-pro-and-seo/design-research.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 | $0.00124 | $0.01803 |
| Opus 5 | $0.00062 | $0.00901 |
| Sonnet 5 | $0.00025 | $0.00361 |
| Haiku 4.5 | $0.00012 | $0.00180 |
Grade A, and why
design-research 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 12d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
design-research
Family: design Status: Stable
Purpose
The research front-end of an engagement. Given a target domain and a niche, it
captures the target's positioning, identifies top competitors, scores each on key
dimensions, surfaces the white space no one is filling, and produces a markdown or
interactive-HTML report that feeds design-system-gen, design-dimensions,
blast-prompt, and the SEO skills. It is tool-aware: with the Firecrawl MCP it
does deep multi-page scraping; without it, built-in web fetch/search + html-extract
still produce a real competitive read.
Triggers
- "competitive research" / "competitor analysis"
- "research the niche" / "niche research"
- "what are competitors doing"
- "give me a competitive report"
- "client research" / "engagement research"
Inputs
- Target domain
- Industry / vertical
- Geographic scope (local / national / global)
- Number of competitors (default 5)
- Output format: markdown | interactive HTML | both
Steps
- Detect tooling. Check whether the Firecrawl MCP is connected:
(on Windows usepython3 "${CLAUDE_PLUGIN_ROOT}/scripts/workflow/capability_probe.py"pyifpython3is absent; in PowerShell the variable is$env:CLAUDE_PLUGIN_ROOT). It reports whetherFIRECRAWL_API_KEYis set. - Capture the target. Tier 1: Firecrawl-scrape the site for voice, services,
positioning. Tier 2: WebFetch the key pages +
html-extractfor structure/tokens. - Identify competitors. Use WebSearch on the niche + the target's core terms to assemble a competitor set (default 5); let the user confirm/adjust.
- Scan each competitor. Same tier path as step 2, one pass per competitor.
- Score each on the 18-dimension rubric in
data/competitor-rubric.csv(positioning, brand-craft, content-depth, trust-signals, conversion-path, findability). Score every dimension 0–4 against its good/bad anchors and roll up the weighted composite exactly asreferences/design-research/competitor-rubric.mddescribes, so scores compare across competitors. - Find the white space — walk each dimension across the whole set and flag the
ones where every competitor is weak (the rubric's
white_space_testcolumn). Read the craft dimensions with the negative-space/rhythm method inreferences/design-research/white-space-method.md. - Keyword landscape — call
seo-clusterif available; otherwise a WebSearch-based intent grouping. - Compose the report into the user's workspace (see Outputs).
- Report which tier ran and what a full Firecrawl crawl would add.
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.
- 12d ago First seen · 124 lines · 124 tokens per session scan A e07abc8d9253
design-research is a skill published in the GitHub repository ZachArticulateV/designer-pro-and-seo (8 stars, last pushed 2mo ago), licensed MIT. It adds 124 tokens to every session and 1,803 once invoked, about $0.0006 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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