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.
npx skills add jnPiyush/AgentX --skill brand-spec-extractiongit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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/jnpiyush/agentx/brand-spec-extraction)<a href="https://agentmods.dev/skills/jnpiyush/agentx/brand-spec-extraction"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/brand-spec-extraction/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/jnpiyush/agentx/brand-spec-extraction"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/brand-spec-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00065 | $0.01862 |
| Opus 5.5 | $0.00026 | $0.00745 |
| Sonnet 5.5 | $0.00013 | $0.00372 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
brand-spec-extraction 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 1mo 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Spec Extraction
WHEN: A user references an existing brand (URL, screenshot, deck, PDF, app, or "match the look of X"). Run BEFORE picking a visual direction or writing any prototype CSS. The output is a single artifact at
docs/ux/brand-spec-<issue>.mdthat every subsequent design step cites.
A design choice that contradicts the extracted brand-spec must be flagged as a finding, not silently overridden. The spec is the source of truth.
When to Use This Skill
- User says "make it match our website at example.com"
- User attaches a screenshot, deck, PDF, or Figma export of an existing brand
- The product is an extension of an existing app with established UI
- An issue carries
needs:brand-fidelity
The 5-Step Protocol
Step 1 - Locate the source
Identify exactly what authoritative artifact the brand lives in. Order of preference:
- Live website at a stable URL.
- Brand-guidelines PDF or deck.
- Existing app screenshots.
- Figma file (read-only export if accessible).
- Verbal description from the user (least reliable -- escalate for an artifact before continuing).
Record the URL or filename in the brand-spec under "Sources".
Step 2 - Capture it
Fetch the source. Recommended methods:
- Live URL: use the browser-automation skill to take screenshots at
desktop, tablet, and mobile widths. Save under
docs/ux/brand-spec-<issue>/sources/. - PDF or deck: read with the corresponding plugin (
read-pdf,read-slides). - App screenshots: ingest as provided.
Each captured asset gets a filename like home-desktop-2026-02-04.png.
Step 3 - Grep the visual atoms
Extract concrete values, not impressions. Use one of:
- DOM inspection on the live URL: read
getComputedStylefor hero, body, primary CTA, secondary CTA. Recordcolor,background-color,font-family,font-size,font-weight,line-height,border-radius,box-shadow. - Screenshot color-pick: sample at least three hero pixels per surface.
- PDF or deck: read declared spec pages.
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.
- 1mo ago Changed 78a2f9feaa23
- 1mo ago First seen · 204 lines · 65 tokens per session scan A f6e3648dd60b
brand-spec-extraction is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 65 tokens to every session and 1,862 once invoked, about $0.0003 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 2026-09-03.
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