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 prime-radiant-inc/greenfield --skill visual-explorationgit clone --depth 1 https://github.com/prime-radiant-inc/greenfieldWrote 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/prime-radiant-inc/greenfield/visual-exploration)<a href="https://agentmods.dev/skills/prime-radiant-inc/greenfield/visual-exploration"><img src="https://agentmods.dev/badge/skills/prime-radiant-inc/greenfield/visual-exploration/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/prime-radiant-inc/greenfield/visual-exploration"><img src="https://agentmods.dev/badge/skills/prime-radiant-inc/greenfield/visual-exploration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 248 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00049 | $0.03437 |
| Opus 5 | $0.00024 | $0.01718 |
| Sonnet 5 | $0.00010 | $0.00687 |
| Haiku 4.5 | $0.00005 | $0.00344 |
Grade A, and why
visual-exploration 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 — 383 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Exploration Methodology
Document behavioral intelligence through browser automation and visual observation. Every screenshot is an empirical observation. Every interaction sequence is a behavioral flow. This mode captures what the user SEES and what the system DOES in response to user actions.
When to Use This Mode
Visual exploration activates when:
- The target is a web application with a UI
- The discovery inventory identifies a running web UI accessible via HTTP
- A running instance of the target is accessible via HTTP
This mode requires a running instance of the target application and browser automation capabilities (Playwright, Puppeteer, or equivalent). All output is RAW (screenshots and flow documentation capture the target's UI in detail).
Targets without a web UI get no useful signal from this mode — skip it.
Why Visual Exploration Matters
Source code tells you what the system CAN do. Tests tell you what the system MUST do. Visual exploration tells you what the system LOOKS LIKE while doing it. This is behavioral intelligence that no other mode captures:
- Affordances -- what actions does the UI present as available?
- Feedback -- how does the system communicate success, failure, and state changes?
- Navigation structure -- how are features organized from the user's perspective?
- State representation -- how does the UI change as the system's state changes?
- Accessibility -- is the interface usable with keyboard, screen reader, or at different sizes?
Phase 1: Initial Reconnaissance
Goal: Navigate to the root URL, capture the landing page, and identify the application type, auth requirements, and available affordances.
1.1 Landing Page Capture
# Navigate to root URL and screenshot
# (Using Playwright as reference; adapt to available browser automation)
const page = await browser.newPage();
await page.goto(ROOT_URL, { waitUntil: 'networkidle' });
await page.screenshot({ path: 'workspace/raw/runtime/visual/screenshots/001-landing.png', fullPage: true });
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 · 383 lines · 49 tokens per session scan A a917ca2049cd
visual-exploration is a skill published in the GitHub repository prime-radiant-inc/greenfield (273 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 3,437 once invoked, about $0.0002 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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