BrowserAct is a browser automation system that lets AI agents use real, separate browser sessions to extract data and complete account-based web tasks. It is for agents and teams that need parallel workflows, reused login states, human handoffs, or access to sites that resist ordinary automated requests.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/browser-act/skillsnpx agentmods add skills/browser-act/skills/walmart-product-reviewsWrote 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/browser-act/skills/walmart-product-reviews)<a href="https://agentmods.dev/skills/browser-act/skills/walmart-product-reviews"><img src="https://agentmods.dev/badge/skills/browser-act/skills/walmart-product-reviews/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/browser-act/skills/walmart-product-reviews"><img src="https://agentmods.dev/badge/skills/browser-act/skills/walmart-product-reviews.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 | $0.00185 | $0.01566 |
| Opus 5 | $0.00093 | $0.00783 |
| Sonnet 5 | $0.00037 | $0.00313 |
| Haiku 4.5 | $0.00018 | $0.00157 |
Grade A, and why
walmart-product-reviews 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 11d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Walmart — Product Reviews
product item ID + page → paginated customer reviews from walmart.com
Language
All process output to user (progress updates, process notifications) follows the user's language.
Objective
Extract paginated customer reviews from a Walmart product reviews page, returning structured review data with ratings, text, author info, and metadata.
Prerequisites
- Target reviews page is open in the browser:
https://www.walmart.com/reviews/product/{item-id}?page={page}
Pre-execution Checks
1. Tool Readiness
If browser-act has been confirmed available in the current session → skip this step.
Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.
Capability Components
This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. Its role is equivalent to copy-pasting on the user's behalf — the data is already on screen, automation merely saves time. JS code is encapsulated in Python files under the
scripts/directory, invoked viaeval "$(python scripts/xxx.py {params})".$(...)is bash syntax; it is recommended to use the bash tool for execution.
Below are all atomic capabilities discovered and verified during the exploration phase, listed by command template with parameters. Simply invoke them as needed — no need to read scripts/*.py source code or re-verify. Only inspect scripts when execution fails for troubleshooting. Combine freely as needed during execution.
DOM: extract reviews from current reviews page
Navigate to the target reviews URL first, then extract:
navigate "https://www.walmart.com/reviews/product/{item-id}?page={page}"wait stableeval "$(python scripts/extract-reviews.py)"
Parameters in URL:
{item-id}: Walmart item ID (numeric, e.g.,18656507313){page}: page number starting from1; 10 reviews per page
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 123 lines · 185 tokens per session scan A 8809b030cc64
walmart-product-reviews is a skill published in the GitHub repository browser-act/skills (5,875 stars, last pushed 17d ago), licensed MIT. It adds 185 tokens to every session and 1,566 once invoked, about $0.0009 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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