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/zpoint/vibe-sellernpx agentmods add skills/zpoint/vibe-seller/review-collectWrote 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/zpoint/vibe-seller/review-collect)<a href="https://agentmods.dev/skills/zpoint/vibe-seller/review-collect"><img src="https://agentmods.dev/badge/skills/zpoint/vibe-seller/review-collect/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/zpoint/vibe-seller/review-collect"><img src="https://agentmods.dev/badge/skills/zpoint/vibe-seller/review-collect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 122 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00163 | $0.02078 |
| Opus 5 | $0.00081 | $0.01039 |
| Sonnet 5 | $0.00033 | $0.00416 |
| Haiku 4.5 | $0.00016 | $0.00208 |
Grade A, and why
review-collect 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 13d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Collect — Catalog
PREREQUISITE: Read
../amazon-shared/SKILL.mdfor the marketplace TLD map, hamburger-menu navigation, sign-in / Ziniao / OTP handling, and the ad-console vs seller-central account caveat. For noon, read../noon-shared/SKILL.mdfor login + page structure.
This skill collects — it does not analyze or recommend. The output is a machine-readable dataset (one JSON per product + a manifest) that a downstream consumer ingests. Your only job is to make that dataset complete and well-formed for every product the store sells.
What this skill produces
For every (platform, country) the store covers, and every product in
that combo's catalog:
- One JSON per product:
store-data/<slug>/reviews/<platform>/<country>/<product_id>.json(thereviews/v1contract — current rating + full review history). - One run index:
store-data/<slug>/reviews/_MANIFEST.json— per combo, the enumeratedexpectedproduct set and thecollectedset with files written. The server's completeness reviewer parses this to tell you what's still missing each round. - One short Markdown summary:
./REVIEW_COLLECT_<YYYY-MM-DD>.mdin the task dir — combo totals + the manifest progress line. This is the result you pass tovibe_seller_set_task_result.
The JSON dumps are the deliverable; the MD is just a human-readable
cover. Write JSON via vibe_seller_write_workspace_file (the only tool
that writes through the stores/<slug> symlink).
Safety — read-only
This is a read-only skill, exactly like the ad-audit Layer-1 collect
step. You open and read review pages. You never: post a review,
reply to a review, vote/report a review, edit a listing, or change
anything on Amazon or noon. If a task asks you to respond to reviews,
stop and say that is out of scope for review-collect.
START HERE — two files, then run
Do NOT pre-read every reference (it buries the model and causes shortcutting). Read just these two, then execute:
What ships with it
2 files 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.
- 13d ago First seen · 149 lines · 163 tokens per session scan A 4839c3a0df29
review-collect is a skill published in the GitHub repository zpoint/vibe-seller (67 stars, last pushed yesterday), licensed Apache-2.0. It adds 163 tokens to every session and 2,078 once invoked, about $0.0008 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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