Borrowing it
Nothing to install: this file belongs to motorway-sandbox/product-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/motorway-sandbox/product-os/main/.claude/commands/customer-reviews.mdgit clone --depth 1 https://github.com/motorway-sandbox/product-osWrote 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/commands/motorway-sandbox/product-os/customer-reviews)<a href="https://agentmods.dev/commands/motorway-sandbox/product-os/customer-reviews"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/customer-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/commands/motorway-sandbox/product-os/customer-reviews"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/customer-reviews.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 | $0.00000 | $0.00955 |
| Opus 5 | $0.00000 | $0.00477 |
| Sonnet 5 | $0.00000 | $0.00191 |
| Haiku 4.5 | $0.00000 | $0.00096 |
Grade A, and why
customer-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 7d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weekly Customer Reviews Summary
Generate a weekly summary of customer reviews from {your-review-platform}, Google Reviews, and the App Store, then save it and draft a Slack message to {your-pm-channel}.
Instructions
Step 1: Fetch {your-review-platform} reviews
- Use WebFetch to pull the most recent reviews from
{your-review-platform-url} - Extract: reviewer name, star rating, date, and full review text for every review visible
- Note the overall star rating distribution if visible on the page
Step 2: Fetch App Store reviews
- Use WebFetch to pull reviews from
{your-app-store-url} - Extract: reviewer name, star rating, date, title, and full review text
- Only include reviews from the past week
Step 3: Read Slack review channels
- Load Slack tools using ToolSearch
- Search for channels "reviews" and "app-reviews" using slack_search_channels to get their IDs
- Read the past 7 days of messages from both channels using slack_read_channel with appropriate Unix timestamps for oldest/latest
- Also search within both channels using slack_search_public with keywords like "star review", "{your-company-name}", "support", "easy", "recommend" to surface reviews where the text content is in the message body (many reviews are posted as rich attachments with empty text fields)
- Extract any readable review content from Google Reviews (these tend to have visible text in the #reviews channel)
Step 4: Categorise into themes
From ALL collected reviews across all sources, identify the key themes. For each theme provide:
- A descriptive name
- An estimated percentage of reviews falling into this category (use the overall {your-review-platform} star distribution to weight positive vs negative)
- Whether it is positive or negative sentiment
- 2-3 direct quotes from actual reviews
Common themes to look for (but don't limit to these):
- Positive: Easy/simple process, fair pricing, helpful staff, fast payment, better than alternatives
- Negative: Pricing issues, onboarding friction, customer support issues, delivery problems
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.
- 7d ago First seen · 85 lines · 0 tokens per session scan A 8a5e24675f26
customer-reviews is a command published in the GitHub repository motorway-sandbox/product-os (10 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 955 tokens. 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 2026-09-04.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.