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/app-review-digest.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/app-review-digest)<a href="https://agentmods.dev/commands/motorway-sandbox/product-os/app-review-digest"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/app-review-digest/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/app-review-digest"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/app-review-digest.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 | $0.01715 |
| Opus 5 | $0.00000 | $0.00857 |
| Sonnet 5 | $0.00000 | $0.00343 |
| Haiku 4.5 | $0.00000 | $0.00171 |
Grade A, and why
app-review-digest 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Review Digest
Generate a rolling 30-day digest of app reviews from the {your-reviews-channel} Slack channel. Surfaces the top positive and negative themes to help the team prioritise fixes.
Instructions
Step 1: Read app reviews from Slack
- Load Slack tools using ToolSearch
- The {your-reviews-channel} channel ID is
{your-channel-id} - Calculate the Unix timestamp for 30 days ago from today
- Read messages from {your-reviews-channel} using slack_read_channel with
oldestset to the 30-days-ago timestamp. Uselimit: 100and paginate with the cursor if needed to get all messages in the window - Also search using slack_search_public with
in:{your-reviews-channel} after:{30-days-ago-date}to catch reviews in rich attachments. Try multiple searches:in:{your-reviews-channel} after:{date}(general)in:{your-reviews-channel} star after:{date}(star ratings)in:{your-reviews-channel} review after:{date}
- Extract review content: star rating, review text, reviewer name (if available), date, platform (App Store / Google Play), and the Slack message permalink
Important: Many reviews are posted by bots as rich attachments with empty text fields. Use both slack_read_channel and slack_search_public to maximise coverage. Note how many messages were in the channel vs how many had extractable review content.
Step 2: Categorise into themes
From all collected reviews, identify themes. Analyse every review and assign it to one or more themes.
Positive themes: Identify the top 3 positive themes by volume. Negative themes: Identify the top 5 negative themes by volume.
For each theme provide:
- A clear, descriptive theme name
- The number of reviews that align with this theme (exact count)
- Whether it is positive or negative
- 2-3 direct customer quotes with attribution (reviewer name if available, date, star rating)
- Slack message permalinks for the quoted reviews so the team can click through
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 · 163 lines · 0 tokens per session scan A c083bd4ae6c9
app-review-digest is a command published in the GitHub repository motorway-sandbox/product-os (9 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,715 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.