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 5.5 | $0.00000 | $0.00686 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo 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 (11 stars, last pushed 5mo 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.
app-review-digest compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| app-review-digestmotorway-sandbox/product-os | Command | 11 | 5mo ago | MIT | A | 0 | Claude Code |
| checklistgithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| analyzegithub/spec-kit | Command | 148k | yesterday | MIT | A | 26 | Claude Code |
| convergegithub/spec-kit | Command | 148k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 148k | yesterday | MIT | A | 14 | Claude Code |