Classify review

Skill jackhendon/ecom-feedback-intelligence/.claude/skills/classify-review

Open-source Claude Code skills for turning eCommerce customer reviews into structured PM insights. Configurable for any brand via a single YAML file.

Install

npx -y skills add jackhendon/ecom-feedback-intelligence --skill classify-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.1 KB, 509 tokens by cl100k_base, as published. Nobody here has run it

Skill: classify-review

Deep-dive analysis of a single review. Useful for spot-checking, edge cases, or understanding how the classifier handles ambiguous reviews.

Usage

/classify-review

After running the command, paste the review text (or provide rating + text). Claude will prompt if not given.

Optionally provide: /classify-review rating=2 "The product was lovely but delivery was terrible"

Steps

Step 1: Accept input

If the user hasn't provided a review, ask:

Paste the review text (include the star rating if you have it):

Parse:

rating: integer 1–5 (if not provided, ask or infer from language)
text: the review body
date: optional (default to today if not provided — affects recency in priority score)

Step 2: Classify

Apply .claude/rules/analysis-standards.md and .claude/rules/theme-taxonomy.md.

Sentiment analysis:

Label + confidence + 1–3 drivers
Explain the reasoning briefly (1–2 sentences)

Theme classification:

1–3 themes (primary first)
For each theme: quote the specific phrase that justified inclusion

Priority score:

Calculate using the formula from .claude/rules/analysis-standards.md
Show the working: (6 - rating) × weight × confidence × recency = score

Step 3: Output analysis card

REVIEW ANALYSIS
───────────────────────────────────────
Rating:    ★N
Text:      "[full review text]"

SENTIMENT: POSITIVE/NEUTRAL/NEGATIVE (confidence: 0.X)
  Drivers: driver1, driver2

THEMES
  Primary:   theme_slug — "[quoted evidence]"
  Secondary: theme_slug — "[quoted evidence]"

PRIORITY SCORE: N.N / 12.0
  Working: (6 - N) × N.N × N.N × N.N = N.N
  Threshold: [HIGH PRIORITY ≥ 6.0 | below threshold]

PM NOTE
  [One sentence: what a PM should do with this review, if anything]
───────────────────────────────────────

Step 4: Offer follow-up

Run /analyze-reviews to process all reviews. Run /classify-review again to analyse another.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.