analyzing-user-feedback

Create a User Feedback Analysis Pack: source inventory + normalized feedback table + taxonomy/codebook + themes & evidence + recommendations + feedback loop plan.

What this skill produces

• Context snapshot (scope, decision, time window, segments, constraints)
• Source inventory + sampling plan
• Taxonomy + codebook (tagging definitions + rules)
• Normalized feedback table (tagged items; no PII)
• Themes & evidence report (quotes/examples + frequency/severity + confidence)
• Recommendations + learning plan
• Feedback loop plan (cadence, owners, storage)
• Risks / Open questions / Next steps

How to use (prompt)

Use language like:

“Use analyzing-user-feedback. Context: <product + area>. Decision: <what this should inform>. Sources: <tickets/interviews/surveys/reviews/logs>. Time window: <range>. Segments: <important segments>. Constraints: <PII/internal-only>. Output: a User Feedback Analysis Pack.”

If key details are missing, the skill will ask up to 5 intake questions (see references/INTAKE.md) and then proceed with explicit assumptions.

Optional file output

If you want the deliverables as files, ask the agent to write them under a folder you specify (e.g., docs/user-feedback-analysis/) using references/TEMPLATES.md.

Example prompts

• “Analyze 90 days of support tickets about onboarding for SMB users. Produce themes, evidence, and top fixes.”
• “Synthesize churn survey comments from the last quarter into a voice-of-customer report with recommended actions.”
• “We shipped a new AI feature. Review a sample of 200 traces and support tickets to identify failure modes and a learning plan.”