Borrowing it
Nothing to install: this file belongs to MrJoeSack/pm-prompts. 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/MrJoeSack/pm-prompts/main/.claude/commands/customer-feedback.mdgit clone --depth 1 https://github.com/MrJoeSack/pm-promptsWrote 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/mrjoesack/pm-prompts/customer-feedback)<a href="https://agentmods.dev/commands/mrjoesack/pm-prompts/customer-feedback"><img src="https://agentmods.dev/badge/commands/mrjoesack/pm-prompts/customer-feedback.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.00563 |
| Opus 5 | $0.00008 | $0.00282 |
| Sonnet 5 | $0.00003 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
customer-feedback 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 8d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Feedback Analysis
I'll help you synthesize customer feedback, interview data, and usage insights using the Jobs-to-be-Done framework and systematic analysis methods.
What I Can Analyze
Feedback Sources:
- Customer interviews and surveys
- Support tickets and feature requests
- User behavior data and analytics
- Sales team insights and objections
- Churn interviews and exit feedback
Analysis Types:
- Jobs-to-be-Done identification
- Pain point and friction analysis
- Feature request prioritization
- User journey mapping insights
- Satisfaction and retention drivers
Information I Need
Raw Feedback Data:
- Interview transcripts or notes
- Survey responses and ratings
- Support ticket themes
- Feature request lists
- Usage data insights
Context:
- User segments or personas
- Product area or feature focus
- Business objectives and metrics
- Current product capabilities
- Competitive landscape
Analysis Framework
1. Jobs-to-be-Done Analysis
- Functional Jobs: What tasks are users trying to accomplish?
- Emotional Jobs: How do they want to feel?
- Social Jobs: How do they want to be perceived?
2. Pain Point Identification
- Frequency: How often does this problem occur?
- Intensity: How painful is this problem?
- Workarounds: What do users do instead?
- Impact: How does this affect their success?
3. Opportunity Sizing
- Market size: How many users experience this?
- Satisfaction gaps: Current vs. desired outcomes
- Competitive differentiation: Unique solving opportunity
- Revenue impact: Business value potential
4. Solution Implications
- Feature requirements: What capabilities needed?
- User experience: How should this work?
- Success metrics: How to measure improvement?
- Implementation priority: Effort vs. impact assessment
Output Format
Executive Summary
- Key insights and themes
- Top priority opportunities
- Strategic recommendations
Jobs-to-be-Done Report
- Primary and secondary jobs identified
- Job statement validation
- Opportunity scoring and prioritization
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.
- 8d ago First seen · 97 lines · 16 tokens per session scan A 4f670465459f
customer-feedback is a command published in the GitHub repository MrJoeSack/pm-prompts (6 stars, last pushed 7mo ago), licensed MIT. It adds 16 tokens to every session and 563 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-31.
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.