Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/unixcrh/phuryn-pm-skillsWrote 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/unixcrh/phuryn-pm-skills/analyze-feedback)<a href="https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/analyze-feedback"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/analyze-feedback/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/unixcrh/phuryn-pm-skills/analyze-feedback"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/analyze-feedback.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 | $0.00016 | $0.00845 |
| Opus 5 | $0.00008 | $0.00423 |
| Sonnet 5 | $0.00003 | $0.00169 |
| Haiku 4.5 | $0.00002 | $0.00085 |
Grade A, and why
analyze-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 11d 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.
This is a copy
100% identical to analyze-feedback — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze-feedback -- User Feedback Analysis
Process large volumes of user feedback (reviews, surveys, support tickets, NPS responses) into structured insights with sentiment analysis and segment-level patterns.
Invocation
/analyze-feedback [upload a CSV of NPS responses]
/analyze-feedback [paste app store reviews or survey responses]
/analyze-feedback [upload support ticket export]
Workflow
Step 1: Accept Feedback Data
Accept in any format:
- CSV/Excel with feedback text (and optional metadata: date, segment, rating)
- Pasted text (reviews, survey responses, Slack messages)
- Uploaded documents or exports from feedback tools
Ask:
- What kind of feedback is this? (NPS, reviews, support tickets, survey, etc.)
- Any segments to analyze separately? (user tier, plan, geography)
- What are you looking for? (general themes, specific issues, trends over time)
Step 2: Analyze
Apply the sentiment-analysis skill:
- Sentiment scoring: Classify each piece of feedback (positive, neutral, negative)
- Theme extraction: Identify recurring topics and cluster related feedback
- Frequency analysis: Count how often each theme appears
- Segment analysis: Break down sentiment and themes by user segment (if data available)
- Trend detection: If dates are available, identify sentiment shifts over time
Step 3: Generate Analysis Report
## Feedback Analysis Report
**Date**: [today]
**Feedback analyzed**: [count] responses
**Source**: [NPS survey / app reviews / support tickets / etc.]
**Period**: [date range if available]
### Overall Sentiment
- Positive: [X%] | Neutral: [Y%] | Negative: [Z%]
- Average sentiment score: [X/10]
- Trend: [improving / stable / declining]
### Top Themes
| # | Theme | Mentions | Sentiment | Segments Most Affected |
|---|-------|----------|-----------|----------------------|
### Theme Deep-Dive
#### Theme 1: [Name] — [X] mentions, [sentiment]
- **What users are saying**: [summary with representative quotes]
- **Root cause**: [what's driving this feedback]
- **Impact**: [how this affects retention, satisfaction, or revenue]
- **Recommendation**: [what to do about it]
[Repeat for top 5-8 themes]
### Segment Analysis
| Segment | Volume | Avg Sentiment | Top Theme | Key Difference |
|---------|--------|-------------|-----------|---------------|
### Notable Quotes
> "[quote]" — [segment, sentiment]
### Trends Over Time
[If date data available: chart-ready data showing sentiment shifts]
### Actionable Insights
1. [Insight + recommended action]
2. ...
### Gaps
[What this feedback doesn't tell you — suggested follow-up research]
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.
- 11d ago First seen · 104 lines · 16 tokens per session scan A 23f85c49ee23
analyze-feedback is a command published in the GitHub repository unixcrh/phuryn-pm-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 845 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to analyze-feedback, differing in 0 lines, and is treated as a copy.
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.