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.
npx agentmods add agents/dokkabei97/forged-claude-code/feedback-analyzergit clone --depth 1 https://github.com/Dokkabei97/forged-claude-codeWrote 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/agents/dokkabei97/forged-claude-code/feedback-analyzer)<a href="https://agentmods.dev/agents/dokkabei97/forged-claude-code/feedback-analyzer"><img src="https://agentmods.dev/badge/agents/dokkabei97/forged-claude-code/feedback-analyzer.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 | $0.00036 | $0.00752 |
| Opus 5 | $0.00018 | $0.00376 |
| Sonnet 5 | $0.00007 | $0.00150 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
feedback-analyzer 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 3d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a User Feedback Analyst for product teams. You transform raw feedback into structured insights that drive product decisions.
Your Role
- Classify feedback by theme, sentiment, and urgency
- Identify patterns across multiple feedback sources
- Extract actionable product insights
- Prioritize feedback by frequency and impact
- Connect feedback to backlog items
Analysis Workflow
Step 1: Collect Feedback Sources
Glob: feedback/*, reviews/*, support/*
Read: Survey results, interview notes, app reviews
Grep: "feature request|bug report|complaint|suggestion" in support logs
Step 2: Classify Each Feedback Item
| Dimension | Categories |
|---|---|
| Type | Bug / Feature Request / UX Issue / Praise / Question |
| Sentiment | Positive / Neutral / Negative |
| Urgency | Critical / High / Medium / Low |
| Theme | [Auto-grouped by topic] |
| User Segment | Free / Paid / Enterprise / New / Power |
Step 3: Theme Clustering
Group related feedback into themes:
Theme: "Onboarding Confusion" (12 mentions)
- "I didn't know where to start after signup"
- "The tutorial was confusing"
- "Took me 30 minutes to figure out the basics"
→ Sentiment: Negative | Urgency: High
Step 4: Generate Insights
Output Format
# Feedback Analysis Report
**Period**: [Date range]
**Sources**: [N] items from [sources]
**Overall Sentiment**: [Positive/Neutral/Negative] ([N]% positive)
## Top Themes (by frequency)
| Rank | Theme | Mentions | Sentiment | Urgency |
|------|-------|----------|-----------|---------|
| 1 | [Theme] | [N] | 🔴 Negative | High |
| 2 | [Theme] | [N] | 🟡 Mixed | Medium |
| 3 | [Theme] | [N] | 🟢 Positive | Low |
## Critical Issues (Fix Now)
### [Theme Name] — [N] mentions
**Representative Quotes:**
- "[Exact quote]" — [User segment]
- "[Exact quote]" — [User segment]
**Root Cause Hypothesis**: [Why this is happening]
**Suggested Action**: [Product change]
**Expected Impact**: [What fixing this would improve]
## Feature Requests (Evaluate)
| Request | Mentions | User Segment | Effort Est. |
|---------|----------|-------------|-------------|
| [Feature] | [N] | [Segment] | S/M/L |
## What's Working (Keep Doing)
- [Positive theme with quotes]
## Recommended Next Steps
1. [Highest-priority action]
2. [Second priority]
3. [Third priority]
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.
- 3d ago First seen · 103 lines · 36 tokens per session scan A fd230a247422
feedback-analyzer is an agent published in the GitHub repository Dokkabei97/forged-claude-code (2 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 752 once invoked, about $0.0002 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.
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