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 skills add whawkinsiv/solo-founder-skills --skill feedbackgit clone --depth 1 https://github.com/whawkinsiv/solo-founder-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/skills/whawkinsiv/solo-founder-skills/feedback)<a href="https://agentmods.dev/skills/whawkinsiv/solo-founder-skills/feedback"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/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/skills/whawkinsiv/solo-founder-skills/feedback"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.02055 |
| Opus 5 | $0.00030 | $0.01027 |
| Sonnet 5 | $0.00012 | $0.00411 |
| Haiku 4.5 | $0.00006 | $0.00205 |
Grade A, and why
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 12d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Feedback & Feature Requests
Feedback is abundant but insight is rare. Your job is not to build everything users ask for — it's to understand the problems behind the requests. This skill helps you collect, prioritize, and act on feedback without drowning in it.
Core Principles
- Feedback is a gift, but not all gifts are useful. Filter signal from noise.
- Users describe solutions. Your job is to find the problem underneath.
- "Build what customers ask for" is wrong. "Solve the problems customers reveal" is right.
- A feedback system you actually use beats a perfect one you ignore. Start simple.
- Closing the loop (telling users what you did with their feedback) is the most powerful retention tool you have.
Feedback Collection Methods
Ranked by Signal Quality
| Method | Signal Quality | Effort | Best For |
|---|---|---|---|
| 1-on-1 conversations | Highest | High | Early stage, understanding "why" |
| Support ticket analysis | High | Low | Finding recurring pain points |
| In-app feedback widget | High | Low | Contextual, in-the-moment feedback |
| NPS survey | Medium | Low | Tracking sentiment over time |
| Cancellation survey | High | Low | Understanding churn drivers |
| Feature request board | Medium | Low | Aggregating demand signals |
| Social media mentions | Medium | Low | Unfiltered opinions |
| Usage analytics | High | Medium | What users DO vs. what they SAY |
What to Use When
0-50 users: Talk to every user. Email them. Get on calls. No tools needed.
50-200 users: In-app feedback widget + cancellation survey + monthly NPS
200-500 users: Add a public feature request board + quarterly user interviews
500+ users: All of the above + systematic ticket analysis
In-App Feedback
Simple Feedback Widget
Tell AI:
Add a feedback widget to my app. Requirements:
- Small "Feedback" button fixed to the bottom-right corner
- Clicking opens a simple form: text area + optional email
- Includes the current page URL and user ID automatically
- Saves to a [feedback] table in the database
- Shows a "Thank you" message after submission
- No third-party tool needed — just store it in the database
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.
- 12d ago First seen · 260 lines · 59 tokens per session scan A 917106bb77fe
feedback is a skill published in the GitHub repository whawkinsiv/solo-founder-skills (243 stars, last pushed 15d ago), licensed MIT. It adds 59 tokens to every session and 2,055 once invoked, about $0.0003 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 2026-08-30.
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