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 perniemann/pnCore --skill pn-feedback-analysisgit clone --depth 1 https://github.com/perniemann/pnCoreWrote 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/perniemann/pncore/pn-feedback-analysis)<a href="https://agentmods.dev/skills/perniemann/pncore/pn-feedback-analysis"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-feedback-analysis/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/perniemann/pncore/pn-feedback-analysis"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-feedback-analysis.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 | $0.00027 | $0.00206 |
| Opus 5.5 | $0.00011 | $0.00082 |
| Sonnet 5.5 | $0.00005 | $0.00041 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
pn-feedback-analysis 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 1mo 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.
What it actually says
Feedback analysis
When to use
- User feedback analysis (surveys, support, reviews).
- Theme identification and prioritization.
- NPS, CSAT, churn signals from feedback.
- Feature request impact assessment.
Workflow
- Collect: Aggregate from defined channels.
- Process: Categorize, sentiment where applicable.
- Synthesize: Themes, priorities, recommendations.
- Deliver: Report with actionable insights; link to roadmap.
Output
- Themed feedback summary, prioritized recommendations, and explicit links to roadmap or backlog items.
Guardrails
- Separate anecdote from volume-weighted signal; cite channel and date range for samples.
Integration
- pn-sprint-prioritization — Feed into backlog.
- pn-discovery-questionnaire — When defining research scope.
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.
- 1mo ago First seen · 34 lines · 27 tokens per session scan A 66a98b1aeee5
pn-feedback-analysis is a skill published in the GitHub repository perniemann/pnCore (0 stars, last pushed 6d ago), licensed MIT. It adds 27 tokens to every session and 206 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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