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 liqiongyu/lenny_skills_plus --skill analyzing-user-feedbackgit clone --depth 1 https://github.com/liqiongyu/lenny_skills_plusWrote 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/liqiongyu/lenny_skills_plus/analyzing-user-feedback)<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/analyzing-user-feedback"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/analyzing-user-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/liqiongyu/lenny_skills_plus/analyzing-user-feedback"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/analyzing-user-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 | $0.00040 | $0.02034 |
| Opus 5 | $0.00020 | $0.01017 |
| Sonnet 5 | $0.00008 | $0.00407 |
| Haiku 4.5 | $0.00004 | $0.00203 |
Grade A, and why
analyzing-user-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.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing User Feedback
Scope
Covers
- Aggregating and normalizing feedback from multiple channels (support, sales, research, reviews, surveys, usage signals)
- Turning raw feedback into themes with evidence and actionable recommendations
- Identifying friction / reasons users won’t use the product (not just validation)
- Producing a repeatable feedback loop (cadence, owners, and handoffs)
When to use
- “Synthesize our user feedback into themes and actions.”
- “Analyze support tickets / feature requests for the top issues.”
- “Create a voice-of-customer report for in the last .”
- “Summarize churn reasons / cancellation feedback.”
- “Cluster survey open-ends into insights and recommendations.”
When NOT to use
- You need to collect new feedback via interviews (use
conducting-user-interviews) or surveys (usedesigning-surveys); this skill analyzes data you already have - You need task-based usability evaluation of a specific flow or prototype (use
usability-testing) - You need backlog prioritization as the primary output (use
prioritizing-roadmap) - You need a PRD/spec for a chosen solution (use
writing-prds/writing-specs-designs) - You need retention/engagement metric analysis (quantitative cohort/funnel work) rather than qualitative feedback synthesis (use
retention-engagement) - You only need to respond to individual tickets (support workflow, not synthesis)
Inputs
Minimum required
- Product area / workflow to analyze (or “all product”)
- Time window + volume expectations (e.g., “last 90 days”, “~2k tickets”)
- Feedback sources available (tickets, interviews, sales notes, reviews, surveys, community, logs)
- The decision this analysis should inform (roadmap theme, launch readiness, onboarding fixes, messaging, quality)
- Any segmentation that matters (ICP, persona, plan tier, lifecycle stage)
- Constraints: privacy/PII rules, internal-only vs shareable, deadline/time box
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- eval/eval_config.json 787 B
- eval/SHOWCASE.md 4.1 KB
- eval/with_skill.md 43 KB
- eval/without_skill.md 19 KB
- README.md 1.7 KB
- references/CHECKLISTS.md 2.1 KB
- references/EXAMPLES.md 2.0 KB
- references/INTAKE.md 1.6 KB
- references/RUBRIC.md 3.8 KB
- references/SOURCE_SUMMARY.md 2.0 KB
- references/TEMPLATES.md 3.2 KB
- references/WORKFLOW.md 3.8 KB
- skillpack.json 393 B
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 · 140 lines · 40 tokens per session scan A beee41f707f8
analyzing-user-feedback is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 2,034 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 2026-08-30.
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