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 skills/rampstackco/claude-skills-pm/user-feedback-aggregationnpx skills add rampstackco/claude-skills-pm --skill user-feedback-aggregationgit clone --depth 1 https://github.com/rampstackco/claude-skills-pmWrote 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/rampstackco/claude-skills-pm/user-feedback-aggregation)<a href="https://agentmods.dev/skills/rampstackco/claude-skills-pm/user-feedback-aggregation"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills-pm/user-feedback-aggregation.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 5.1 | $0.00157 | $0.04010 |
| Opus 5 | $0.00078 | $0.02005 |
| Sonnet 5 | $0.00031 | $0.00802 |
| Haiku 4.5 | $0.00016 | $0.00401 |
Grade A, and why
user-feedback-aggregation 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 6d 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 user-feedback-aggregation — 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 — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Feedback Aggregation
A senior product leader's playbook for collecting and synthesizing user feedback across channels into continuous decision signal. Support tickets, NPS surveys, in-app feedback, sales calls, social mentions, customer councils, all aggregated into a triaged synthesis the team can actually act on.
Most product programs accumulate feedback they do not use. Channels overflow with submissions; CSAT and NPS scores get reported in monthly updates; customer council meetings produce notes that nobody references. The loudest voices steer roadmap because they are easiest to hear; quieter signal that matters more goes unaddressed.
This skill is the triage discipline that turns continuous feedback streams into continuous decision signal. Each channel surfaces different signal at different reliability. Each signal type warrants different weight in different decisions. The team that aggregates feedback well makes better decisions; the team that drowns in feedback makes the same decisions they would have made without the feedback.
Different from discovery-research-synthesis, which covers one-off research projects (a defined batch of artifacts, a defined synthesis output). This skill covers the always-on streams: feedback that arrives every day, every week, every month, and that the team must continuously triage and synthesize.
The voice is the senior product leader who has watched feedback aggregation work and watched it fail. Concrete, opinionated about which channels matter for which decisions, willing to call out where loudest-voice or averaged-noise patterns produce bad outcomes.
When to use this skill: building a feedback aggregation system, auditing a feedback program that is producing volume without decisions, deciding which feedback channels matter for the program, or designing the synthesis cadence for ongoing feedback streams.
What this skill is for
This skill spans continuous user-feedback aggregation. The PM-skill distinction:
What ships with it
9 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.
- references/categorization-and-tagging-at-scale.md 9.7 KB
- references/channel-source-weighting.md 11 KB
- references/channel-types-and-what-each-surfaces.md 11 KB
- references/closing-the-loop-with-users.md 9.5 KB
- references/common-feedback-aggregation-failures.md 10 KB
- references/detecting-drift-in-feedback.md 9.5 KB
- references/frequency-vs-intensity.md 9.6 KB
- references/from-feedback-to-product-decision.md 9.8 KB
- references/tooling-considerations.md 8.9 KB
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.
- 6d ago First seen · 303 lines · 157 tokens per session scan A 0e15fc40121b
user-feedback-aggregation is a skill published in the GitHub repository rampstackco/claude-skills-pm (4 stars, last pushed 1mo ago), licensed MIT. It adds 157 tokens to every session and 4,010 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to user-feedback-aggregation, differing in 0 lines, and is treated as a copy.
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