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 P47Phoenix/Claude-Plugins --skill user-feedbackgit clone --depth 1 https://github.com/P47Phoenix/Claude-PluginsWrote 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/p47phoenix/claude-plugins/user-feedback)<a href="https://agentmods.dev/skills/p47phoenix/claude-plugins/user-feedback"><img src="https://agentmods.dev/badge/skills/p47phoenix/claude-plugins/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/p47phoenix/claude-plugins/user-feedback"><img src="https://agentmods.dev/badge/skills/p47phoenix/claude-plugins/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 | $0.00098 | $0.03169 |
| Opus 5.5 | $0.00039 | $0.01268 |
| Sonnet 5.5 | $0.00020 | $0.00634 |
| Haiku 4.5 | $0.00010 | $0.00317 |
Grade A, and why
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 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.
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Feedback Agent
Design Principle: Persona Context Isolation
This skill keeps persona-specific reasoning out of the main context window and away from other personas. Each persona is a separate sub-agent invocation that receives only its own profile and the artifact under review. Personas never see each other's feedback. The skill orchestrates the full cycle: select personas, spawn each independently, then aggregate results after all have responded.
Key principles:
- Personas are sub-agents, not roles. Each persona gets its own Agent invocation with ONLY its profile and the artifact. No shared state between persona agents.
- Independence produces diversity. If personas saw each other's feedback, they would converge toward consensus prematurely. Independent feedback catches more issues across different user perspectives.
- Aggregation happens after all personas respond. The skill synthesizes consensus, conflicts, and recommendations only after collecting all independent feedback.
- Always include accessibility. At least one accessibility persona must be included in every review, regardless of persona selection method.
- Personas stay in character. They are users, not designers or developers. They give emotional, honest, personal feedback grounded in their profile's goals, frustrations, and tech literacy.
Unlike the architect skill (which loads multiple references into a single sub-agent for cross-cutting concerns), user-feedback spawns multiple isolated sub-agents that each see only their own persona definition and the artifact. The main context receives only the aggregated report.
Phase 1: Persona Selection
Auto-detect relevant persona categories from project type:
| Project Type | Primary Category | Default Personas |
|---|---|---|
| GAME_DEV | Gamer | Casual Casey, Hardcore Hank, Speedrunner Sam, Completionist Cora, Social Skyler, Accessible Alex, Mobile Morgan |
| GREENFIELD, FEATURE, WEB_APP | Web/App User | Power User Pat, Average User Avery, First-Time Fiona, Non-Technical Nate, Accessible Ash |
| ENTERPRISE, B2B | Enterprise/B2B | Admin Alice, End User Eddie, Manager Maya, IT/Security Ivan |
| Any | Demographic Overlays | Gen Z Zara, Millennial Mia, Gen X Xavier, Boomer Barbara |
What ships with it
10 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/aggregation-patterns.md 4.4 KB
- references/custom-personas.md 4.6 KB
- references/feedback-protocols.md 6.3 KB
- references/persona-invocation.md 3.1 KB
- references/persona-library.md 14 KB
- references/sub-agent-interface.md 2.3 KB
- skills/personas/demographic/SKILL.md 2.1 KB
- skills/personas/enterprise/SKILL.md 2.1 KB
- skills/personas/gamers/SKILL.md 2.3 KB
- skills/personas/web-app/SKILL.md 2.1 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.
- 1mo ago First seen · 273 lines · 98 tokens per session scan A ad7dda4202a5
user-feedback is a skill published in the GitHub repository P47Phoenix/Claude-Plugins (2 stars, last pushed 2d ago), licensed MIT. It adds 98 tokens to every session and 3,169 once invoked, about $0.0004 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 2026-08-31.
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