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 paruff/uFawkesAI --skill platform-feedbackgit clone --depth 1 https://github.com/paruff/uFawkesAIWrote 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/paruff/ufawkesai/platform-feedback)<a href="https://agentmods.dev/skills/paruff/ufawkesai/platform-feedback"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/platform-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/paruff/ufawkesai/platform-feedback"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/platform-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 | $0.00052 | $0.01529 |
| Opus 5 | $0.00026 | $0.00764 |
| Sonnet 5 | $0.00010 | $0.00306 |
| Haiku 4.5 | $0.00005 | $0.00153 |
Grade A, and why
platform-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 8d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Platform Feedback
Load trigger:
"load platform-feedback skill"> DORA: AI Capability 7 (Quality internal platforms) Token cost: Low
Purpose
Measure whether the fawkes platform actually delivers on its promise — reduced cognitive load, faster time-to-running-service, trustworthy golden paths — from the perspective of the product engineers and Dojo learners who use it.
DORA AI Capabilities Model v2025.1: A quality internal platform provides automated, secure pathways that allow AI's benefits to scale. Without measurement, "quality" is self-assessed by the builders. This skill is the mechanism that makes platform quality externally validated.
Scope boundary: This skill collects feedback on the platform from its users.
Feedback on the product (what users are building on the platform) is handled by
the discovery skill and the learn agent.
Cadence
| Feedback type | Frequency | Channel |
|---|---|---|
| Quarterly survey | Every 3 months | GitHub Discussion (pinned) |
| Onboarding feedback | After first golden-path completion | GitHub Discussion reply or issue |
| Incident-triggered | After any platform incident affecting users | GitHub issue (label: platform-feedback) |
| Dojo lab feedback | After each belt completion | GitHub Discussion in Dojo repo |
The Four Survey Questions
Quarterly feedback uses exactly four questions. Not five. Not ten. Four questions that a busy developer will actually answer in 3 minutes.
## fawkes Platform Feedback — Q[N] YYYY
Thanks for taking 3 minutes to improve the platform.
**1. Task completion**
Did you complete your primary task (deploy a service, run the Dojo lab, set up
observability) without needing help outside the platform documentation?
- [ ] Yes, completely self-serve
- [ ] Yes, but I needed to look something up externally
- [ ] Partially — I got stuck at [describe briefly in comments]
- [ ] No — I couldn't complete it
**2. Hardest part**
What was the hardest or most confusing part of using the platform this quarter?
[Free text — 1-3 sentences]
**3. What to skip**
If you could remove one thing from the platform (docs, step, config, tool), what
would it be and why?
[Free text — 1-2 sentences]
**4. Recommendation**
Would you recommend the fawkes platform to a colleague building a similar system?
- [ ] Yes, without hesitation
- [ ] Yes, with caveats (describe in comments)
- [ ] Not yet — needs improvement first
- [ ] No
**Optional: Your role**
- [ ] Platform engineer
- [ ] Product engineer using golden paths
- [ ] Dojo learner
- [ ] Team lead evaluating fawkes
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.
- 8d ago First seen · 177 lines · 52 tokens per session scan A 782ddb1be4e3
platform-feedback is a skill published in the GitHub repository paruff/uFawkesAI (2 stars, last pushed 19d ago), licensed MIT. It adds 52 tokens to every session and 1,529 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 2026-09-03.
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