platform-feedback

name	platform-feedback
description	Quarterly developer feedback collection and analysis for the fawkes platform. Use to measure whether the IDP actually reduces cognitive load for the product teams using it. Implements DORA AI Capability 7 (Quality internal platforms) — the measurement half.
license	MIT
compatibility	Claude Code, GitHub Copilot, OpenCode, Cursor, Codex, Gemini CLI
metadata	{"author":"paruff","suite":"uFawkesAI"}

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

Metric Mapping (H.E.A.R.T.-inspired)

Map survey responses to platform quality metrics:

Survey question	Metric	Formula
Q1: Task completion	Task success rate	(Yes completely + Yes with lookup) / total responses
Q1: Got stuck	Task abandonment rate	(Partially + No) / total responses
Q4: Recommend	NPS proxy	(Yes without hesitation) - (No) / total responses
Q2: Hardest part	Top friction themes	Qualitative — categorize by component
Q3: What to skip	Removal candidates	Qualitative — prioritize by frequency

Baseline and Targets

Metric	Initial baseline	Target (12 months)	Elite benchmark
Task success rate	Establish in Q1	>80%	>90%
Task abandonment rate	Establish in Q1	<15%	<5%
NPS proxy	Establish in Q1	>40	>70

GitHub Discussion Template

Create quarterly via:

# Requires gh CLI with Discussions permissions
gh api graphql -f query='
mutation {
  createDiscussion(input: {
    repositoryId: "REPO_ID"
    categoryId: "CATEGORY_ID"
    title: "fawkes Platform Feedback — Q[N] YYYY"
    body: "[paste four-question survey above]"
  }) {
    discussion { url }
  }
}'
# Pin the discussion after creation

Analysis Protocol

After the feedback window closes (2 weeks after posting):

1. Tally Q1 and Q4 — compute task success rate and NPS proxy
2. Categorize Q2 responses into themes:
   • Documentation gaps
   • Golden path friction (missing steps, unclear config)
   • Tool/dependency issues (installation, version conflicts)
   • Performance issues (slow builds, slow tests)
   • Conceptual gaps (user didn't understand what the platform does)
3. Count Q3 removal candidates — anything mentioned by >1 respondent is a signal
4. Compare to previous quarter — trend matters more than absolute value

Output → Plan Agent

Each analysis session produces at most 3 action items for the plan agent:

• One for the highest-friction theme (Q2)
• One for the most-requested removal (Q3)
• One for any metric below target

Each action item filed as a GitHub issue with:

• Label: platform-feedback, capability-improvement, tier label
• Body: links to specific Discussion responses as evidence
• DORA capability: Cap7 (Quality internal platforms)

Output Format

{
  "skill": "platform-feedback",
  "quarter": "YYYY-QN",
  "responses": 12,
  "task_success_rate": 0.75,
  "task_abandonment_rate": 0.17,
  "nps_proxy": 33,
  "top_friction_themes": [
    { "theme": "Golden path config unclear", "count": 5 },
    { "theme": "Dojo lab prerequisites missing", "count": 3 }
  ],
  "removal_candidates": [
    { "item": "Manual docker network setup step", "count": 4 }
  ],
  "vs_previous_quarter": {
    "task_success_rate": "+0.08",
    "nps_proxy": "+12"
  },
  "action_items_filed": [42, 43, 44],
  "next_survey_due": "YYYY-MM-DD"
}