DO
dogfooding
Critically dogfood the running app by playing real user flows and reporting severity-ranked UX and fun feedback with concrete code-linked fixes. Use when asked to test the app experience, evaluate engagement, or provide product-quality UX critique.
Install
mkdir -p .claude/skills/dogfooding-litago75 && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/15078" && unzip -o skill.zip -d .claude/skills/dogfooding-litago75 && rm skill.zipInstalls to .claude/skills/dogfooding-litago75
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Critically dogfood the running app by playing real user flows and reporting severity-ranked UX and fun feedback with concrete code-linked fixes. Use when asked to test the app experience, evaluate engagement, or provide product-quality UX critique.248 chars✓ has a “when” trigger
About this skill
Dogfooding Skill
Hands-on product review workflow for this workspace. Prefer direct interaction over code-only analysis.
When To Use
- User asks to dogfood, playtest, or critically review UX.
- User asks if the app is fun, engaging, sticky, or clear.
- User wants feedback tied to actual interaction evidence.
Inputs
- Optional focus area: fun, onboarding, retention, accessibility.
- If no focus is provided, default to fun + usability.
Procedure
- Ensure app is running locally.
- Start with npm run dev if needed.
- Open browser at http://localhost:5173/.
- Play at least one full primary loop.
- Start screen -> in-game interactions -> win state or terminal state -> replay/reset path.
- Stress key interactions.
- Fast-path completion (speed-run behavior).
- Error/edge behavior (toggle, undo, reset, back navigation).
- Post-win continuation behavior and replay quality.
- Capture concrete evidence.
- Cite exact UI behavior observed in browser interactions.
- Map major findings to code locations when possible.
- Report findings in severity order.
- Focus on bugs, UX friction, fun blockers, retention risks.
- Keep summaries brief after findings.
- Provide actionable fixes.
- Propose high-leverage changes first.
- Include smallest effective next steps.
Report Format
- Dogfooder verdict (short score + rationale).
- Findings ordered by severity.
- Severity level.
- Observed behavior.
- User impact (fun/usability).
- Relevant file links when available.
- What is working well.
- Highest-leverage improvements (top 3-5).
Workspace Anchors
- App shell: src/App.tsx
- Game state and persistence: src/hooks/useBingoGame.ts
- Rules engine: src/utils/bingoLogic.ts
- In-game UI: src/components/GameScreen.tsx
- Win modal: src/components/BingoModal.tsx
Quality Bar
- Do not provide feedback without interaction evidence.
- Do not stop at generic advice; tie feedback to observed behaviors.
- Prioritize issues that reduce fun, clarity, or replay value.
- Explicitly call out if a critical loop is too easy to game or lacks reward.