skill-parhamb-design-skills-ai-automation · by Install
$ agentstack add skill-parhamb-design-skills-ai-automation ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-parhamb-design-skills-ai-automation) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
AI and Automation UX
AI features fail not because the model is bad, but because the experience around it is poorly designed. Users need to trust the AI, understand what it can and cannot do, stay in control, and recover gracefully when it is wrong.
Checklist
| Item | Guidance | |---|---| | AI assistant / chatbot | Clear entry point. Example prompts shown to start. Scope communicated upfront. | | Smart recommendations | Explains why ("Because you liked X"). Includes a "Not interested" option. | | AI-powered search | Supports natural language queries. Shows what the AI understood from the input. | | Predictive input | Non-intrusive. Tap to accept, ignore to dismiss. Never auto-inserts without confirmation. | | Auto-categorization | Shows the AI-assigned label and lets the user correct it inline. | | AI-generated summaries | Clearly labeled as AI-generated. Links to the original source. | | AI transparency | All AI-generated content is marked with a consistent icon or badge. | | User control over AI | Per-feature opt-out in Settings. Not a single global "Disable AI" toggle. | | AI error handling | Falls back gracefully when uncertain. Never exposes a raw model error to the user. | | On-device AI | On-device processing is used for sensitive content (photos, notes, health data). | | Feedback mechanism | Thumbs up / down on every AI output. Minimal friction to give feedback. | | Automation and workflows | Preview of what the automation will do is shown before the user confirms execution. | | Context-aware suggestions | Suggestions adapt to the user's current task, time of day, and usage history. | | AI model update disclosure | Users are informed when the AI model powering a feature changes significantly. |
AI UX Principles
Explainability: Always answer "why is the AI showing me this?" — either proactively or available on demand. A recommendation with a visible reason is trusted. One without a reason is suspicious.
Graceful degradation: When confidence is low, the AI should say less, not guess more. "I'm not sure — here are 3 options" is better than one confident wrong answer.
Progressive trust: Earn trust over time. Start with conservative recommendations. Let users see that the system is improving with their use.
User in the loop: AI proposes, the user decides. Never execute an irreversible action without explicit user confirmation. For automation, show a preview and require a deliberate "Run" tap.
Transparency by default: Every AI-generated piece of content should be labeled. Users should always be able to ask "How was this made?" and get a clear answer.
AI Content Labeling
Use a consistent badge across the entire product (a sparkle icon is widely recognized). Place it near the content, not buried in metadata. On tap or hover, show a brief explanation: "This was generated by AI." Never mix AI-generated and human-created content in the same feed without differentiation.
Feedback Loop Design
Micro-feedback (low friction): Thumbs up / thumbs down visible on hover or long-press. "Was this helpful?" at the end of AI responses.
Macro-feedback (periodic): "How is this feature working for you?" prompt after 7 days of use. A dedicated feedback option in Settings > AI.
Where possible, show users that their feedback has an effect: "Your suggestions have been used to improve your recommendations."
On-Device vs Cloud AI
| Criteria | On-device | Cloud | |---|---|---| | Privacy | High — data stays on device | Lower — data sent to server | | Latency | Very low | Depends on connection speed | | Capability | Limited by device hardware | Full model capability | | Cost | One-time model download | Per-inference cost | | Best for | Photos, text suggestions, classification | Chat, generation, summarization |
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: parhamb
- Source: parhamb/design-skills
- License: MIT
- Homepage: https://design-skills-pi.vercel.app
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.