owl-listener/ai-design-skills122 installs
task-success-metrics
Measuring whether the AI actually helped users accomplish their goals.
How do I install this agent skill?
npx skills add https://github.com/owl-listener/ai-design-skills --skill task-success-metricsIs this agent skill safe to install?
- Gen Agent Trust Hubpass
The skill contains only informational markdown documentation about task success metrics and includes no executable code, tools, or external network requests.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
Task Success Metrics
Output quality doesn't guarantee task success. The AI might produce a beautiful response that doesn't actually help the user do what they came to do. Task success metrics measure the end-to-end outcome.
Defining Task Success
For each user task, define:
- What does success look like? The user completed their goal (sent the email, found the information, finished the design)
- What are the success criteria? Specific, observable conditions that indicate the task is done
- What's the time expectation? How long should this task take with AI assistance vs. without?
- What's the quality bar? Not just done, but done well enough
Task Success Metrics
- Task completion rate: Percentage of users who complete the task (not just get a response)
- Time to completion: How long from first input to task done
- Turns to completion: How many back-and-forth exchanges needed
- First-attempt success rate: Did the AI's first response accomplish the task, or did it require iteration?
- Intervention rate: How often did the user need to correct, redirect, or override the AI?
- Abandonment rate: How often did users give up before completing the task?
Measuring Task Success
- Direct measurement: Track task completion through product analytics (user clicked "done", saved the output, moved to next step)
- Inferred measurement: Infer success from proxy signals (session length, return rate, output edits)
- Self-reported measurement: Ask users whether the AI helped them accomplish their goal
- Comparative measurement: Compare task success with AI vs. without AI, or with version A vs. version B
Task Success vs. Output Quality
These can diverge:
- High output quality, low task success: The AI's answer is well-written but doesn't address the real need
- Low output quality, high task success: The AI's answer is rough but gives the user exactly what they needed
- Both matter: Track both and investigate when they diverge
Design Artefacts
- Task success definitions per key user task
- Metrics framework with measurement methods
- Success criteria specifications
- Baseline measurements (before AI, or current version)
- Task success dashboard specifications
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/owl-listener/ai-design-skills/task-success-metrics">View task-success-metrics on skillZs</a>