task-success-metrics
Measuring whether the AI actually helped users accomplish their goals.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/owl-listener/ai-design-skills --skill task-success-metrics- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads task-success-metrics automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
FAQ
What does the task-success-metrics skill do?
Measuring whether the AI actually helped users accomplish their goals.
How do I install task-success-metrics?
Run npx skills add https://github.com/owl-listener/ai-design-skills --skill task-success-metrics in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/task-success-metrics/ (personal) or .claude/skills/task-success-metrics/ (project).
Which AI agents can use task-success-metrics?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is task-success-metrics safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains task-success-metrics?
It is published by owl-listener in owl-listener/ai-design-skills. It has 130 recorded installs.
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