Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/ai-design-skills --skill task-success-metricsgit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/task-success-metrics)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/task-success-metrics"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/task-success-metrics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/task-success-metrics"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/task-success-metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00509 |
| Opus 5 | $0.00009 | $0.00254 |
| Sonnet 5 | $0.00003 | $0.00102 |
| Haiku 4.5 | $0.00002 | $0.00051 |
Grade A, and why
task-success-metrics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 36 lines · 17 tokens per session scan A 47b1de51339f
task-success-metrics is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 509 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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