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 longitudinal-measurementgit 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/longitudinal-measurement)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/longitudinal-measurement"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/longitudinal-measurement/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/longitudinal-measurement"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/longitudinal-measurement.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 | $0.00021 | $0.00483 |
| Opus 5.5 | $0.00008 | $0.00193 |
| Sonnet 5.5 | $0.00004 | $0.00097 |
| Haiku 4.5 | $0.00002 | $0.00048 |
Grade A, and why
longitudinal-measurement 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 1mo 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Longitudinal Measurement
AI products change over time — models get updated, usage patterns shift, and quality can drift without anyone noticing. Longitudinal measurement is how you track quality across time and catch degradation before users do.
What Changes Over Time
- Model updates: New model versions may improve some capabilities and regress others
- Prompt drift: System prompts accumulate edits that may interact in unexpected ways
- Usage evolution: Users discover new use cases that weren't tested for
- Data drift: The real-world inputs diverge from what was tested
- Expectation drift: Users' expectations change as they become more experienced
What to Measure Longitudinally
- Quality scores: Track rubric scores on a consistent test set over time
- Task success rates: Monitor whether users are completing tasks at the same rate
- Satisfaction signals: Track trends in explicit and implicit satisfaction
- Error rates: Monitor failure frequency and type distribution
- Latency: Response time changes can indicate degradation
- Engagement patterns: Changes in usage frequency, depth, and breadth
Measurement Infrastructure
- Golden test sets: A fixed set of inputs evaluated regularly to detect quality changes
- Automated evaluation: Run golden test sets automatically on a schedule
- Dashboards: Visualise trends and set alerts for significant changes
- Regression detection: Statistical methods to distinguish real changes from noise
- User cohort tracking: Follow specific user groups over time
Responding to Drift
When measurements show drift:
- Detect: Automated alerts flag significant changes
- Diagnose: Was it a model update, prompt change, data shift, or usage change?
- Assess: Is the drift harmful, neutral, or actually an improvement?
- Act: Adjust prompts, revert changes, update guardrails, or accept the new baseline
- Verify: Confirm the fix worked and set the new baseline
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.
- 1mo ago First seen · 39 lines · 21 tokens per session scan A 36be90a29241
longitudinal-measurement is a skill published in the GitHub repository Owl-Listener/ai-design-skills (189 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 483 once invoked, about $0.0001 per session on Opus 5.5. 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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