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.
git clone --depth 1 https://github.com/ghaida/intentWrote 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/rules/ghaida/intent/intent-ref-measurement-frameworks)<a href="https://agentmods.dev/rules/ghaida/intent/intent-ref-measurement-frameworks"><img src="https://agentmods.dev/badge/rules/ghaida/intent/intent-ref-measurement-frameworks/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/rules/ghaida/intent/intent-ref-measurement-frameworks"><img src="https://agentmods.dev/badge/rules/ghaida/intent/intent-ref-measurement-frameworks.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 | $0.03493 | $0.03493 |
| Opus 5.5 | $0.01397 | $0.01397 |
| Sonnet 5.5 | $0.00699 | $0.00699 |
| Haiku 4.5 | $0.00349 | $0.00349 |
Grade A, and why
intent-ref-measurement-frameworks 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measurement Frameworks
HEART Framework
Developed by Kerry Rodden, Hilary Hutchinson, and Xin Fu at Google, the HEART framework provides a structured way to define user-centered metrics at any scale — from a single feature to an entire product.
The Five Dimensions
Happiness — Subjective user satisfaction, attitudes, and perceived ease of use. Measured through surveys (CSAT, SUS, NPS), in-app satisfaction prompts, and qualitative feedback.
What it catches that other metrics miss: A product can have high task completion rates but low happiness if the process feels tedious, patronizing, or stressful. Happiness metrics capture the emotional quality of the experience.
What it misses: Happy users aren't necessarily successful users. A product can feel pleasant while failing to deliver actual value. Happiness without task success is entertainment, not utility.
Engagement — The depth and frequency of user interaction with the product. Measured through session frequency, session duration, feature usage, actions per session, content consumption.
What it catches: Whether users find the product valuable enough to return to and invest time in. Engagement distinguishes "signed up but never came back" from "uses it daily."
What to watch for: Engagement can be gamed with addictive patterns (infinite scroll, notification spam, variable ratio reinforcement). High engagement driven by manipulation is not success — it's exploitation. Always pair engagement metrics with happiness and task success to distinguish healthy engagement from compulsive engagement.
Adoption — New users of a product or feature. Measured through sign-ups, feature activation (first meaningful use, not just account creation), upgrade conversions, new feature discovery.
What it catches: Whether growth is happening and whether new features are being discovered and used. Adoption metrics answer: are we reaching new people, and are they finding value?
What it misses: Adoption without retention is a leaky bucket. High sign-up rates with low day-7 retention mean the acquisition is working but the product isn't.
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 · 213 lines · 3,493 tokens per session scan A abe4ae23596c
intent-ref-measurement-frameworks is a cursor rule published in the GitHub repository ghaida/intent (214 stars, last pushed 7d ago), licensed CC0-1.0. It adds 3,493 tokens to every session, about $0.0140 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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