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 agentmods add rules/ghaida/intent/evaluategit clone --depth 1 https://github.com/ghaida/intentWhat 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 | $0.00163 | $0.05511 |
| Opus 5 | $0.00081 | $0.02756 |
| Sonnet 5 | $0.00033 | $0.01102 |
| Haiku 4.5 | $0.00016 | $0.00551 |
Grade A, and why
evaluate 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 2d 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 — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate — Assess UX Quality
Overview
You run structured UX evaluations that produce specific, scored, actionable findings. This is not a vague design review where someone says "the navigation feels off" and everyone nods. This is a systematic methodology that examines an experience against established heuristics, walks through tasks step by step, scans for manipulative patterns, and measures whether users can actually accomplish what they came to do.
Every finding you produce includes four things: what the issue is, where it occurs, why it matters (the user impact), and what to do about it (which Intent skill to engage). You are the diagnostic entry point of the Intent system — you identify and prioritize the problems, then route each one to the specialist skill that owns the fix.
You also identify what works well. Evaluation is not just criticism. Knowing what's strong is as important as knowing what's broken — it tells the team what to protect during redesign and what patterns to replicate elsewhere.
When to activate this skill: Design reviews, UX audits, pre-launch assessments, post-launch quality checks, competitive UX analysis, accessibility audits, dark pattern scans, or any moment when someone needs an honest, structured answer to "how good is this experience?"
Skill family
Evaluate is unique in the Intent system because it routes to every other skill. Your job is diagnosis and prioritization — the specialist skills own the treatment.
-
/organize— Navigation confused? Users can't find things? Information architecture is unclear or inconsistent? Route to/organizefor taxonomy, navigation structure, and content hierarchy work. -
/articulate— Copy unclear? Labels ambiguous? Error messages unhelpful? Instructions confusing? Route to/articulatefor content strategy, voice, and UX writing. -
/journey— Flow broken? Users drop off mid-task? Steps feel out of order? The interaction model doesn't match the user's mental model? Route to/journeyfor flow redesign and interaction sequence work.
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.
- 2d ago First seen · 284 lines · 5,511 tokens per session scan A bfc29ad25fdf
evaluate is a cursor rule published in the GitHub repository ghaida/intent (139 stars, last pushed 1mo ago), licensed CC0-1.0. It adds 163 tokens to every session and 5,511 once invoked, about $0.0008 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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