/crisp-research
Start oriented.
What it does
The research intelligence layer. /crisp-research takes a feature brief, classifies the feature type, and searches designated design sources — Mobbin, SaaSpo, Screenlane, Dribbble, and written case studies — for named patterns, mapped to the CRISP dimensions most at risk for that feature type.
The output is structured orientation, not inspiration: patterns worth studying, anti-patterns not to copy, benchmarks to beat, and the open questions the brief left unanswered. It feeds directly into /feature-design.
When to reach for it
Reach for it at the start of feature work, before opening a single reference site yourself:
- Starting a feature and about to lose an afternoon to Mobbin and Dribbble
- A PM brief has landed and you suspect it has gaps — states, permissions, mobile
- You want named, sourced patterns rather than a folder of screenshots
- Feeding
/feature-designwith evidence instead of taste
What it produces
- Competitive pattern synthesis
- CRISP dimension risk flags
- Anti-patterns to avoid
- Brief gaps surfaced before design
In the terminal
Illustrative example output — not a real audit.
What it will not do
- No raw URLs or screenshot dumps — named patterns and named products only
- No more than three patterns per dimension, ever
- No padding when results are thin — it signals low confidence instead
- No design recommendations — that is
/feature-design's job - No designing into a monoculture — saturated aesthetic lanes are flagged so the design consciously departs from AI-generated defaults
Common questions
What input does it need?
A feature name, a problem statement, or a pasted PM brief. If the brief is too vague to search on, it asks exactly one clarifying question and waits — it will not burn searches on a guess.
What is a saturated lane warning?
A flag that the brief points toward a visual monoculture — the editorial-typographic landing page, the generic SaaS cream dashboard, the glowing dark AI aesthetic. The warning names the lane and suggests departure directions so /feature-design makes a conscious choice.
How do the sources differ?
Mobbin for production UI patterns from shipped apps, SaaSpo for B2B and enterprise SaaS, Screenlane for micro-interactions, Dribbble for visual direction only — flagged as such — and web search for written case studies. The priority order changes by feature type.
What are the open questions in the output?
The specific gaps the brief left — states, permissions, mobile, triggers — surfaced as a maximum of three named questions for the PM. Every brief has gaps; the research names the ones that change the design.
Install
Installs all fourteen CRISP skills and auto-detects your AI harness. Run /crisp-teach once per project first.
Related skills