layers-conceptual-mode…
Techniques for defining the product's objects, relationships, states, and vocabulary…
Techniques for planning user research and synthesising it into grounded, confidence-rated findings about what users actually do
$ npx -y skills add jamiemill/layers-skills --skill layers-observed-behaviour --agent claude-codeHow it fires
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/layers-observed-behaviourContext preview
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Techniques for planning user research and synthesising it into grounded, confidence-rated findings about what users actually do
name: layers-observed-behaviour description: Techniques for planning user research and synthesising it into grounded, confidence-rated findings about what users actually do
*Assumes `/layers-intro` has been loaded. This skill is a library of techniques, not a script — see "How to use these skills" there.*
The observed behaviour layer is the closest we can get to reality — what users actually do, not what we think they do or wish they would. Everything above it is interpretation; this layer is the source.
It splits into two situations. Detect which applies and say so:
With partial research, synthesise what exists first, then plan to fill the gaps.
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| Technique | Use it when | |---|---| | **Define the learning goal** | Always start here. Push past "understand users better" to 2–3 specific questions — "what triggers someone to refer a friend, and what makes them hesitate." | | **JTBD interviews** | Understanding triggers, motivations, anxieties. Interview about a real past experience, not hypotheticals. Guide: opening ("tell me about the last time you…"), timeline (what triggered it, what you tried), motivations (what you hoped, what worried you), closing. | | **Contextual inquiry / observation** | What users say differs from what they do — watch real work for tacit behaviour. | | **Diary studies** | Behaviour is distributed over time or infrequent — users self-report as events occur. | | **Support ticket / review analysis** | Existing product with accumulated signal — pain points at scale without recruiting. | | **Analytics review** | What users do (not why). Complements qualitative; doesn't replace it. | | **Usability observation** | Where people struggle or succeed with an existing product. |
For interviews, plan synthesis up front: one observation per note, tagged with the question it speaks to, raw quotes over summaries. (6–10 qualitative interviews usually reach saturation.)
| Technique | Use it to | |---|---| | **Extract observations** | Pull out concrete things users said, did, or felt — no interpretation yet. From memory, prompt: most surprising thing? what recurred? what did they struggle with unexpectedly? | | **Pattern grouping** | Group observations by recurring situations, common motivations, shared anxieties, and workarounds. | | **Candidate job stories** | *When [situation], I want to [motivation], so I can [outcome].* Check the "When" is specific and the "want" is a motivation not a solution; mark confidence. | | **Gap-flagging** | What do the observations not yet answer? These become a follow-up Plan session. |
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First find out what exists — interviews, recordings, tickets, analytics — and state the mode. Listen for nouns (candidate domain objects) and the natural language users use; that feeds the domain layer.
Offer the technique that fits: in Plan, the method matched to the learning goal; in Synthesise, extraction → patterns → candidate stories. Do the next useful thing, not a full battery.
Capture only the residue — key raw observations, the patterns with their supporting evidence, candidate job stories with confidence ratings, and the named research gaps.
Candidate job stories are ready to refine at `/layers-user-needs`.
A set of AI skills that guide product designers through the Layers of Product Design framework — a structured way to think about design work across seven layers, from observed user behaviour through to surface decisions.
Repo: jamiemill/layers-skills
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