Layer 1 · Problem space
Observed behaviour
/layers-observed-behaviour
Plan and synthesise user research at the observed-behaviour layer — into grounded, confidence-rated findings about what users actually do.
Example prompts
Drop one of these into your AI tool. The skill takes it from there.
- Plan a research study to understand how users actually finish onboarding. Run
/layers-observed-behaviour. - Analytics shows a drop-off at step 3. Use
/layers-observed-behaviourto design what to investigate. - Synthesise these 12 interviews into job stories with confidence ratings —
/layers-observed-behaviour.
The skill itself
What /layers-observed-behaviour tells the AI to do
Below is the actual skill — the instructions your AI tool follows when you run it. Reading it is optional; the skill loads itself.
/layers-observed-behaviour
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:
- Plan — no research yet; design a study.
- Synthesise — research material exists; make sense of it.
With partial research, synthesise what exists first, then plan to fill the gaps.
The decisions this layer makes
- What specific questions we most need to answer about our users
- What evidence already exists, and how reliable it is
- How to gather what’s missing
- What patterns hold with confidence vs. what remains assumption
Disciplines — what keeps observation honest
- Stay close to raw data. Observations should be specific and near the source — what users said, did, felt — not summarised into conclusions.
- Ground in something seen or heard, not in team beliefs.
- Mark confidence: observed / inferred / assumed. If you mark something observed, the verbatim that supports it should be quotable in the same note — an observed claim with no quotable evidence is really inferred.
- Name research gaps explicitly rather than papering over them.
- Workarounds are signal. A need real enough to motivate improvisation is a strong one.
Techniques
To plan a study
| 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.)
To synthesise material
| 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. |
Working with the designer
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.
Example output
An example of what /layers-observed-behaviour captures
From a research plan for an onboarding redesign.
Research plan — onboarding completion
Goal
Understand why new users drop off between sign-up and creating their first dashboard.
Method
Mixed: 8 user interviews with people who signed up in the last 30 days (4 completed onboarding, 4 dropped); 1 week of session replays for the same cohort.
What we'll capture
- Where (which step) users hesitate, abandon, or backtrack.
- What they expected vs. what the product showed them.
- Which terms in the UI confused them.
- What they tried before reaching for help (or giving up).
Synthesis — after 5 of 8 interviews
Job story 1 observed
When I sign up to evaluate a tool, I want a quick win that proves the product works for my data, so I can decide whether to invest more time in setup.
Job story 2 inferred
When I get blocked during setup, I want examples that match my context, so I don't have to translate generic docs to my situation.
Strategic flag. "Connect a data source" is the single biggest drop-off — users disengage at credential prompts. Worth a strategy-layer conversation about removing or deferring this step.
## Research plan — onboarding completion
### Goal
Understand why new users drop off between sign-up and creating their first dashboard.
### Method
Mixed: 8 user interviews with people who signed up in the last 30 days (4 completed onboarding, 4 dropped); 1 week of session replays for the same cohort.
### What we'll capture
- Where (which step) users hesitate, abandon, or backtrack.
- What they expected vs. what the product showed them.
- Which terms in the UI confused them.
- What they tried before reaching for help (or giving up).
## Synthesis — after 5 of 8 interviews
**Job story 1** `observed`
> *When I sign up to evaluate a tool, I want a quick win that proves the product works for my data, so I can decide whether to invest more time in setup.*
**Job story 2** `inferred`
> *When I get blocked during setup, I want examples that match my context, so I don't have to translate generic docs to my situation.*
**Strategic flag.** "Connect a data source" is the single biggest drop-off — users disengage at credential prompts. Worth a strategy-layer conversation about removing or deferring this step. Install
Install the skills
Install in Claude Code, Cursor, Codex & more
npx skills add jamiemill/layers-skills
Install once with the skills package, then run any /layers-* skill
in your AI tool.