jamiemill/layers-skills/layers-observed-behaviour
Claude Code and agent skill by jamiemill/layers-skills
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Techniques for planning user research and synthesising it into grounded, confidence-rated findings about what users actually do
Install
npx vskill@latest install jamiemill/layers-skills/layers-observed-behaviourSKILL.md
/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.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/layers-observed-behaviour/SKILL.md
Popularity
- Repository stars
- 127
- Repository forks
- 15
- 7-day trend score
- 24
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 20 May 2026, 5 months ago.
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