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Skills/jamiemill/layers-skills/layers-observed-behaviour
layers-observed-behaviour logo

layers-observed-behaviour

jamiemill/layers-skills
827 installs172 stars
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Installation

npx skills add https://github.com/jamiemill/layers-skills --skill layers-observed-behaviour

Summary

Techniques for planning user research and synthesising it into grounded, confidence-rated findings about what users actually do

SKILL.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

TechniqueUse it when
Define the learning goalAlways 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 interviewsUnderstanding 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 / observationWhat users say differs from what they do — watch real work for tacit behaviour.
Diary studiesBehaviour is distributed over time or infrequent — users self-report as events occur.
Support ticket / review analysisExisting product with accumulated signal — pain points at scale without recruiting.
Analytics reviewWhat users do (not why). Complements qualitative; doesn't replace it.
Usability observationWhere 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

TechniqueUse it to
Extract observationsPull 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 groupingGroup observations by recurring situations, common motivations, shared anxieties, and workarounds.
Candidate job storiesWhen [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-flaggingWhat 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.

Score

0–100
63/ 100

Grade

C

Popularity15/30

827 installs — growing adoption.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Layers Observed Behaviour skill score badge previewScore badge

Markdown

[![Layers Observed Behaviour skill](https://www.claudemarket.ai/skills/jamiemill/layers-skills/layers-observed-behaviour/badges/score.svg)](https://www.claudemarket.ai/skills/jamiemill/layers-skills/layers-observed-behaviour)

HTML

<a href="https://www.claudemarket.ai/skills/jamiemill/layers-skills/layers-observed-behaviour"><img src="https://www.claudemarket.ai/skills/jamiemill/layers-skills/layers-observed-behaviour/badges/score.svg" alt="Layers Observed Behaviour skill"/></a>

Layers Observed Behaviour FAQ

How do I install the Layers Observed Behaviour skill?

Run “npx skills add https://github.com/jamiemill/layers-skills --skill layers-observed-behaviour” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Layers Observed Behaviour skill do?

Techniques for planning user research and synthesising it into grounded, confidence-rated findings about what users actually do The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Layers Observed Behaviour skill free?

Yes. Layers Observed Behaviour is a free, open-source skill published from jamiemill/layers-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Layers Observed Behaviour work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Layers Observed Behaviour works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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