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name: thematic-analysis description: "Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those."

Thematic Analysis — Braun & Clarke's Six-Phase Framework

This skill walks a user through conducting a rigorous thematic analysis (TA) on qualitative data, following the six-phase framework from Braun and Clarke (2006). It produces a Word document (.docx) write-up of the analysis and an annotated thematic map (PNG).

The skill is grounded in one source:

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Where this skill cites the paper, treat those statements as the method's published position, not Claude's own.

Before you begin

Read these reference files as needed:

• references/upfront-decisions.md — The four analytic decisions to settle before coding starts. Consult during Phase 1 (interview).
• references/coding-guide.md — How to generate codes well. Consult during Phase 3 (generating initial codes).
• references/theme-development.md — How to move from codes to themes, with worked examples. Consult during Phases 4–6.
• references/thematic-map.md — How to build and annotate the thematic map. Consult during Phase 6.
• references/quality-checklist.md — The 15-point checklist for assessing the analysis. Consult before producing the final write-up.
• references/pitfalls.md — The five common pitfalls. Consult after the first draft of the write-up.

Also read these skills before generating outputs:

• docx skill (/mnt/skills/public/docx/SKILL.md) — Required for the Word document.
• apa-referencing skill (/mnt/skills/user/apa-referencing/SKILL.md) — If the user wants citations to existing literature in the analysis, format them in APA 7th Edition.

If the user has a writing-style skill, do not apply it to the manuscript body — see "Writing register" under Phase 7. A writing-style skill may still apply to ancillary outputs (a plain-language summary, a blog version of the findings) if the user asks for those separately.

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Step 0: Establish the research question(s) or objective(s) first

Before any of the six phases begin, elicit the research question(s) or objective(s) explicitly. This is the first action of the skill and is non-negotiable. The research question disciplines what counts as interesting in the data, which codes earn their keep, and which patterns rise to the level of a theme. Coding without a clear question tends to drift into surface description.

Prompt the user along these lines:

Before we begin the analysis, please state the research question(s) or objective(s) for this study. If there is more than one, list them in order of priority. If they are still in draft form, share the draft — we can sharpen them together before coding starts.

If the user is unsure or only has a study aim, help them work a draft into a workable analytic question. A good TA research question is broad enough to allow patterned meaning to surface across the data set, but narrow enough to discipline what is included and excluded.

Record the agreed research question(s) verbatim. They will be referenced explicitly in every subsequent phase:

• Phase 3 (coding): when generating codes, keep the research question in view. Ask of each segment, "does this speak to the research question, directly or obliquely?" Code inclusively, but the question anchors the work.
• Phase 4 (searching for themes): themes must capture something important in relation to the research question, not just frequent content.
• Phase 5 (reviewing themes): the second-level review checks the candidate map against the data set as a whole — also check it against the research question. A theme that is internally coherent but unrelated to the question is a candidate for the discard pile.
• Phase 7 (report): the introduction states the research question(s) verbatim, and the findings are organised to answer them.

If the analytic approach is theoretical/deductive, the research question is also tied to the theoretical framework being applied — make this link explicit at this stage, before any coding begins.

Save the agreed research question(s) to the workspace as step0_research_questions.md. Refer back to this file at the start of each subsequent phase.

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Phase 1 (skill workflow): Interview

Before any analysis, gather what is needed to plan the TA. Offer the user two paths up front.

Path A: Upload existing materials

Ask the user whether they have any of the following:

• Interview, focus group or open-ended survey transcripts (the data corpus itself)
• A research question or research aim document
• A proposal or protocol that specifies the analytic approach
• An existing coding frame or codebook (for theoretical/deductive work)
• Field notes, memos, or reflexive journal entries

Read uploaded transcripts using the appropriate tool (file-reading skill for .txt/.md, docx skill for .docx, pdf-reading skill for PDFs, xlsx skill for spreadsheet-formatted survey data). Then summarise what is in the corpus and ask the user to confirm.

Path B: Conversational interview

If the user has no materials, gather the essentials conversationally. Adapt to what they offer; do not interrogate.

Essential information to collect

About the project:

• Working title of the study
• Research question(s) — already collected in Step 0; carry these forward, do not re-elicit
• The data corpus: what kind of data, how many items, who from
• The data set for this particular analysis (may be the whole corpus or a subset — see Braun & Clarke, p. 6)

The four upfront analytic decisions (see references/upfront-decisions.md for full guidance):

1. Rich description of the whole data set, or detailed account of one aspect?
2. Inductive (bottom-up) or theoretical/deductive (top-down) coding?
3. Semantic themes (surface meaning) or latent themes (underlying ideas, assumptions, ideologies)?
4. Epistemology: essentialist/realist, contextualist, or constructionist?

These decisions are inter-related. Tendencies cluster: realist + semantic + inductive + rich description; constructionist + latent + theoretical + detailed account. But other combinations are valid — what matters is that the choices are explicit and internally consistent.

Walk the user through each decision. Do not assume realist + semantic + inductive by default just because the paper notes this is the common (often unspoken) default. Ask.

Confirm the plan

Before moving to Phase 2, produce a short plan summary and ask the user to confirm:

• Research question
• Data set (what items, how many)
• The four decisions (with one-sentence rationale for each)
• Whether engagement with prior literature happens before or after coding (inductive work usually delays it; theoretical work requires it upfront)

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Phase 2 (skill workflow): Familiarising yourself with the data

The first of Braun and Clarke's six phases. This phase is immersion.

Ask the user to confirm that transcription (if needed) has been done. The transcript must be at minimum a rigorous orthographic verbatim record — every word spoken, including non-verbal utterances where they carry meaning (laughter, sighs, "um", "you know"). TA does not require Jefferson-style detail.

In this phase:

• Read every data item at least once before coding starts.
• Read actively — search for meanings, oddities, contradictions, patterns.
• Take notes as you read. Jot down initial ideas, hunches, and possible codes. These are not yet codes; they are a starting list to feed Phase 3.

Output of this phase: a familiarisation note for the user — a paragraph per data item summarising what struck you, plus a running list of initial ideas across the data set. Save this to the workspace as phase2_familiarisation.md.

If the data set is too large for full re-reading in one pass, do it in batches and combine the notes.

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Phase 3 (skill workflow): Generating initial codes

Before coding starts, re-read the research question(s) saved in step0_research_questions.md. Coding is inclusive but not undisciplined — the question is the compass.

A code identifies a feature of the data — semantic content or latent meaning — that appears interesting to the analyst. A code is the most basic segment of raw data that can be assessed in a meaningful way (Braun & Clarke, 2006, p. 18, citing Boyatzis).

Codes are not themes. Codes are smaller, narrower, more numerous. Themes come later.

For full guidance on what good coding looks like (including data-driven vs theory-driven approaches, manual vs software coding, inclusive coding, and contradictions), read references/coding-guide.md.

In this phase:

• Work systematically through every data item. Give equal attention to each.
• Code for as many potential themes/patterns as possible — you do not yet know what will matter.
• Code extracts inclusively — keep a little surrounding context so meaning is not lost.
• A single extract can be coded under multiple codes, or none.
• Retain accounts that depart from the dominant story; do not smooth them out.

Output of this phase: a coded data table. For each data item, list the extracts and the code(s) applied to each. Save as phase3_codes.md. At the end, produce a consolidated code list with every code and the data extracts that sit under it.

A short worked example showing data → code, modelled on Braun and Clarke's Figure 1:

| Data extract | Codes applied | |---|---| | "it's too much like hard work I mean how much paper have you got to sign to change a flippin' name no I I mean no I no we we have thought about it half heartedly and thought no no I jus- I can't be bothered" | (1) Talked about with partner; (2) Too much hassle to change name |

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Phase 4 (skill workflow): Searching for themes

A theme captures something important about the data in relation to the research question, and represents some level of patterned response or meaning across the data set.

Prevalence matters but is not decisive. A theme can appear in many items briefly, or in a few items at length. Researcher judgement — guided by the research question — decides what is a theme.

In this phase:

• Sort codes into candidate themes. Some codes will become themes, some will be sub-themes, some will be discarded, some will sit in a temporary "miscellaneous" pile.
• Look for relationships between codes, between themes, and between levels of themes (overarching themes vs sub-themes).
• Produce an initial thematic map — a visual sketch (mind-map style) showing candidate themes and how codes feed into them. See references/thematic-map.md.

Output of this phase: a draft thematic map (saved as phase4_initial_map.png or as a markdown outline if a visual is not yet practical) and a candidate theme list with the codes under each.

End this phase with candidate themes, sub-themes, and all coded extracts grouped under them. Do not discard anything yet — Phase 5 will tell you whether the themes hold.

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Phase 5 (skill workflow): Reviewing themes

Refining