Performs qualitative coding and thematic analysis of text corpora using the bewley CLI and EDSL. Covers project setup, open coding, code refinement, annotation, memo-writing, and export. Use when the user wants to analyze text of any kind -- transcripts, speeches, reviews, articles, field notes, open-ended survey responses, social media posts, or any unstructured text data. Triggers on requests to code, tag, theme, categorize, or find patterns in text, including content analysis and discourse analysis.
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Performs qualitative coding and thematic analysis of text corpora using the bewley CLI and EDSL. Covers project setup, open coding, code refinement, annotation, memo-writing, and export. Use when the user wants to analyze text of any kind -- transcripts, speeches, reviews, articles, field notes, open-ended survey responses, social media posts, or any unstructured text data. Triggers on requests to code, tag, theme, categorize, or find patterns in text, including content analysis and discourse analysis.
This skill guides qualitative coding of text corpora using the bewley CLI. It covers installation, the core workflow, and EDSL-assisted open coding.
For the full bewley command reference, see references/bewley-commands.md.
For grounded theory methodology and the corresponding bewley workflow, see references/grounded-theory.md.
Project: A directory containing a corpus/ folder and a .bewley/ metadata directory. Created with bewley init.
Document: A UTF-8 text file tracked by bewley (e.g., an interview transcript).
Code: A named analytic label applied to text (e.g., "trust", "themes/rapport").
Annotation: An application of a code to a whole document or a line span within a document revision. See "Annotation granularity" below for when to use each.
Memo: A free-text analytic note attached to a code, document, or the project.
Event log: Append-only JSON log in .bewley/events/. This is the source of truth. SQLite is a rebuildable cache.
References (document_ref, code_ref) accept UUIDs, names, paths, or path prefixes.
Getting help
Every command and subcommand has detailed --help output:
When unsure about a command's arguments or output format, always run --help first.
Project location
Qualitative analysis studies use the same directory layout as other research projects (see skill:workflow-file-layout). The bewley corpus and coding data live inside the study's data/ folder:
Query and review: bewley query '<expr>' and bewley show snippets --code <ref>
Build hierarchy: bewley code set-parent and bewley code link
Export: bewley export snippets, bewley export theory, etc.
Render theory diagram: Export with bewley export theory --format json --output theory.json, then choose a rendering format. For report embedding, use python scripts/render_collapsible_diagram.py theory.json --flow flow.yml -o theory_interactive.html (collapsible theme blocks, best for readers). For analyst exploration, use python scripts/render_theory_diagram.py theory.json --format html -o theory.html (D3 force-directed, drag/zoom/hover). For print/PDF, use --format svg. See references/grounded-theory.md "Collapsible report diagrams" for the flow spec format and embedding instructions.
Makefile target: Add a qualitative_report target that runs bewley -H export html and copies the output into the writeup/ folder. The writeup should reference the generated HTML report.
Open coding with EDSL
Bewley includes a script for generating candidate qualitative codes using EDSL. This automates the initial open coding pass.
Before generating codes, read all documents and write a corpus summary:
Run bewley list documents to get all document paths.
Read each document from the corpus/ directory.
Write a corpus_summary.md with:
What kind of texts the corpus contains (interviews, field notes, etc.)
Approximate size and scope (number of documents, topics covered)
Initial impressions of recurring themes or notable features
Any contextual information about the research setting
This summary provides shared context so the LLM can generate codes that are coherent across the whole corpus, not just locally relevant to each document.
Step 2: Generate candidate codes
python scripts/generate_candidate_codes.py
Options:
--project-dir DIR -- path to the bewley project (default: current directory)
--summary FILE -- path to corpus summary (default: corpus_summary.md)
--model MODEL -- EDSL model name (e.g., claude-3-5-sonnet-20241022)
The script reads the corpus summary and all documents, builds an EDSL ScenarioList (one Scenario per document), and runs a Survey asking an LLM to suggest open codes for each document. Results are saved to candidate_codes.csv with columns: code_name, description, quote, source_document_id, source_document_path.
Quote-anchored coding. The LLM returns a verbatim quote from the document for each code instead of line numbers. This is critical -- LLMs cannot reliably count line numbers, especially in documents with metadata headers or blank lines. The quote is then resolved to byte/line ranges programmatically (see Step 2b).
This script fuzzy-matches each quote against its source document using three strategies (exact, normalized, longest common substring) and outputs a CSV with start_byte, end_byte, start_line, end_line columns. Unresolved quotes are written to a separate file for manual review.
Always review the unresolved file -- if more than ~10% of quotes fail to resolve, the LLM may be paraphrasing rather than quoting verbatim. In that case, rerun the coding pass with a stronger prompt emphasis on exact copying.
Step 3: Refine and apply codes
After reviewing candidate_codes_resolved.csv:
Deduplicate: Merge near-synonyms (e.g., trust_building and building_trust)
Thematic codes (e.g., route_pressure, dehumanization) should be span-level (--lines S:E). These codes are grounded in specific passages, and anchoring them to lines produces better exports (the quote explorer shows the actual text) and better auditability.
Document-level codes (e.g., cautionary_tone, ambivalent_assessment, positive_overall) are fine as document-level (--document). These describe the character or framing of the review as a whole, not a specific passage.
# Thematic code -- anchor to the passage that supports it
bewley annotate apply route_pressure <doc_id> --lines 8:10
# Document-level code -- describes the overall document
bewley annotate apply cautionary_tone <doc_id> --document
You must specify exactly one of --document, --bytes S:E, or --lines S:E. Omitting the scope flag is a silent error — bewley prints bewley error: with an empty message and exits with code 1. When generating batch annotation scripts, always include the scope flag and check exit codes.
Working directory discipline
Bewley has no --cwd flag. It discovers its project by looking for .bewley/ in the current working directory. Every bewley command must be run from the project directory (e.g., data/qualitative-coding/).
Shell state is not preserved between Bash tool calls. Always cd with an absolute path before each bewley command:
cd /absolute/path/to/sessions/topic_foo/study_a/data/qualitative-coding && bewley list documents
Define QUAL_DIR early in the session and reuse it:
QUAL_DIR="/absolute/path/to/sessions/topic_foo/study_a/data/qualitative-coding"cd"$QUAL_DIR" && bewley status
Anti-pattern — do NOT do this:
bewley --cwd /some/path list documents # WRONG: --cwd does not exist
Important notes
All IDs are UUIDs. Capture and reuse them from command output.
The --lines flag uses 1-based inclusive ranges (e.g., --lines 10:20 means lines 10 through 20). The --bytes flag uses 0-based, exclusive-end ranges.
Document updates may cause annotations to become conflicted if fuzzy relocation fails. Check bewley status for conflicted_annotations > 0 and resolve with bewley annotate resolve.
The event log is append-only. bewley undo does not delete history; it appends a compensating event.
bewley rebuild-index can recover from any index corruption since SQLite is a cache, not the source of truth.
When passing query expressions containing &, |, !, or parentheses, always quote the expression to prevent shell interpretation.
All commands produce JSON output.
Mutating commands print the ID of the created or affected entity.
Errors go to stderr. Exit code is 0 on success, 1 on failure.
