content-analysis
Scientific content analysis for academic research. Supports the full workflow from research question to publication-ready output: codebook d
Open source Repository Open in the app JSON README (API)
About
Scientific content analysis for academic research. Supports the full workflow from research question to publication-ready output: codebook development, qualitative and quantitative coding, thematic analysis, and inter-coder reliability assessment. Follows established methodology from Krippendorff, Hsieh & Shannon, Mayring, and Braun & Clarke. Produces structured output files including coded data tables (.xlsx), theme summaries (.md), and reliability reports.
Details
- Kind
- Plugins
- Topic
- Government & public data
- Publisher
- petermalmkjaer
- Origin
- marketplace
- Category
- ferramentas
- Stars
- 1
- Forks
- 2
- Open pull requests
- 2
- Last push
- 2026-09-04T06:59:30Z
- Repository state
- ativo
- License
- NOASSERTION
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
petermalmkjaer/content-analysis-plugin/content-analysis
README
# Content Analysis Plugin Scientific content analysis for academic research, built for Claude. **Author:** Peter Malmkjaer ([Peter.Malmkjaer@mail.dk](mailto:Peter.Malmkjaer@mail.dk)) Independent project by Peter Malmkjaer. **Version:** 4.2.2 --- ## What It Does This plugin enables Claude to perform rigorous, methodologically grounded content analysis on textual material. It follows established social-science conventions from Krippendorff (2018), Hsieh & Shannon (2005), Mayring (2014), and Braun & Clarke (2006). It supports the full workflow from research question to publication-ready output files. --- ## Installation Add the plugin to your Claude marketplace and install it: ```bash /plugin marketplace add PeterMalmkjaer/content-analysis-plugin /plugin install content-analysis@content-analysis-plugin ``` Or from the terminal with the Claude Code CLI: ```bash claude plugin marketplace add PeterMalmkjaer/content-analysis-plugin claude plugin install content-analysis@content-analysis-plugin ``` Once installed, the skill activates automatically when you describe a content-analysis task. You can also invoke the workflow explicitly with the `/content-analysis` command. --- ## Components ### Skill: `content-analysis` Activates automatically when you mention phrases such as "content analysis", "codebook", "thematic analysis", "code the data", "qualitative coding", or "inter-coder reliability". Provides an eight-step workflow: 1. Clarify research question and unit of analysis 2. Select approach (quantitative / qualitative / mixed) 3. Select coding logic (deductive / inductive / abductive) 4. Develop coding scheme (codebook) 5. Pilot-code a sample 6. Assess inter-coder reliability 7. Code the full corpus 8. Analyse and report ### Reference Files - [`skills/content-analysis/references/reliability-guide.md`](skills/content-analysis/references/reliability-guide.md) — Inter-coder reliability metrics (Cohen's κ, Fleiss' κ, Krippendorff's α, percentage agreement) with formulas, worked examples, and Python implementation. - [`skills/content-analysis/references/coding-approaches.md`](skills/content-analysis/references/coding-approaches.md) — Comparative reference for Mayring's qualitative content analysis, Hsieh & Shannon's three approaches, and Braun & Clarke's thematic analysis, with a decision guide. - [`skills/content-analysis/references/protocol-driven-runs.md`](skills/content-analysis/references/protocol-driven-runs.md) — Rules for analyses commissioned in writing or run unattended: deriving the research question when the brief does not state one, the required report skeleton, output-format precedence, and how to proceed without blocking on unanswerable questions. ### Command: `/content-analysis` Run `/content-analysis` to start a content-analysis session directly. Optionally pass arguments describing your documents or research focus. --- ## Output Files | File | Format | Description | |------|--------|-------------| | Codebook | `.xlsx` | Codes with definitions, inclusion/exclusion criteria, examples | | Coded data | `.xlsx` | Every coded unit with excerpts, codes, and notes | | Theme summary | `.md` / `.docx` | Narrative findings with method note | | Thematic map | `.mermaid` | Visual theme hierarchy | | Reliability report | `.xlsx` | ICR calculations (when applicable) | --- ## Usage Example ``` User: "Use Content analysis. I have 12 interview transcripts about remote work attitudes among knowledge workers. I want to identify themes inductively." Claude will: 1. Confirm the research question and propose speaking turns as the unit of analysis. 2. Recommend Hsieh & Shannon's Conventional approach (data-driven, no preconceived categories). 3. Read a 10–20% sample to develop initial codes through open coding. 4. Group codes into higher-level categories (axial coding) and present a draft codebook for your review. 5. Pilot-code a small subset to test the codebook, revise as needed. 6. Code the full corpus and produce coded_data.xlsx and a theme summary with a method note. ``` The skill always begins by clarifying your research question and unit of analysis. It will not start coding until these are explicit. --- ## Written Briefs and Unattended Runs The workflow above assumes a conversation. Analyses are often commissioned differently: a written protocol defines the task, names the deliverables, and no one is available to answer questions while the run proceeds. For those cases the skill loads `references/protocol-driven-runs.md`, which supplies four rules that replace the conversational assumptions: 1. **Derive the research question.** Briefs typically specify what to do but not what the analysis is for. A deliverable list is not a research question. The skill derives one from the brief's own terms, marks it explicitly as derived, decomposes it into one sub-question per coded dimension, and never proceeds without one. 2. **Use the full report skeleton.** Task · problem statement and analysis questions · constraints declaration · method · findings (one section per question) · uncertainties · deliverables · references. Written briefs mean the report will be read by someone who was not present. 3. **The brief's formats take precedence.** Named deliverables, names and paths override the `.xlsx` conventions in the Output Files table above. The format changes; the substance does not. 4. **Do not block.** In an unattended run, make the most defensible choice, state the assumption at the point it applies, record the rejected alternative, and continue. Nothing changes for conversational use — the rules apply only when the run is protocol-driven or unattended. --- ## Methodological Transparency Every analysis produced with this skill includes a method note documenting the research question, corpus description, unit of analysis, approach, coding logic, codebook development process, reliability assessment (when applicable), and limitations. This is essential for academic credibility and reviewers in any peer-reviewed venue. The skill is explicit about the limitations of LLM-assisted coding: latent content (irony, sarcasm, cultural subtext) may be missed, and results should ideally be validated against a human-coded subsample before publication. --- ## License This plugin is released under a **dual license**. See [LICENSE](LICENSE) for full terms. ### Academic & Non-Commercial Use (Free) Free for academic research, teaching, and non-commercial use. You may use, copy, and adapt the skill for scholarly purposes provided you credit the author: > Content Analysis Skill by Peter Malmkjaer ### Commercial Use (Paid License Required) Any commercial use — including integration into paid products, consulting services, corporate training, or resale on marketplaces — requires a separate commercial license. Contact the author for terms and pricing: **Peter Malmkjaer** — [Peter.Malmkjaer@mail.dk](mailto:Peter.Malmkjaer@mail.dk) © 2026 Peter Malmkjaer. All rights reserved. --- ## Citation If you use this plugin in academic work, please cite: > Malmkjaer, P. (2026). *Content Analysis Plugin for Claude* (Version 1.1.0) [Computer software]. https://github.com/PeterMalmkjaer/content-analysis-plugin