A11: Good Practice Examples — Researchers Using Claude Code

Compiled from web research, March 2026. Sources verified.

The headline example: A humanities scholar building research tools with no programming background

David M. Berry — "Vibe Decoding: Building the Critical Code Studies Workbench"

Over 18 days (January 19 – February 6, 2026), Berry built a web application for collaborative scholarly annotation of source code — specifically to support distributed annotation of the ELIZA source code, for which existing tools (Google Docs, etc.) were inadequate.

Tool used: Claude Sonnet 4.5 in the Claude Code environment. No prior programming experience.

Berry explicitly frames this as a record of "what AI-augmented development looks like when undertaken by a humanities scholar rather than a professional software engineer." He terms the approach "vibe coding" and argues it changes "the conditions under which scholarly tools can be made" — humanities scholars can now build bespoke research infrastructure that previously required a funded development team.

Why this matters for the workshop: This is the most direct proof-of-concept. A historian's equivalent would be: building a custom tool to annotate inquisition depositions, track name variants across documents, or visualize network connections — tasks DISSINET researchers might otherwise wait months for a developer to build.

Social scientists using Claude Code for real research tasks

Scott Cunningham — "Claude Code for Quantitative Social Scientists"

Cunningham has been documenting Claude Code applied to real social science projects since December 2025:

Cunningham calls Claude Code an "experience good" — its value only becomes apparent through direct use. He notes that nearly all online Claude Code content is abstract; his series provides concrete empirical examples with honest reflections on failure.

For the workshop: Good illustration that systematic, large-scale textual analysis (200,000 documents) is within reach of a social scientist using Claude Code without a computer science collaborator.

Solomon Messing and Joshua Tucker — "The Train Has Left the Station"

Documented productivity cases:

These are quantitative social science examples — but the speed and scale translate directly to humanities tasks: a 20-page structured analysis of a corpus of sources, a systematic comparison across languages, a documented processing pipeline.

The non-programmer entry point — documented

Teresa Torres — "Claude Code: What It Is, How It's Different, and Why Non-Technical People Should Use It"

Key quotes directly useful for the workshop:

Every.to / Katie Parrott — "How to Use Claude Code for Everyday Tasks — No Programming Required"

Dan Shipper's analogy:

Chris Blattman — political economist, University of Chicago

Blattman is probably the most important example for the workshop's pitch: a senior academic with no coding background using Claude Code for complex, sustained research work. His use cases are not experiments — they are real workflows:

The key framing from Blattman: he came to Claude Code not looking for a programming tool but for a research assistant that could work with his files. The fact that it runs in a terminal was incidental; what mattered was that it could act on documents without him pasting them one by one.

For the workshop: Use the "I have never coded in my life" quote directly. It is the clearest one-sentence rebuttal to "this isn't for me."

Qualitative research: coding, annotation, and interview analysis

Three accounts of using Claude Code (or equivalent agentic environments) for qualitative data work — the use case most likely to resonate with DISSINET researchers who work with interview corpora, deposition records, and narrative sources.

Xule Lin — "Interpretive Orchestration" (Imperial College London)

Lin's project is the most intellectually serious treatment of qualitative coding with AI from within the research community. He rejects both naive automation ("AI codes for me") and dismissive scepticism ("AI cannot do interpretation"). His framework: epistemic partnership — the researcher and AI deepen each other's reflexivity through structured dialogue.

The practical system: four specialised Claude Code agents (@dialogical-coder, @research-configurator, @scholarly-companion, and a synthesis agent), a three-stage methodology: 1. Solo practice — researcher manually codes 10–15 documents to develop interpretive instincts before involving AI 2. Collaboration — human-AI joint coding with transparent reasoning at every step 3. Synthesis — findings examined against scholarly tradition, not just data

Lin's framing addresses the workshop's core tension directly: AI can offer scale AND interpretive depth — but only if the researcher maintains interpretive authority at each stage rather than accepting AI output as finished analysis.

Fatin Asnan — thematic coding of 1,000+ user feedback responses

A concrete, reproducible account of thematic coding at scale. Workflow: exported responses to CSV, instructed Claude Code as a "seasoned qualitative researcher" with a defined coding schema, tested on a 50-response sample before full run, validated output in Google Sheets with multi-theme tagging and spot-checking.

Key honest finding: Claude's first pass produced overlapping and inconsistent codes. The iterative refinement — revising the schema and re-running — was where the value appeared. This is not one-shot automation; it is AI-augmented iteration.

Child Trends — qualitative interview transcripts (sensitive research data)

Documents the full pipeline: de-identification before any AI processing, two-stage inductive/deductive coding, detailed prompts with smaller batches to reduce error rates, mandatory human review of final themes. Findings: AI initially produced vague overlapping themes; iterative human correction was essential.

The de-identification protocol is directly relevant for DISSINET work involving sensitive archival material: the lesson is not "AI cannot handle this" but "AI can handle this if you apply the same protocols you would for any research data."

Corpus linguistics — LLM annotation of historical texts

Systematic study of Claude 3.5 Sonnet for pragmatic annotation of Early Modern English texts. The paper explicitly notes that Claude provides "a powerful and accessible interface that does not require programming expertise." Inter-coder agreement between Claude and human annotators: moderate — comparable to disagreement between human coders on ambiguous cases. The finding is not that AI is as good as a human expert, but that it is as good as a second human coder for well-defined annotation tasks, which is the operationally relevant threshold for augmentation.

Directly applicable to DISSINET: annotation of Latin inquisition depositions, classification of narrative elements in trial records, extraction of relationship labels from structured sources.

The DH methodological framework: Promptotyping

Christopher Pollin / Digital Humanities Craft

Pollin proposes Promptotyping as a rigorous alternative to naive "vibe coding" — a three-stage framework: 1. Prompt engineering (clear, structured task definition) 2. Iterative requirements engineering (test, observe, refine) 3. Critical-epistemological reflection (interrogate the output; do not accept confident-sounding answers uncritically)

His Critical-Expert-in-the-Loop concept: dual verification — human checks output AND you explicitly ask Claude to critique its own work ("what could be wrong with this? what did you assume?").

Concrete example: Built a timeline-annotation tool for the Stefan Zweig Digital project — fetches XML metadata from historical correspondence records, enables researcher annotations, exports JSON for scholarly integration — in two hours.

The LLM Summer School materials are CC BY 4.0 and cover prompt engineering, digital editions (TEI), and promptotyping. Directly reusable for DISSINET workshop development.

Research infrastructure: the personal knowledge agent

Seth Lazar — "How to use coding agents for philosophy research"

Lazar built a personal research agent named "Minty" using Claude Code. Tasks it runs autonomously:

Lazar's framing: the problem he is solving is "InfoGlut" — the structural impossibility of reading everything relevant in a rapidly moving field. Minty does not replace his reading; it triage-reads at scale so that his reading time is spent on material that has already been filtered for relevance.

Why this matters for humanities researchers: The same logic applies to anyone tracking a fast-moving scholarly conversation, monitoring a corpus of new digitisation releases, or maintaining awareness of a large document set over time. The agent is not doing the thinking; it is doing the filtering and indexing that makes thinking possible.

Batch PDF reading: documented approaches

The "I have 50 PDFs and need to read them all" scenario is the most common research use case and the one most clearly differentiated from Desktop. Here is what practitioners have documented.

Scott Cunningham — "The Split-PDF Workflow" (Claude Code Part 13)

Cunningham found that feeding long academic PDFs to Claude Code directly produced two failure modes: session crashes (the PDF exceeded what Claude could hold at once) or shallow hallucinated output (Claude appeared to summarise but was confabulating). His solution: split PDFs into sections first, process each section sequentially, accumulate results in a running markdown file.

The split-PDF workflow "externalises understanding" — rather than hoping Claude summarises the whole document correctly, you force section-by-section attention and build the synthesis file incrementally. Each section produces notes; the final prompt synthesises the notes. The intermediate file is both the output and the audit trail.

For DISSINET: The same logic applies to long trial registers or multi-folio transcriptions. Split by folio range or topical section, extract persons and events from each part, then consolidate.

Research Memex — Systematic Literature Review with 100–200 papers

Documents a full pipeline for systematic literature reviews using Claude Code at scale. The key technical choice: MinerU MCP, which processes up to 200 documents in parallel with 90%+ accuracy on complex academic layouts (multi-column, embedded tables, figures). Workflow: export from Zotero → convert to markdown in /00_literature_files/ → Claude Code processes the markdown, not the PDFs.

The document is honest about where human oversight remains mandatory: borderline inclusion/exclusion decisions, theoretical framing, gap identification, and final synthesis arguments. These are execution-vs.-interpretation limits: AI handles classification at scale; the researcher handles what it means.

pdf-mcp — a tool built specifically to solve Claude's large-PDF problem

Kevin Tan built this after repeatedly hitting Claude's limits with large PDFs. Rather than loading the whole document, pdf-mcp provides Claude with specialised tools: search the PDF by keyword, read specific page ranges, retrieve the table of contents, extract tables. Claude explores the document surgically rather than loading it all at once.

The insight this produces: For large PDFs, the bottleneck is not Claude's reasoning — it is how much of the document fits in the context window at once. Tools that let Claude navigate a document (rather than consume it) solve a different problem than tools that convert it to smaller text. Both have their place: conversion works best for batch summarisation; surgical navigation works best for targeted extraction from long documents.

Large-scale academic research with multi-agent Claude Code

"Using Claude Code for academic research at scale (The Agents Research Lab)"

A researcher documenting what they call an "Agents Research Lab" workflow — using Claude Code to orchestrate multiple agents across a literature corpus at a scale not possible in Claude Desktop. Key patterns: parallel processing of sources, agents checking each other's outputs, persistent project memory across sessions, and structured output for downstream analysis. The thread includes community discussion of what scales and what breaks.

Why this matters: One of the more detailed public accounts of multi-agent Claude Code applied to genuine research tasks, not toy examples.

AI as research collaborator: the co-scientist paradigm

Google DeepMind — "Accelerating scientific breakthroughs with an AI co-scientist"

Google's proposal and implementation of an AI co-scientist system — not Claude, but directly relevant as a framework for thinking about what AI-assisted research looks like at the frontier. The system is designed to generate novel hypotheses, critique them, and iterate in a cycle that models how scientific thinking works.

What it offers for the workshop: A conceptual anchor for the question "what does genuine AI-augmented research mean?" The co-scientist model is explicit that AI is not replacing researcher judgment — it is providing a systematic partner for hypothesis generation and critique. The parallel with Claude's role in B.lifecycle.1.creativity (Socratic partner, not question generator) is direct. The paper also illustrates the execution/interpretation distinction at scale: the system generates and evaluates hypotheses, but the researcher still selects, contextualises, and takes responsibility.

For the workshop: the core demonstration

The most powerful live demonstration combining these examples:

  1. Navigate to a folder of PDFs in the terminal (30 seconds)
  2. Ask Claude Code: "Read each PDF, extract all named persons and places, and write the results to a structured CSV"
  3. Watch Claude work through the files one by one (visible in terminal)
  4. Open the CSV

This takes 2–5 minutes. It is something no participant can do in Claude Desktop in the same session. It requires no coding. It is directly analogous to what historians do with archival sources. And it has been documented as working by multiple researchers.

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