| name | guide-llm-declaration |
| description | Generate, progressively build, and finalize an AI usage declaration for a research paper using the GUIDE-LLM checklist (Feuerriegel et al., 2026, Nature Human Behaviour). Use this skill whenever a researcher needs to declare, document, or report AI/LLM usage in a study — even if they say "add AI disclosure", "write the AI section", "fill in the LLM checklist", "prepare my AI declaration", "document how I used Claude/GPT/Gemini in my research", or "append AI usage to my paper". The output is always a Markdown declaration appendix, suitable for direct inclusion in a manuscript. Supports incremental updates — the researcher can invoke this skill multiple times as their study progresses, updating fields as decisions are made. Accepts input in any format (PDF, DOCX, .tex, plain text, prior GUIDE-LLM draft).
|
GUIDE-LLM Declaration Skill
This skill helps researchers build a complete, accurate GUIDE-LLM checklist declaration that can be appended as a supplementary section to any research paper. The declaration tracks AI/LLM usage across the full research lifecycle.
Citation to embed in all outputs:
Feuerriegel, S., Barrie, C., Crockett, M. J., et al. (2026). A reporting checklist for LLMs in behavioural science. Nature Human Behaviour. DOI: 10.1038/s41562-026-02492-7
Step 0 — Input Handling
Before doing anything else, check what the researcher has provided:
| Input type | Action |
|---|
| No prior draft | Proceed to Step 1 (Interview) |
| Prior Markdown draft | Load it, identify blank/N/A fields, proceed to Step 2 (Fill gaps) |
| DOCX / PDF / .tex file | Use the file-reading skill or pdf-reading skill to extract content, map to checklist fields, then proceed to Step 2 |
| Conversational description | Extract answers from conversation history, map to fields, confirm with researcher, then proceed to Step 2 |
If the input is a .tex file (such as GUIDE-LLM.tex), parse the longtable environments to extract item labels (A.1, B.1, etc.) and any existing answers in the second column.
Step 1 — Interview (First Invocation)
If no prior draft exists, ask the researcher the following in a single message. Keep it conversational — do not ask all 30+ checklist items at once. Start with the high-level scope, then drill down.
Opening questions (always ask these first)
- Paper title / working title — for the declaration header
- Scope of LLM use — which stages? (research design / data processing / analysis / LLM as object / participant-facing / writing & editing)
- Which LLM(s) were used? — provider, model name, exact version if known, date accessed
- How were they accessed? — API / web interface / local / other
- Were outputs reviewed by a human, or fully automated?
After receiving answers, generate a partial draft immediately (Step 3), marking unknown fields as <!-- TODO: fill in -->. Tell the researcher which fields still need input and offer to collect them in follow-up turns.
Step 2 — Progressive Update Mode
When the researcher returns with more information (e.g., "I finished the data collection phase"), or uploads a new version of their draft:
- Load the existing Markdown declaration
- Identify which fields have changed or were left as TODO
- Ask targeted questions only for the new phase/fields
- Merge answers into the declaration
- Output the updated full declaration
This allows the declaration to grow organically as the research progresses.
Step 3 — Generate the Declaration
Output the declaration as a Markdown document using the structure below. All fields map 1:1 to GUIDE-LLM checklist items. See references/checklist-items.md for full item text and guidance.
Declaration template
---
# AI Usage Declaration
**GUIDE-LLM Checklist** (v1.1)
Feuerriegel et al. (2026). *A reporting checklist for LLMs in behavioural science.*
Nature Human Behaviour. DOI: [10.1038/s41562-026-02492-7](https://doi.org/10.1038/s41562-026-02492-7)
**Paper:** [Paper title]
**Prepared by:** [Author name(s)]
**Date:** [Date of last update]
---
## Section A — Scope of LLM Use
| Item | Response |
|------|----------|
| **A.1** Purposes of LLM use | [e.g., "Research design (hypothesis generation), Analysis (data labeling), Communication (editing)"] |
| **A.2** Degree of automation | [e.g., "All outputs reviewed by PI; no automated pipeline"] |
---
## Section B — Model / System Details
| Item | Response |
|------|----------|
| **B.1** Model name, provider, version, date, source | [e.g., "claude-sonnet-4-20250514 (Anthropic); accessed via Claude.ai web interface, May 2025"] |
| **B.2** Access mode & context mode | [e.g., "Web interface, chat mode (persistent context window)"] |
| **B.3** Relevant configurations | [e.g., "Default settings; no custom temperature or sampling parameters"] |
| **B.4** Customization | ☐ Base model / ☐ Fine-tuning / ☐ RAG / ☐ Automated prompt optimization / ☐ Tool/function calling / ☐ Web search / ☐ Agentic workflows / ☐ Other — Description: [value] |
| **B.5** Persistent memory across sessions | ☐ Yes / ☑ No / ☐ N/A |
---
## Section C — Prompts
| Item | Response |
|------|----------|
| **C.1** Exact prompts reported | [Include verbatim prompts or link to repository] |
| **C.2** System-wide instructions | [e.g., "No system prompt used; default assistant mode"] |
---
## Section D — Data Inputs & Privacy
| Item | Response |
|------|----------|
| **D.1** Handling of personal/sensitive data | [e.g., "No personal data were processed. All inputs were anonymized research texts."] |
---
## Section E — Validation & Interpretation
| Item | Response |
|------|----------|
| **E.1** Human validation of LLM outputs | ☐ Yes / ☐ No / ☐ N/A — Description: [value] |
| **E.2** Post-processing steps | [e.g., "Outputs were manually reviewed and reformatted into CSV; no automated parsing"] |
---
## Section F — Reproducibility
| Item | Response |
|------|----------|
| **F.1** Code/scripts shared | ☐ Yes / ☐ No / ☐ N/A — Link/DOI: [value] |
---
## Section G — Competing Interests
| Item | Response |
|------|----------|
| **G.1** Funding, support, affiliations | ☐ Yes / ☑ No — Description: [value] |
---
## Optional Items
| Item | Response |
|------|----------|
| Justification for LLM choice | ☐ Performance / ☐ Transparency / ☐ Reproducibility / ☐ Ethical / ☐ Other |
| Rationale for prompt design | [value or N/A] |
| Comparison against other LLMs/methods | ☐ Yes / ☐ No / ☐ N/A |
| Training data leakage risks | ☐ Yes / ☐ No / ☐ N/A |
| Potential bias in LLM behavior | ☐ Yes / ☐ No |
| Conversation transcripts | ☐ Yes / ☐ No / ☐ N/A |
| Ethical implications | [value or N/A] |
| Computational resources | [value or N/A] |
---
*This declaration was prepared using the GUIDE-LLM checklist (v1.1). For the full checklist and guidance, see [llm-checklist.com](https://llm-checklist.com).*
Step 4 — Finalisation Check
Before outputting the final declaration (when the researcher signals the paper is ready for submission), run through this checklist mentally:
If any required fields are still blank, prompt the researcher to fill them before finalizing.
Flag optional items that are particularly relevant given the research context:
- If LLM was participant-facing → flag conversation transcripts and ethics
- If LLM was used for data labeling → flag human validation (E.1) and inter-rater reliability
- If sensitive/personal data → flag D.1 carefully; note applicable data protection frameworks (e.g., GDPR, HIPAA, or relevant national law)
Step 5 — Output the Final Markdown File
When the researcher is satisfied, output the completed declaration as a standalone .md file named:
GUIDE-LLM-Declaration-[PaperShortTitle]-[YYYY-MM-DD].md
Include at the top:
- Paper title
- Date of declaration
- GUIDE-LLM version (1.1)
- Citation to Feuerriegel et al. (2026)
Tell the researcher: "Attach this file as a supplementary appendix to your manuscript submission."
Reference files
references/checklist-items.md — Full text of all GUIDE-LLM items A.1–G.1 plus all optional items, with explanations. Read this when the researcher asks about a specific item or when generating detailed guidance.
