academic-aio

Files (24)

• SKILL.md
• ai_tool_citation_framing.md · references/
• kjr_mllm_2025.md · references/case_studies/
• AIO_GENERAL.md · references/checklists/
• journal_summarybox_templates.yaml · references/
• launch_sequencing.md · references/
• oac_funding_checklist.yaml · references/
• pre_draft_strategy.md · references/
• reporting_guideline_mapping.md · references/
• repository_and_cards.md · references/
• CodeRepository.jsonld · references/schema_markup_templates/
• Dataset.jsonld · references/schema_markup_templates/
• Person.jsonld · references/schema_markup_templates/
• README.md · references/schema_markup_templates/
• ScholarlyArticle.jsonld · references/schema_markup_templates/
• summary_box_specs.json · references/
• batch_metadata_audit.py · scripts/
• check_summary_box.py · scripts/
• validate_schema.py · scripts/
• skill.yml
• aio_audit_checklist.md.j2 · templates/
• test_batch_metadata_audit.sh · tests/
• test_summary_box.sh · tests/
• test_validate_schema.sh · tests/

SKILL.md (first lines)

# Academic AIO Skill — Medical AI Paper Visibility for AI Search Engines

## Communication Rules

- Surface the checklist in the response. Never apply AIO edits silently.
- When a rule conflicts with journal formatting, defer to the journal and mark the item NA with the reason.
- When introducing a rule, cite its source (TRIPOD+AI, CLAIM, STARD-AI, Wu 2025, Algaba 2024, Aggarwal 2024 GEO) by DOI or arXiv ID from External References. Never invent a citation, DOI, arXiv ID, or reporting-guideline item number; mark anything you cannot verify `[VERIFY]`.

## Section 1 — Title and Abstract Optimization

### 1.1 Title three-slot rule
Structure: `[Task] + [Modality or anatomy] + [Model family or method class]`. Include one concrete differentiator (dataset scale, new benchmark, "first …") when defensible. Avoid keyword stuffing (penalized as spam by AI overviews).
- PASS: "Transformer-based segmentation of skull fractures on non-contrast head CT."
- FAIL: "A novel advanced deep-learning AI machine-learning framework for medical image analysis."

### 1.2 Structured abstract
Use the journal-required structure (Background / Methods / Findings / Interpretation for the Lancet family; Background / Purpose / Materials and Methods / Results / Conclusion for the RSNA family; etc.). If the journal allows unstructured, still structure it internally. Each section stands alone as a semantic chunk of ≤ 3 sentences so that RAG chunk-boundary splits do not break the claim.

### 1.3 Opening and closing sentences
- First sentence: the problem AND the contribution in one line. Never open with filler ("In recent years, AI has revolutionized …") — it burns the chunk LLM summarizers extract most.
- Last sentence: explicit interpretation ("we show that …", "this implies …"). No hedging-only closes.

### 1.4 Taxonomy line
Include one sentence that names the field's controlled vocabulary ("diagnostic-accuracy study", "foundation-model evaluation", "LLM-as-judge", "agentic radiology workflow"). Entity linkers in AI indexes use this line.

### 1.5 Quantified claim
Every abstract must contain at least one numeric primary outcome with a confidence interval (for example, "AUC 0.94 [95 % CI 0.91–0.96]").

### 1.6 Reporting-guideline anchor
Name the guideline in the abstract or the opening sentence of Methods: "Reported following TRIPOD+AI (Collins 2024) and CLAIM 2024 (Tejani 2024)". Add STARD-AI 2025, DECIDE-AI, or TRIPOD-LLM when applicable. AIO-rule ↔ guideline-item mapping: `references/reporting_guideline_mapping.md`.

### 1.7 Keyword, MeSH, and RadLex coverage
Title, abstract, and keywords together should cover the concept's key terms, without repeating title terms in the keywords (92 % of the surveyed papers repeated key terms across title, abstract and keywords; Royal Society 2024, doi:10.1098/rspb.2024.1222). Include:
- Core MeSH terms (verify against the NLM MeSH browser) and RadLex terms where applicable.
- Modality synonyms ("chest radiograph (CXR)", "non-contrast CT (NCCT)").
- Both US and UK spellings when relevant.

## Section 2 — Manuscript-Level AIO

### 2.1 Summary box
Include the journal-specific summary box verbatim when supported; it is the fragment AI search engines most often copy or paraphrase:
- Lancet family: "Research in context" (Evidence before this study / Added value / Implications).
- RSNA Radiology and RYAI: 3 bullets, one claim each — labelled "Key Results" for Radiology and "Key Points" for RYAI in `references/journal_summarybox_templates.yaml`. Confirm the label in the current RSNA instructions for authors; the format check accepts either label for Radiology.
- npj Digital Medicine: no summary box; Articles carry an unstructured abstract of up to 150 words.
- Nature Medicine: editor's summary (supplied by editorial, but draft one proactively).

Journal-specific templates: `references/journal_summarybox_templates.yaml`. Never invent a summary-box rule: verify each template against the journal's current instructions for authors before applying it.

**Deterministic format check.** Validate the drafted box against its journal spec with `python3 ${CLAUDE_SKILL_DIR}/scripts/check_summary_box.py --manuscript <file> --journal <stem> --strict` (reads `references/summary_box_specs.json`: Key Points / Key Results top-level bullet count + one-claim-per-bullet, Research-in-context's three sub-blocks each opening a line and carrying text, plain-language word band). It catches the wrong-format / wrong-bullet-count box that a production technical check rejects.

**Known limits.** The box ends only at a heading or a bold-only label line, so a list under a plain-text label (`Abbreviations:`) right after the box is counted into it, and an emphasized body phrase that starts with the box label (`*Key points* of prior work ...`) is taken as the box; read the box by eye when either shape is present. In a Research-in-context box, a bold-only line after the last sub-block label ends the box, so a sub-heading inside Implications (`**For clinicians**`) cannot be told from the next section; an Implications sub-block left empty that way is reported as an advisory, not a failure.

### 2.2 Declarative section headings
Headings state a claim, not a generic label: "Model underperforms on rare-finding subset" beats "Subgroup analysis".

### 2.3 Numeric claim compression
In the Methods and in at least one Results paragraph, compress primary-outcome statistics into one sentence:
"On the internal test set (n = 842), the model achieved AUC 0.94 (95 % CI 0.91–0.96), sensitivity 88.2 % (85.1–91.0), specificity 91.4 % (88.7–93.6), at an operating point of 0.37."

### 2.4 Reproducibility block
Include a labeled block (end of Methods or a standalone Data/Code Availability section) listing data availability and license, code availability with DOI, model weights and checkpoints, prompts and configuration files, random seeds, and compute environment. Flag code described only as "available on reasonable request": scrapers read it as not reproducible and AI tools demote the paper.

### 2.4a Citing an AI-assisted tool by use-class
Frame an AI-assisted tool by what it did, not by hiding it: verification/QA and analysis uses go in a **Software / Code-availability statement** (citable); generative drafting or humanizing goes in the journal's **AI-use disclosure field**, not a citation. A self-citation by the tool's author also requires a COI disclosure and should cite only the functions the work used. Use-class table and rules: `${CLAUDE_SKILL_DIR}/references/ai_tool_citation_framing.md`.

### 2.5 Limitations enumeration
List limitations explicitly and name each one (generalizability, spectrum bias, dataset shift, single-center training, label noise). Flag "clinical grade" or "replaces radiologists" overclaims: LLM trust heuristics demote them and they invite reviewer rejection.

### 2.6 Standalone figure captions
Each caption re-states the claim, the dataset, and the metric, because captions survive in vector and image-retrieval indexes when the body text is lost.

## Section 3 — Preprint, Channel, and Indexing Strategy

### 3.1 Preprint versus fast-track
- Default: post to medRxiv (clinical), arXiv (methods, cs.CV / eess.IV), or bioRxiv on the day of journal submission. This puts the paper into Semantic Scholar within 24–72 hours and into Perplexity's web index immediately.
- Exception: if the target journal offers a fast-track review cycle (acceptance → online within roughly 30–60 days) AND the authors prefer a single canonical version, the preprint may be skipped; compensate with aggressive post-acceptance SNS seeding and PMC deposit.
- Never skip both preprint and fast-track — a paywall-only paper is invisible to most RAG pipelines.

### 3.2 Journal preprint policy
Most medical-AI venues allow preprints; a few restrict them or require disclosure. Verify the current policy on Sherpa Romeo or the journal's instructions for authors before posting.

### 3.3 Indexing time-lag
Read `${CLAUDE_SKILL_DIR}/references/launch_sequencing.md` when planning launch timing; it lists how long each index takes to pick a paper up.

### 3.4 Open-access choice
Prefer gold OA with CC-BY when budget allows; otherwise green OA via preprint plus author-accepted manuscript. Closed-access papers without a preprint lose roughly 30–50 % of AI-tool citations because Elicit, Consensus, and Perplexity Academic cannot extract from paywalled PDFs.

Funder OA-policy decision tree (Plan S, NIH, UKRI, Gates, Wellcome, NRF, MoHW): `references/oac_funding_checklist.yaml`.

### 3.5 Post-acceptance channel checklist
- Deposit AAM to PMC or Europe PMC.
- Update ORCID and Google Scholar profile.
- Post to Threads / X / BlueSky with DOI, one-sentence claim, and figure; long-form post on LinkedIn.
- Submit to Papers with Code if the paper reports a benchmark.
- Upload model or dataset to Hugging Face with a model/dataset card.

Section 12 gives the day-by-day order.

## Section 4 — Review-Paper Strategy

Read `${CLAUDE_SKILL_DIR}/references/pre_draft_strategy.md` when the artifact is a review or taxonomy paper, or the phase is `pre-draft`.

## Section 5 — GitHub, CITATION.cff, Zenodo, Hugging Face

Read `${CLAUDE_SKILL_DIR}/references/repository_and_cards.md` when the artifact is a README, `CITATION.cff`, Zenodo record, or Hugging Face model/dataset card (rules 5.1–5.6).

- Embed JSON-LD `ScholarlyArticle` / `SoftwareSourceCode` / `Dataset` / `Person` markup in repository pages and author landing pages — templates in `references/schema_markup_templates/`, validated with `python scripts/validate_schema.py path/to/file.jsonld`.
- Never auto-complete author lists, ORCIDs, or affiliations in `CITATION.cff` or Zenodo metadata; surface the empty slots to the user.

## Section 6 — Authority and E-E-A-T Signals

- Maintain an author landing page (GitHub Pages, personal domain, or institutional page) listing all papers with DOIs and open-access links.
- Use one consistent affiliation string across papers; inconsistency fragments the author entity in knowledge graphs.
- Keep ORCID complete and linked to Google Scholar. Re-run author disambiguation on Semantic Scholar every 6 months.
- Cross-link related papers by the same group in Discussion sections when defensible; within-group citation raises co-retrieval in RAG.
- Refresh repository and model cards quarterly; quarterly-updated articles outperform single-publish ones in AI-overview retention (Conductor 2026 benchmark).

## Section 7 — LLM-Citation Fabrication Defense

From Aperivue/medsci-skills, MIT.

More from Aperivue/medsci-skills