Source: https://github.com/aipoch/medical-research-skills

Scite Database Skill

This skill provides access to Scite.ai Smart Citations data. Given a paper DOI, it summarizes how the paper is cited by others—specifically whether citations are supporting, contrasting, or mentioning—to help you evaluate the strength and reception of scientific claims.

When to Use

• Use this skill when you need access scite.ai smart citations to classify how a paper is cited (supporting, contrasting, mentioning) and assess scientific claims; use it when you need to evaluate a paper’s reliability or its acceptance in the literature in a reproducible workflow.
• Use this skill when a evidence insight task needs a packaged method instead of ad-hoc freeform output.
• Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
• Use this skill when scripts/scite_client.py is the most direct path to complete the request.
• Use this skill when you need the scite-database package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Access Scite.ai Smart Citations to classify how a paper is cited (supporting, contrasting, mentioning) and assess scientific claims; use it when you need to evaluate a paper’s reliability or its acceptance in the literature.
• Packaged executable path(s): scripts/scite_client.py plus 3 additional script(s).
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Evidence Insight/scite-database"
python -m py_compile scripts/scite_client.py
python scripts/scite_client.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/scite_client.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/scite_client.py with additional helper scripts under scripts/.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

1. When to Use

Use this skill when you need to:

1. Assess claim reliability: determine whether a paper is mostly supported or frequently contradicted by later work.
2. Prioritize reading in a literature review: quickly gauge consensus and controversy around a key DOI.
3. Fact-check scientific statements: validate whether a claim is broadly supported in subsequent citations.
4. Compare competing papers: contrast citation sentiment profiles across multiple DOIs.
5. Screen sources for downstream use: decide whether a paper is suitable to cite in reports, reviews, or product decisions.

2. Key Features

• Citation classification counts: returns totals for supporting, contrasting, and mentioning citations for a given DOI.
• Summary output formats: supports human-readable text output and machine-readable JSON output.
• Basic venue/journal metadata (when available): returns limited publication/venue information if provided by the endpoint.

3. Dependencies

• Python: 3.9+
• requests: 2.x

4. Example Usage

Run from CLI (text output)

python scripts/scite_client.py "10.1038/nature12345"

Example output:

--- Scite Analysis for 10.1038/nature12345 ---
Total Citations: 45
Supporting:      12
Contrasting:     1
Mentioning:      32

Run from CLI (JSON output)

python scripts/scite_client.py "10.1038/nature12345" --format json

Example JSON (shape may vary by endpoint response):

{
  "doi": "10.1038/nature12345",
  "total_citations": 45,
  "supporting": 12,
  "contrasting": 1,
  "mentioning": 32
}

5. Implementation Details

• Primary entry point: scripts/scite_client.py
• Input: a single DOI string (e.g., 10.1038/nature12345)
• Core logic:
  • Calls a public Scite endpoint for the DOI.
  • Parses the response to extract citation classification counts:
    • supporting
    • contrasting
    • mentioning
  • Computes/prints total_citations as the sum of the above (or uses the API-provided total when available).
• Output modes:
  • Default: formatted text summary for quick inspection.
  • --format json: emits a JSON object suitable for pipelines and automated checks.
• Limitations / notes:
  • Uses public Scite API endpoints; availability and response fields may change.
  • Citation snippets (context text) may require authentication and are not configured by default; this skill focuses on aggregate counts rather than full citation contexts.