Sci Paper Reviewer

When to Use

• When a user uploads a manuscript (PDF/DOC/DOCX/TXT) and asks for an SCI-style peer review.
• When a user wants an innovation/novelty score (1–12) with explicit criteria and justification.
• When a user needs a logic audit of the Results section (false positives, missing controls, broken mechanism chains).
• When a user requests actionable experimental revisions (what to add/verify, which controls are missing).
• When a user provides copy-pasted manuscript text and wants the same structured review output.

Key Features

• Automatic manuscript parsing for PDF, Word, and TXT, plus direct text input.
• Section-oriented analysis: focuses on Abstract, Results, Introduction, and Discussion.
• Research type classification: Materials, Basic Medical, Clinical, or Review.
• Innovation evaluation with a strict 1–12 scoring rubric (originality, theory extension, translational path).
• Logic vulnerability screening in Results:
  • false-positive risk (lack of orthogonal validation)
  • mechanism breaks (unverified upstream/downstream links)
  • control failures (missing double-negative controls)
• Structured review report with numbered, concrete modification suggestions (no generic filler).

Dependencies

• Python >=3.9
• Document parsing libraries (optional but supported):
  • pypdf (version varies)
  • pdfplumber (version varies)
  • PyMuPDF (version varies)
  • PyPDF2 (version varies)
  • python-docx (version varies)

The parser should fall back to basic extraction if some advanced libraries are unavailable.

Example Usage

1) Parse a file and review the extracted text

# Parse an uploaded manuscript into a text file (recommended to avoid console buffer limits)
python scripts/enhanced_document_parser.py /path/to/manuscript.pdf extracted_content.txt

Then provide extracted_content.txt to the skill (or paste its content) and request a review, for example:

Please review this manuscript as a strict SCI reviewer.
Requirements:
1) Classify research type.
2) Evaluate innovation (score 1–12) using your rubric.
3) Screen Results for logic vulnerabilities (false positives, mechanism breaks, control failures).
4) Output a structured report with numbered experimental modification suggestions.
[PASTE CONTENT OF extracted_content.txt HERE]

2) Direct text input (no file)

I will paste the manuscript text below. Please perform an SCI-style review:
- Extract Abstract/Results/Introduction/Discussion (as available)
- Classify research type
- Innovation score (1–12) and rationale
- Logic vulnerability screening
- Provide numbered modification suggestions only (no generic “other suggestions”)
[PASTE MANUSCRIPT TEXT]

Implementation Details

1) Document Processing Rules

• Input detection:
  • If a file is provided, detect type: PDF, DOCX, DOC, or TXT.
  • If text is pasted, process it directly.
• Parsing script:
  • Use: scripts/enhanced_document_parser.py
  • Recommended invocation (write to file):
    • python scripts/enhanced_document_parser.py <file_path> extracted_content.txt
  • Then read extracted_content.txt as the canonical extracted content.
• Failure handling:
  • If the parser outputs Warning: No text extracted, treat the file as likely scanned/image-based and inform the user that OCR may be required before review.

2) Section Extraction (for analysis)

From the parsed content, extract (as available):

• Abstract (work summary)
• Results (core experimental findings and data claims)
• Introduction & Discussion (background, positioning, interpretation)

If headings are missing, infer sections by typical academic structure and transitions.

3) Research Type Classification

Classify into one of:

• Materials Research
• Basic Medical Research
• Clinical Research
• Review

Use cues such as study subjects (cells/animals/patients), endpoints, materials synthesis/characterization, and whether the manuscript is primarily summarizing prior work.

4) Innovation Evaluation (Score 1–12)

Evaluate primarily from Introduction and Discussion (and claims in Abstract), using the following rubric:

• Major Original (9–12): Proposes a fundamentally new mechanism or a disruptive hypothesis.
• Clear Translation Path (8–11): Identifies targetable markers and provides inhibitor screening/validation data.
• Theory Extension (5–8): Extends the boundary or applicability of an existing theory/framework.
• Potential Application Value (4–7): Reveals regulatory mechanisms but lacks actionable intervention/translation.
• Validation Study (1–4): Primarily replicates/validates known theories or fills incremental details.
• Heuristic note: “miRNA-based novelty” is generally treated as average unless supported by strong mechanistic and translational evidence.

5) Logic Vulnerability Screening (Results-Focused)

Screen the Results for the following vulnerabilities:

1. False Positive Risk

   • Claims rely on a single assay/marker without orthogonal validation (e.g., only qPCR without protein-level confirmation; only one antibody without specificity checks).

2. Mechanism Break

   • Upstream/downstream relationships are asserted but not experimentally verified (e.g., correlation presented as causation; missing rescue/epistasis tests).

3. Control Failure

   • Key experiments lack appropriate controls, especially double-negative controls where required (e.g., vehicle + non-targeting controls; isotype controls; sham operations; matched baseline).

4. Basic Medicine Rule (method sufficiency)

   • For cell-level knockdown, siRNA/shRNA is sufficient; CRISPR is not mandatory unless the claim requires stable knockout or allele-specific inference.

6) Required Output Structure (Final Review Report)

The generated review must follow this structure:

1. Document Information

   • File type and processing status
   • Extracted sections overview (what was found/used)
   • Parser used (enhanced parser vs. fallback)

2. Innovation Evaluation

   • Provide the innovation level and rationale (score may be stated explicitly or implied, but must map to the rubric).
   • Use academic, precise language.

3. Experimental Modification Suggestions

   • Provide only concrete, logic-driven revisions derived from the vulnerability screening.
   • Number items as 2.1, 2.2, 2.3, ...
   • Avoid generic “Other suggestions”; each item must specify what experiment/control/verification to add and what claim it would support or falsify.