WE

weakness-scanner

Scans academic papers to find logical flaws, unsupported claims, and research gaps in existing literature.

Install

mkdir -p .claude/skills/weakness-scanner && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/19422" && unzip -o skill.zip -d .claude/skills/weakness-scanner && rm skill.zip

Installs to .claude/skills/weakness-scanner

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use when you need to identify the weakest arguments across a literature.
72 chars✓ has a “when” trigger
Advanced

Key capabilities

  • Identify logical flaws in academic literature
  • Detect data limitations and unsupported claims
  • Find contradictions between research papers
  • Rank weaknesses by severity
  • Generate a structured weakness scan report

How it works

The tool scans a corpus of papers to extract logical flaws, data limitations, and contradictions, then ranks these weaknesses by severity to identify research gaps.

Inputs & outputs

You give it
Academic literature corpus (PDFs, .bib, or paper list)
You get back
WEAKNESS-SCAN.md report detailing identified flaws and research opportunities

When to use weakness-scanner

  • Find research gaps in literature
  • Prepare for a rebuttal or response to existing claims
  • Evaluate the strength of arguments in prior work
  • Identify flawed methodology in papers

About this skill

Weakness Scanner

Identify the weakest arguments made across a body of literature. Find logical flaws, data limitations, unsupported claims, and findings contradicted by other work. Your contribution section writes itself after this.

Unlike devils-advocate (which stress-tests YOUR argument), this skill scans OTHER people's work for vulnerabilities. It's how you find the gap your paper fills.

When to Use

  • Before writing your contribution section — need to know what's broken in prior work
  • Identifying research opportunities — weak arguments = space for new work
  • Preparing a rebuttal or response — need to show where existing claims fall short
  • Deciding which papers to build on vs. which to challenge

When NOT to Use

  • Your own paper — use devils-advocate or the paper-critic agent
  • Full peer review — use the referee2-reviewer agent
  • Methodological comparison — use method-audit (overlaps, but different focus)

Input

Same corpus inputs: .bib file, PDF directory, topic, or paper list. Works best with 10-20 papers on a focused topic.

Workflow

Phase 1: Corpus Assembly

Same as other corpus skills. Prioritise empirical papers making causal or strong claims — these are most likely to have exploitable weaknesses.

Phase 2: Weakness Extraction

For each paper (read via split-pdf), look for:

  1. Logical flaws

    • Non sequiturs — conclusions that don't follow from the evidence
    • Circular reasoning — assuming what they're trying to prove
    • False dichotomies — presenting only two options when more exist
    • Hasty generalisation — drawing broad conclusions from narrow evidence
  2. Data limitations

    • Small samples without power analysis
    • Non-representative populations with claims of generalisability
    • Measurement issues (self-report bias, proxy variables)
    • Missing data handled without sensitivity analysis
  3. Identification problems

    • Causal claims from observational data without credible identification
    • Omitted variable bias acknowledged but not addressed
    • Reverse causality not ruled out
    • Weak instruments (if IV)
  4. Contradicted claims

    • Findings that conflict with other papers in the corpus
    • Claims undermined by the authors' own robustness checks
    • Results that don't survive alternative specifications
  5. Rhetorical overreach

    • Abstract claims stronger than the evidence supports
    • Policy recommendations not grounded in the findings
    • "First to study X" claims that ignore prior work

Phase 3: Cross-Paper Validation

For each weakness identified:

  1. Check if other papers in the corpus have already flagged it
  2. Search for papers that contradict the weak claim (use scholarly scholarly-search)
  3. Check if the weakness has been addressed in subsequent work by the same authors

Phase 4: Severity Ranking

Rank all weaknesses by severity:

SeverityCriteria
FatalThe core finding is likely wrong — the paper's contribution doesn't hold
SeriousA major limitation that significantly qualifies the findings
ModerateA real limitation that the authors should have discussed
MinorA weakness that doesn't undermine the main claims

Phase 5: Output

Write to WEAKNESS-SCAN.md in the project directory.

Output Format

# Weakness Scan: [Topic]

**Date:** YYYY-MM-DD
**Corpus:** [N] papers
**Weaknesses identified:** [N] (Fatal: X, Serious: Y, Moderate: Z, Minor: W)

## Top 5 Weaknesses

### 1. [Paper — Author (Year)]

**Claim:**
> "[Verbatim quote of the weak claim]" (p. XX)

**Flaw:** [Type: logical / data / identification / contradiction / rhetorical]

**Why it's weak:** [Specific explanation of the logical flaw or data limitation]

**Already contradicted by:**
- [Paper A (Year)] — [How it contradicts]
- [Paper B (Year)] — [How it contradicts]

**What evidence WOULD make it strong:** [What the authors would need to show]

**Severity:** [Fatal / Serious / Moderate / Minor]

**Opportunity for your research:** [How this weakness creates space for new work]

### 2. [Paper — Author (Year)]
...

## Field-Level Vulnerabilities

Patterns that recur across multiple papers:

1. **[Vulnerability]** — seen in [N] papers
   - Papers affected: [list]
   - Why nobody has addressed it: [likely explanation]
   - How to exploit it: [what a new paper could do]

2. **[Vulnerability]**
...

## Contradiction Map

| Claim | Paper A says | Paper B says | Who has better evidence? |
|-------|-------------|-------------|------------------------|

## Implications for Your Research

- **Strongest opportunity:** [The biggest gap this scan reveals]
- **Contribution framing:** "[Your paper] addresses the [specific weakness] in [prior work] by [your approach]"
- **Caution:** [Any weakness that also applies to your planned approach]

Cross-References

SkillWhen to use instead/alongside
devils-advocateTo stress-test YOUR argument (this scans others')
method-auditFor systematic methodological comparison (less adversarial)
theory-mapperTo understand which theories underpin the weak arguments
replication-auditTo check which findings have actually been replicated

When not to use it

  • Analyzing your own paper
  • Performing full peer review
  • Methodological comparison

Prerequisites

Corpus of papers (bib file, PDF directory, or paper list)

Limitations

  • Works best with 10-20 papers on a focused topic

How it compares

This tool specifically scans external literature for vulnerabilities to find research gaps, whereas tools like devils-advocate focus on stress-testing your own arguments.

Compared to similar skills

weakness-scanner side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
weakness-scanner (this skill)012dReviewAdvanced
literature-review5591moReviewAdvanced
youtube-wisdom1016dReviewIntermediate
read-arxiv-paper164moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

literature-review

K-Dense-AI

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

5591,298

youtube-wisdom

sammcj

Extract wisdom, insights, and actionable takeaways from YouTube videos. Use when asked to analyse, summarise, or extract key learnings from YouTube content. Downloads transcripts only (no video files), performs comprehensive analysis including key insights, notable quotes, structured summaries, and actionable takeaways, then saves the analysis to a markdown file.

1092

read-arxiv-paper

karpathy

Use this skill when when asked to read an arxiv paper given an arxiv URL

1656

knowledge-synthesis

anthropics

Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.

556

youtube-summarizer

sickn33

Extract transcripts from YouTube videos and generate comprehensive, detailed summaries using intelligent analysis frameworks

531

arxiv-to-md

solatis

Convert arXiv papers to LLM-consumable markdown. Invoke when user provides an arXiv ID or URL, or when syncing academic papers from a PDF folder to a markdown destination.

412

Search skills

Search the agent skills registry