name: weakness-scanner description: "Use when you need to identify the weakest arguments across a literature." allowed-tools: Read, Write, Edit, Glob, Grep, Bash(uv*), Bash(uv:), Task, WebSearch, WebFetch, Bash(paperpile) argument-hint: "[topic, .bib file, or paper directory]" skill-dependencies: [devils-advocate, method-audit]
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-advocateor thepaper-criticagent - Full peer review — use the
referee2-revieweragent - 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:
-
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
-
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
-
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)
-
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
-
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:
- Check if other papers in the corpus have already flagged it
- Search for papers that contradict the weak claim (use
scholarly scholarly-search) - Check if the weakness has been addressed in subsequent work by the same authors
Phase 4: Severity Ranking
Rank all weaknesses by severity:
| Severity | Criteria | |----------|---------| | Fatal | The core finding is likely wrong — the paper's contribution doesn't hold | | Serious | A major limitation that significantly qualifies the findings | | Moderate | A real limitation that the authors should have discussed | | Minor | A 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
| Skill | When to use instead/alongside |
|-------|-------------------------------|
| devils-advocate | To stress-test YOUR argument (this scans others') |
| method-audit | For systematic methodological comparison (less adversarial) |
| theory-mapper | To understand which theories underpin the weak arguments |
| replication-audit | To check which findings have actually been replicated |
Prompt Engineering
Data & AI
Prompt engineering best practices and templates to maximize AI outputs.
Data Visualization
Data & AI
Generates data visualizations and charts tailored to your data.
RAG Architecture Setup
Data & AI
Setup guide for RAG (Retrieval-Augmented Generation) architectures.