weakness-scanner

name	weakness-scanner
description	Identify recurring weak arguments, unsupported assumptions, and vulnerable inference patterns across a literature corpus. Use when stress-testing a body of work rather than reviewing one manuscript. For one paper's argument, use the appropriate paper-review workflow.
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-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:

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