Weakness Scanner

SkillSecurity

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Weakness Scanner skill

What this skill tells your AI

The instructions your AI receives, as published by flonat/flonat-research in skills/weakness-scanner/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
133
Forks
24
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
weakness-scanner
Source
github.com/flonat/flonat-research