Automated summary
This skill scans a corpus of research papers to surface their weakest arguments — logical flaws, data limitations, causal identification problems, contradicted claims — then ranks those vulnerabilities by severity in a WEAKNESS-SCAN.md report.
Strengths
- A structured five-part weakness taxonomy (logic, data, identification, contradictions, rhetorical overreach) that makes the read systematic rather than impressionistic.
- Cross-paper validation within the corpus plus external literature search, so it avoids flagging weaknesses already addressed elsewhere.
- Severity ranking (Fatal / Serious / Moderate / Minor) that maps directly onto drafting a contribution section or a rebuttal.
- Output includes verbatim quotes and page numbers, making every finding checkable and citable.
Limitations
- Quality depends heavily on corpus access: you need roughly 10–20 readable PDFs on a focused topic for useful results.
- Severity judgement is heuristic — a "Fatal" rating should be confirmed by a domain expert before publication.
- Relies on external tooling (PDF splitting, scholarly search, companion agents) that must be installed and configured.
- Does not substitute for full peer review, as the skill itself states.
Use it before writing your contribution section, to map the weak points in prior work and locate the gap your paper fills.
Don't run it on your own paper or as a substitute for full peer review — use devils-advocate, paper-critic or referee2-reviewer instead.
Security analysis
SafeThe skill is a read-and-critique workflow for academic papers: it reads a corpus, analyses arguments, optionally searches the web for contradicting work, and writes a single markdown report (`WEAKNESS-SCAN.md`). There are no destructive commands, no credential or token handling, no obfuscated payloads, no piping of remote content into a shell, and no attempts to disable safety behaviour. Bash access is scoped to `uv` and `paperpile`, and WebSearch/WebFetch are used for ordinary literature lookups. The only notable point is that the `uv` wildcard technically permits arbitrary script execution, but nothing in the instructions steers it toward anything harmful.
- •Bash(uv*) / Bash(uv:*) is a wildcard grant over the uv toolchain, which includes `uv run` and `uv pip install` (arbitrary code execution / network package fetches). This is normal for a Python-based research skill but is broader than the other tool grants.
- •The workflow references helpers (`split-pdf`, `scholarly scholarly-search`, `paperpile`) that are not all listed in allowed-tools, so the actual capability surface may differ from the declaration.
Examples
Scan minimum-wage.bib (18 papers) and write WEAKNESS-SCAN.md: list the weakest claims ranked Fatal/Serious/Moderate/Minor, with verbatim quotes and page numbers.I'm writing the contribution section of a paper on remote-work productivity. Read the PDFs in ./papers/ and tell me which prior claims are logically flawed or suffer from identification problems, so I know where the gap is.Check whether any findings in this corpus on urban heat islands are contradicted by other papers in it, and note which papers' own robustness checks undermine their headline claims.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 |
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/weakness-scanner-mryiqgh5/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/weakness-scanner-mryiqgh5' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/weakness-scanner-mryiqgh5/download' --output '.skill-downloads/weakness-scanner-mryiqgh5/SKILL.md.part' && mv '.skill-downloads/weakness-scanner-mryiqgh5/SKILL.md.part' '.skill-downloads/weakness-scanner-mryiqgh5/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Tools declared by the author (check availability in your agent): Read, Write, Edit, Glob, Grep, Bash(uv*), Bash(uv:*), Task, WebSearch, WebFetch, Bash(paperpile*)
No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/weakness-scanner-mryiqgh5/SKILL.md' && test -s '.skill-downloads/weakness-scanner-mryiqgh5/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/weakness-scanner' && cp -R '.skill-downloads/weakness-scanner-mryiqgh5' '.claude/skills/weakness-scanner'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/weakness-scanner-mryiqgh5'openskills read 'weakness-scanner-mryiqgh5'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/weakness-scanner-mryiqgh5/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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