Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add flonat/flonat-research --skill weakness-scannergit clone --depth 1 https://github.com/flonat/flonat-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/flonat/flonat-research/weakness-scanner)<a href="https://agentmods.dev/skills/flonat/flonat-research/weakness-scanner"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/weakness-scanner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/flonat/flonat-research/weakness-scanner"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/weakness-scanner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.01327 |
| Opus 5 | $0.00024 | $0.00664 |
| Sonnet 5 | $0.00010 | $0.00265 |
| Haiku 4.5 | $0.00005 | $0.00133 |
Grade A, and why
weakness-scanner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 157 lines · 49 tokens per session scan A 7bcda66848cb
weakness-scanner is a skill published in the GitHub repository flonat/flonat-research (133 stars, last pushed 16d ago), licensed MIT. It adds 49 tokens to every session and 1,327 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
latex-compile
Compile a LaTeX document and fix every error plus aesthetic issue (overfull/underfull boxes, widows, alignment, fonts) for a clean PDF and log. Use this instead of running pdflatex/latexmk manually — it avoids the latexmk stale-log trap and silent grep failures on binary log output, and it reformats rather than…
nb-to-wolfbook
Convert Mathematica .nb or .m files to Wolfbook .wb format so they open and run in VS Code. Use when bringing existing .nb/.m files into Wolfbook, or to make an existing .wb bridge-safe.
sync-wb-nb
Propagate a change made in a Wolfbook .wb notebook into the paired .nb notebook so the two stay identical. Use immediately after every .wb edit.
wolfram-headless
Run heavy Wolfram Language (wolframscript) computations from Claude Code reliably, and diagnose the misleading "The product exited because of a license error". Use whenever invoking wolframscript on a non-trivial computation, when a wolframscript job dies with a "license error" despite a valid license, or when Wolfram…
cross-validate
Format a result, derivation, or numerical value for independent verification by a second model. Use when you want a cross-check on an important or contested result.
verify-citation
Confirm a paper actually exists (arXiv / Semantic Scholar / OpenAlex) before citing it. Use before writing any new citation.