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 causify-ai/helpers --skill paper.fix_figuresgit clone --depth 1 https://github.com/causify-ai/helpersWrote 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/causify-ai/helpers/paper.fix_figures)<a href="https://agentmods.dev/skills/causify-ai/helpers/paper.fix_figures"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/paper.fix_figures/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/causify-ai/helpers/paper.fix_figures"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/paper.fix_figures.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00017 | $0.00621 |
| Opus 5.5 | $0.00007 | $0.00248 |
| Sonnet 5.5 | $0.00003 | $0.00124 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
paper.fix_figures 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 1mo 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Check References
- Make sure every picture and table has a label and caption and it's referenced in the text
- Captions should only contain a reference to what is done and comments and more content should be in the text
Add Captions and Labels
-
For all the figures in the latex files in the passed directory convert the figure adding the caption and label to the commented section.
-
For instance convert
% ``` % rendered_images:end % render_images:begin \begin{figure}[!ht] \centering \includegraphics[width=\linewidth]{figs/02_architecture.1.png} \caption{Confounding adjustment in causal inference} \label{fig:confounding_adjustment} \end{figure} % render_images:endto
% ``` % caption=Confounding adjustment in causal inference % label=fig:confounding_adjustment % rendered_images:end % render_images:begin \begin{figure}[!ht] \centering \includegraphics[width=\linewidth]{figs/02_architecture.1.png} \caption{Confounding adjustment in causal inference} \label{fig:confounding_adjustment} \end{figure} % render_images:end
Create Labels and Captions From Scratch
- If a figure has no caption or label, infer them from the filename and
surrounding context
- E.g.,
figs/02_architecture.1.pngbecomeslabel=fig:architecture,caption=System architecture overview
- E.g.,
- Choose label names that are short, lowercase, and use underscores
Add References in the Text
- If a figure has a label but is not cited anywhere in the text, add a
parenthetical reference near the most relevant paragraph:
(see Fig.~\ref{fig:confounding_adjustment})
Verify No Orphaned References
- Scan the text for
\ref{fig:xxx}or\ref{tab:xxx}that have no matching\label{fig:xxx}or\label{tab:xxx}in any figure or table environment - Report orphaned references as warnings; do not silently delete them
Verification
- Every figure and table has a
\labeland a\caption - Every figure and table
\labelis referenced by a\refin the text - No orphaned
\ref{fig:xxx}or\ref{tab:xxx}exists without a matching\labelin any figure or table environment
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.
- 1mo ago Changed · +7 lines · -7 tokens per session 8fd78fda20e6
- 1mo ago First seen · 70 lines · 24 tokens per session scan A 05e4f3107fc7
paper.fix_figures is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 17 tokens to every session and 621 once invoked, about $0.0001 per session on Opus 5.5. 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.
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