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 shubham0704/claude-skills --skill refining-ml-papersgit clone --depth 1 https://github.com/shubham0704/claude-skillsWrote 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/shubham0704/claude-skills/refining-ml-papers)<a href="https://agentmods.dev/skills/shubham0704/claude-skills/refining-ml-papers"><img src="https://agentmods.dev/badge/skills/shubham0704/claude-skills/refining-ml-papers/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/shubham0704/claude-skills/refining-ml-papers"><img src="https://agentmods.dev/badge/skills/shubham0704/claude-skills/refining-ml-papers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00075 | $0.03268 |
| Opus 5 | $0.00037 | $0.01634 |
| Sonnet 5 | $0.00015 | $0.00654 |
| Haiku 4.5 | $0.00007 | $0.00327 |
Grade A, and why
refining-ml-papers 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 11d 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 — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refining ML Papers: From Feedback to Camera-Ready
This skill captures battle-tested patterns for revising scientific LaTeX papers in response to reviewer/advisor feedback. Built from extensive revision work on multi-file ICML-style papers with Overleaf git workflows.
When to Use This Skill
Invoke when the user:
- Has reviewer or advisor feedback to address
- Wants to restructure paper sections (move content, merge sections)
- Needs to explain tables or figures with concrete examples
- Asks to improve exposition clarity or reduce redundancy
- Wants to fix LaTeX compilation issues after restructuring
- Needs to prepare a camera-ready or arXiv version
Core Methodology
Phase 1: Understand the Paper Architecture
Before making ANY changes:
- Map the file structure:
Globfor**/*.texto find all LaTeX files - Identify the main file and all
\input{}dependencies - Read the target sections completely before editing
- Check for shared macros: Look for
\providecommand/\newcommandpatterns that indicate cross-file dependencies - Note existing labels:
Grepfor\label{and\ref{to understand cross-reference graph
# Typical modular structure:
main_arxiv.tex # Preamble + abstract + introduction + \input{} calls
methods.tex # Section 3
experiments.tex # Section 4
appendix.tex # Appendix
appendix_casimir.tex # Specialized appendix
references.bib # Bibliography
Phase 2: Plan Changes with Feedback Mapping
Map each piece of feedback to a concrete file + line range + action:
| Feedback | Action | File(s) | Risk |
|---|---|---|---|
| "Problem statement too late" | Move to Sec 1 + slim Sec 3 | main.tex + methods.tex | Cross-ref breakage |
| "Table entries unclear" | Add concrete instantiations paragraph | main.tex | None |
| "Sections redundant" | Merge + back-reference | methods.tex | Label conflicts |
| "Abstract doesn't state problem" | Restructure abstract | main.tex | None |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 298 lines · 75 tokens per session scan A 7316eda8f9b2
refining-ml-papers is a skill published in the GitHub repository shubham0704/claude-skills (1 stars, last pushed 7d ago), licensed MIT. It adds 75 tokens to every session and 3,268 once invoked, about $0.0004 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 2026-08-31.
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