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 ShaishavMaisuria/research-paper-lifecycle-skills --skill polish-tables-figuresgit clone --depth 1 https://github.com/ShaishavMaisuria/research-paper-lifecycle-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/shaishavmaisuria/research-paper-lifecycle-skills/polish-tables-figures)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/polish-tables-figures"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/polish-tables-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/shaishavmaisuria/research-paper-lifecycle-skills/polish-tables-figures"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/polish-tables-figures.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 | $0.00191 | $0.01678 |
| Opus 5 | $0.00096 | $0.00839 |
| Sonnet 5 | $0.00038 | $0.00336 |
| Haiku 4.5 | $0.00019 | $0.00168 |
Grade A, and why
polish-tables-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 12d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Polish Tables & Figures
Turn draft-quality LaTeX floats into the tables and figures reviewers expect
to see at ACM / IEEE / NeurIPS-style / LNCS venues: booktabs rules, columns
that fit the page budget, clean subfigure layouts, venue-correct captions,
consistent \cref references, and colorblind-safe colors. Every change is
presentation-only; the numbers are sacred.
When to use
- "Make my tables/figures look professional" / "polish for camera-ready"
- "Convert this table to booktabs" / "my table is too wide" / "overfull hbox"
- "Lay these plots out as subfigures" / "fix my captions"
- "Are my figure colors colorblind-safe?" / "pick a palette for this plot"
- "Fix my \ref/\cref mess"
- After
tailor-to-venue, beforepreflight-check.
Inputs
- The main
.texfile (the one with\documentclass);\input/\includefiles are followed automatically. - Optional: a venue profile
venues/conferences/<venue>-<year>.yml(schema invenues/schema.yml) — sets column count and caption conventions. Without one, conventions are inferred from the documentclass. - Strongly recommended: a compiled
.lognext to the.texso the column-sizing (overfull box) check can run.
Process
-
Resolve venue conventions. If a venue is named, load its profile (sanity-check with
python3 scripts/venueyaml.py venues/conferences/<venue>.yml). Re-verify format-critical facts against the livecfp_urlbefore relying on them: template/documentclass, column count, any stated figure/table rules (minimum font sizes, color/grayscale requirements, accessibility requirements). Profiles are a starting point, never ground truth. -
Lint everything first:
python3 scripts/check_floats.py paper.tex \ --venue venues/conferences/<venue>-<year>.ymlFlags:
--json,--strict(warnings also fail),--log <file>,--overfull-threshold <pt>,--no-inputs. Exit codes: 0 clean, 1 findings, 2 bad arguments. The linter covers: booktabs style (\hline,\cline, vertical rules, double rules, missing package),\resizebox/tiny-font scaling, caption position per family, missing\caption/\label/\centering/ACM\Description, label-before-caption, deprecatedsubfigurepackage,width=\textwidthin a one-column float at a two-column venue, raster graphics,[h]/[H]placement, undefined and unreferenced labels, mixedFigure~\refvs\crefstyles, "Figure \cref" double prefixes, cleveref load order, lowercase\crefat sentence start, and Overfull\hboxentries from the.log.
What ships with it
7 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.
- 12d ago First seen · 127 lines · 191 tokens per session scan A 1a459a81da10
polish-tables-figures is a skill published in the GitHub repository ShaishavMaisuria/research-paper-lifecycle-skills (42 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 191 tokens to every session and 1,678 once invoked, about $0.0010 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 2026-08-30.
Other skills, from other repositories
aaai-reproducibility
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across…
aaai-topic-selection
Use when deciding whether a project is a strong AAAI submission across its broad AI scope, should be reframed or routed to a dedicated track such as AI for Social Impact or AI Alignment, or should instead go to IJCAI, NeurIPS, ICML, ICLR, AISTATS, UAI, ACL, CVPR, KDD, CHI, ICRA, or another specialist venue.
acl-experiments
Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP…
acl-reproducibility
Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper…
aaai-experiments
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
acmmm-experiments
Use when designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia…