plotting-agent

plotting-agent is a skill for Claude Code, Codex from raja21068/AutoResearch. It costs 102 tokens per session (1,961 once invoked), scanned A, a copy of plotting-agent, MIT.

A research-paper workflow step that creates charts and explanatory diagrams from an experiment log and research idea. It can also refine the rendered figures using visual review and write captions for them.

In plain words
What is it for?
Use it to render plots, conceptual diagrams, captions, and optionally improved versions of existing figures for a LaTeX research manuscript.
Why use it?
It turns raw experiment information into figures that can be placed in a paper, while checking whether the rendered visuals communicate clearly.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to render plots, conceptual diagrams, captions, and optionally improved versions of existing figures for a LaTeX research manuscript.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/raja21068/autoresearch/plotting-agent
Install

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.

Any agent
npx skills add raja21068/AutoResearch --skill plotting-agent
Clone the repo
git clone --depth 1 https://github.com/raja21068/AutoResearch

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for plotting-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/raja21068/autoresearch/plotting-agent/github.svg)](https://agentmods.dev/skills/raja21068/autoresearch/plotting-agent)
Your own site
<a href="https://agentmods.dev/skills/raja21068/autoresearch/plotting-agent"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/plotting-agent/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.

agentmods 80×15 button for plotting-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/raja21068/autoresearch/plotting-agent"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/plotting-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,961 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00102 $0.01961
Opus 5 $0.00051 $0.00981
Sonnet 5 $0.00020 $0.00392
Haiku 4.5 $0.00010 $0.00196

Measured 6d ago against content hash 57bd1e7e18ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

plotting-agent 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 6d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/paperbanana_render.py, scripts/render_diagram.py, scripts/render_matplotlib.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

100% identical to plotting-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/paper-pipeline/plotting-agent/SKILL.md · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plotting Agent (Step 2)

Faithful implementation of the Plotting Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 2 and App. F.1 p.45).

Cost: ~20–30 LLM calls. The paper uses PaperBanana (Zhu et al., 2026) as the default backbone with a closed-loop VLM-critique refinement. This skill expresses that loop in host-agent terms: you (the host agent) generate matplotlib code with your own LLM, render via your Bash/Python tool, optionally critique the rendered PNG with your vision model, redraw, and finally caption.

Inputs

  • workspace/outline.json — specifically the plotting_plan array
  • workspace/inputs/idea.md and workspace/inputs/experimental_log.md — the source data
  • workspace/inputs/figures/ — optional pre-existing figures (PlotOn mode)

Outputs

  • workspace/figures/<figure_id>.png — one PNG per plotting_plan entry (300 DPI, sized to the requested aspect ratio)
  • workspace/figures/captions.json{figure_id: caption_text} map

Workflow

Per figure (executed independently per figure_id)

  1. Read the figure spec from outline.json:

    {
      "figure_id": "fig_main_results",
      "title": "Main Results on Dataset X",
      "plot_type": "plot",
      "data_source": "experimental_log.md",
      "objective": "Visual summary (Grouped Bar Chart) demonstrating ...",
      "aspect_ratio": "5:4"
    }
    
  2. Few-shot retrieval (visual planning): pick the matching pattern from references/chart-patterns.md (for plot_type=="plot") or references/diagram-patterns.md (for plot_type=="diagram").

  3. Extract data: parse idea.md and/or experimental_log.md (data_source field tells you which) to obtain the numeric values or conceptual entities the figure needs. For experimental_log.md, the ## 2. Raw Numeric Data section contains markdown tables.

  4. Render:

    If PAPERBANANA_PATH is set — use the PaperBanana backbone (Zhu et al., 2026). It runs a Retriever → Planner → Stylist → Visualizer → Critic loop and is especially good for plot_type == "diagram". See references/paperbanana-cookbook.md for setup (needs a Gemini API key).

Read the full file on GitHub · 168 lines

Changes

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.

  1. 6d ago First seen · 168 lines · 102 tokens per session scan A 57bd1e7e18ab

Subscribe to this mod's changes

plotting-agent is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 102 tokens to every session and 1,961 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to plotting-agent, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

plotting-agent

Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…

Ar9av/PaperOrchestra · 102 tokens

plotting-agent

Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…

Aukexecutivedepartment5152/PaperOrchestra · 102 tokens

content-refinement-agent

Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…

Ar9av/PaperOrchestra · 145 tokens

section-writing-agent

Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…

Ar9av/PaperOrchestra · 125 tokens

outline-agent

Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…

Ar9av/PaperOrchestra · 99 tokens

paper-autoraters

Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…

Ar9av/PaperOrchestra · 121 tokens