paper-banana-orchestration

paper-banana-orchestration is a skill for Claude Code from jiutuhky/my-super-capsule. It costs 128 tokens per session (2,710 once invoked), scanned A, original, MIT.

A workflow for creating diagrams and statistical plots for research papers. It coordinates several agents, uses an image service for scientific diagrams, and generates plot code with matplotlib, a Python charting library.

In plain words
What is it for?
Use it to create research-method or system-architecture diagrams and statistical visualizations, with saved image files and matplotlib code.
Why use it?
It organizes the stages of planning, styling, generating, and reviewing an academic illustration. It also keeps descriptions, images, and code in separate files.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_PLUGIN_ROOT} variable. Also seen: mentions subagents; names the AskUserQuestion tool.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the paper-banana plugin — 1 skill, 1 command, 5 agents shipped together

Good fit Use it to create research-method or system-architecture diagrams and statistical visualizations, with saved image files and matplotlib code.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add jiutuhky/my-super-capsule
Claude Code
/plugin install paper-banana

Made for: Claude Code.

Or install paper-banana, the plugin that ships this one along with the rest of its 1 skill, 1 command, 5 agents.

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 paper-banana-orchestration

README.md
[![agentmods](https://agentmods.dev/badge/skills/jiutuhky/my-super-capsule/paper-banana-orchestration/github.svg)](https://agentmods.dev/skills/jiutuhky/my-super-capsule/paper-banana-orchestration)
Your own site
<a href="https://agentmods.dev/skills/jiutuhky/my-super-capsule/paper-banana-orchestration"><img src="https://agentmods.dev/badge/skills/jiutuhky/my-super-capsule/paper-banana-orchestration/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 paper-banana-orchestration

Your own site · 80×15
<a href="https://agentmods.dev/skills/jiutuhky/my-super-capsule/paper-banana-orchestration"><img src="https://agentmods.dev/badge/skills/jiutuhky/my-super-capsule/paper-banana-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 128 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,710 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 original No closer match found 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 $0.00128 $0.02710
Opus 5 $0.00064 $0.01355
Sonnet 5 $0.00026 $0.00542
Haiku 4.5 $0.00013 $0.00271

Measured 12d ago against content hash 7abcd774b00d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

paper-banana-orchestration 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.

paper-banana/skills/paper-banana-orchestration/SKILL.md · 237 lines

How it starts

The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PaperBanana Orchestration Skill

You are the orchestrator for the PaperBanana multi-agent pipeline. Your job is to coordinate 5 sub-agents to generate publication-quality academic illustrations.

Pipeline Overview

Retriever → Planner → Stylist → Visualizer → [Critic → Visualizer] ×N

File-Based Storage Convention

Long text content (descriptions, critic suggestions) is stored as separate files in subdirectories, NOT inline in pipeline_state.json. This keeps the state file lightweight and prevents issues with large JSON values.

Directory layout inside the output directory:

{output_dir}/
├── pipeline_state.json       # lightweight metadata + file path references
├── descriptions/             # all text descriptions and critic suggestions (.txt)
├── images/                   # all generated images (.jpg)
└── code/                     # matplotlib code for plot tasks (.py)

Convention: When a sub-agent writes a description, it writes the full text to a file (e.g., descriptions/desc0.txt) and stores only the relative file path in pipeline_state.json (e.g., "target_diagram_desc0": "descriptions/desc0.txt"). To read a description, construct the absolute path: {output_dir}/{relative_path}.

This convention applies to all description keys (target_*_desc*), critic suggestion keys (target_*_critic_suggestions*), image path keys (target_*_image_path), and code keys (target_*_code). All paths in pipeline_state.json are relative to output_dir.

Step 0: Parse User Input

Parse the user's input to determine:

  1. task_type: "diagram" or "plot"

    • If the user provides a method description, methodology section, or asks for architecture/pipeline/framework diagrams → "diagram"
    • If the user provides raw data (tabular, JSON) or asks for charts/plots/visualizations → "plot"
    • If --type diagram or --type plot is explicitly specified, use that
    • If ambiguous, ask the user via AskUserQuestion
  2. content: The main input content

    • For diagrams: methodology section text
    • For plots: raw data (tabular, JSON, or text)
    • If the user provides a file path, read the file content

Read the full file on GitHub · 237 lines

Files

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.

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. 12d ago First seen · 237 lines · 128 tokens per session scan A 7abcd774b00d

Subscribe to this mod's changes

paper-banana-orchestration is a skill published in the GitHub repository jiutuhky/my-super-capsule (11 stars, last pushed 6mo ago), licensed MIT. It adds 128 tokens to every session and 2,710 once invoked, about $0.0006 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens