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.
/plugin marketplace add jiutuhky/my-super-capsule/plugin install paper-bananaWrote 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/jiutuhky/my-super-capsule/paper-banana-orchestration)<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.
<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>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 | $0.00128 | $0.02710 |
| Opus 5.5 | $0.00051 | $0.01084 |
| Sonnet 5.5 | $0.00026 | $0.00542 |
| Haiku 4.5 | $0.00013 | $0.00271 |
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 2mo 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 — 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:
-
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 diagramor--type plotis explicitly specified, use that - If ambiguous, ask the user via AskUserQuestion
-
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
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.
- 2mo ago First seen · 237 lines · 128 tokens per session scan A 7abcd774b00d
paper-banana-orchestration is a skill published in the GitHub repository jiutuhky/my-super-capsule (11 stars, last pushed 8mo ago), licensed MIT. It adds 128 tokens to every session and 2,710 once invoked, about $0.0005 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 2026-08-30.
paper-banana-orchestration compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| paper-banana-orchestrationjiutuhky/my-super-capsule | Skill | 11 | 8mo ago | MIT | A | 128 | Claude Code |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |