# Jiutuhky My Super Capsule Paper Banana Orchestration > PaperBanana Orchestration Skill - Skill: `tomevault-io/jiutuhky-my-super-capsule-paper-banana-orchestration` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/jiutuhky-my-super-capsule-paper-banana-orchestration` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jiutuhky-my-super-capsule-paper-banana-orchestration/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/jiutuhky-my-super-capsule-paper-banana-orchestration --- # 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 3. **`visual_intent`**: The figure caption or visualization intent - For diagrams: the figure caption (e.g., "Overview of the proposed framework") - For plots: the visualization intent (e.g., "Bar chart comparing model performance") - If not explicitly provided, ask the user 4. **`aspect_ratio`**: Output aspect ratio - Default: "1:1" - If `--ratio` is specified, use that value - Valid options: "16:9", "1:1", "3:2", "21:9" 5. **`max_critic_rounds`**: Maximum critic iteration rounds - Default: 3 6. **`retrieval_setting`**: Reference retrieval mode - Default: "none" (most users won't have the PaperBananaBench dataset) - Set to "auto" only if the user explicitly requests reference-based generation and the dataset exists ## Step 1: Create Working Directory and Initialize Pipeline State Create the output directory with a timestamp suffix, along with subdirectories: ```bash OUTPUT_DIR="./paper_banana_output_$(date +%Y%m%d_%H%M%S)" mkdir -p "$OUTPUT_DIR/descriptions" "$OUTPUT_DIR/images" "$OUTPUT_DIR/code" ``` Resolve `OUTPUT_DIR` to an absolute path (e.g., via `realpath` or `pwd`), then write `pipeline_state.json` **inside the output directory**: ```json { "task_type": "diagram|plot", "content": "", "visual_intent": "
", "aspect_ratio": "1:1", "max_critic_rounds": 3, "current_critic_round": 0, "retrieval_setting": "none", "output_dir": "", "top10_references": [], "retrieved_examples": [] } ``` Note: `pipeline_state.json` is located at `{output_dir}/pipeline_state.json`. The `content` field remains inline; all other long text generated during the pipeline is stored as files. ## Step 2: Dispatch Retriever Agent Use the Task tool to dispatch the retriever sub-agent: ``` Task( subagent_type="paper-banana:retriever", prompt="Read pipeline_state.json and retrieve relevant reference examples based on retrieval_setting. Update pipeline_state.json with top10_references and retrieved_examples. The pipeline_state.json is located at: {output_dir}/pipeline_state.json" ) ``` After the retriever completes, read `pipeline_state.json` to verify `top10_references` was updated. **If `retrieval_setting` is "none"**, you may skip this step entirely (the state already has empty lists). ## Step 3: Dispatch Planner Agent Use the Task tool to dispatch the planner sub-agent: ``` Task( subagent_type="paper-banana:planner", prompt="Read pipeline_state.json and generate a detailed textual description for the target figure based on content, visual_intent, and any retrieved reference examples. Write the description to {output_dir}/descriptions/desc0.txt, then set target_{task_type}_desc0 to 'descriptions/desc0.txt' in pipeline_state.json. The pipeline_state.json is located at: {output_dir}/pipeline_state.json" ) ``` After the planner completes, read `pipeline_state.json` to verify `target_{task_type}_desc0` was set. ## Step 4: Dispatch Stylist Agent Use the Task tool to dispatch the stylist sub-agent: ``` Task( subagent_type="paper-banana:stylist", prompt="Read pipeline_state.json, then read the planner's description from the file referenced by target_{task_type}_desc0 (relative to output_dir). Refine the description with NeurIPS 2025 aesthetic details. Read the appropriate style guide from ${CLAUDE_PLUGIN_ROOT}/skills/paper-banana-orchestration/references/. Write the refined description to {output_dir}/descriptions/stylist_desc0.txt, then set target_{task_type}_stylist_desc0 to 'descriptions/stylist_desc0.txt' in pipeline_state.json. The pipeline_state.json is located at: {output_dir}/pipeline_state.json" ) ``` After the stylist completes, read `pipeline_state.json` to verify `target_{task_type}_stylist_desc0` was set. ## Step 5: Dispatch Visualizer Agent Use the Task tool to dispatch the visualizer sub-agent: ``` Task( subagent_type="paper-banana:visualizer", prompt="Read pipeline_state.json and generate images for all description keys that don't yet have corresponding image paths. Read descriptions from files (paths in pipeline_state.json are relative to output_dir). Save images to {output_dir}/images/. For diagram tasks, use ${CLAUDE_PLUGIN_ROOT}/scripts/generate_diagram.py. For plot tasks, generate matplotlib code, save to {output_dir}/code/, and use ${CLAUDE_PLUGIN_ROOT}/scripts/execute_plot.py. Update pipeline_state.json with relative image paths. The pipeline_state.json is located at: {output_dir}/pipeline_state.json" ) ``` After the visualizer completes, read `pipeline_state.json` to verify image paths were written. ## Step 6: Critic Loop This is the iterative refinement loop. ### Initialization ``` current_best_image_key = "target_{task_type}_stylist_desc0_image_path" ``` ### Loop (up to max_critic_rounds iterations) ``` for round_idx in range(max_critic_rounds): # 6a. Update current_critic_round in pipeline_state.json Update pipeline_state.json: set "current_critic_round" to round_idx # 6b. Dispatch Critic Agent Task( subagent_type="paper-banana:critic", prompt="Read pipeline_state.json (current_critic_round is {round_idx}). Critique the generated image and its description. For round 0, use the stylist output; for round N>0, use critic_desc{N-1}. Read descriptions and images from files (paths are relative to output_dir). Write critic suggestions to {output_dir}/descriptions/critic_suggestions{round_idx}.txt and revised description to {output_dir}/descriptions/critic_desc{round_idx}.txt. Update pipeline_state.json with the relative file paths. The pipeline_state.json is located at: {output_dir}/pipeline_state.json" ) # 6c. Read pipeline_state.json to check critic output Read pipeline_state.json # 6d. Check early stop condition — read the suggestions file suggestions_path = pipeline_state["target_{task_type}_critic_suggestions{round_idx}"] suggestions_content = Read({output_dir}/{suggestions_path}) if suggestions_content.strip() == "No changes needed.": break # Early stop - figure is satisfactory # 6e. Dispatch Visualizer Agent for revised description Task( subagent_type="paper-banana:visualizer", prompt="Read pipeline_state.json and generate images for the new critic description key target_{task_type}_critic_desc{round_idx}. Read the description from its file. Save image to {output_dir}/images/. Update pipeline_state.json with relative image path. The pipeline_state.json is located at: {output_dir}/pipeline_state.json" ) # 6f. Read pipeline_state.json to check visualization result Read pipeline_state.json # 6g. Check if new image was generated successfully new_image_key = "target_{task_type}_critic_desc{round_idx}_image_path" if new_image_key exists and is valid in pipeline_state: current_best_image_key = new_image_key else: # Visualization failed, rollback to previous best image break ``` ### After Loop The final best image is at the path `{output_dir}/{relative_path}` where the relative path is stored in `current_best_image_key` in `pipeline_state.json`. ## Step 7: Present Final Result 1. Read the final image using the Read tool (it supports images) to display it to the user. Construct the absolute path: `{output_dir}/{pipeline_state[current_best_image_key]}` 2. Report the pipeline summary: - Task type (diagram/plot) - Number of critic rounds completed - Whether early stop was triggered - Final image file path (absolute) - Output directory path Example output: ``` Pipeline complete! - Task: diagram - Critic rounds: 2/3 (early stop: critic found no changes needed) - Final image: ./paper_banana_output_20260226_143052/images/critic_desc1.jpg - Output directory: ./paper_banana_output_20260226_143052/ ``` ## Important Notes - **Always use absolute paths** when dispatching sub-agents, as they run in independent contexts. - **Always read pipeline_state.json** after each sub-agent completes to verify the expected output was written. - **pipeline_state.json is inside output_dir** — located at `{output_dir}/pipeline_state.json`. - **File-based storage** — descriptions, suggestions, images, and code are stored as separate files. Only relative file paths are stored in `pipeline_state.json`. To read content, join `output_dir` + relative path. - **For plot tasks**, the visualizer agent generates matplotlib code. If code execution fails, the critic will detect the failure and provide a revised description. - **${CLAUDE_PLUGIN_ROOT}** refers to the root directory of this plugin (i.e., the directory containing `.claude-plugin/`). --- > Source: [jiutuhky/my-super-capsule](https://github.com/jiutuhky/my-super-capsule) — distributed by [TomeVault](https://tomevault.io).