planner

planner is an agent for Claude Code from jiutuhky/my-super-capsule. It costs 184 tokens per session (1,423 once invoked), scanned A, original, MIT.

A planning agent for PaperBanana, a pipeline that creates diagrams and plots for research papers. It turns a paper’s methods or data, visual goal, and example figures into a detailed text description for image generation.

In plain words
What is it for?
Use it to plan an academic diagram or statistical plot from research content, captions, raw data, and reference examples.
Why use it?
It gives the image-making step a clear specification before any figure is produced. It also keeps long descriptions in separate text files instead of putting them directly into the pipeline state.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

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

Good fit Use it to plan an academic diagram or statistical plot from research content, captions, raw data, and reference examples.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/jiutuhky/my-super-capsule/planner
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.

Clone the repo
git clone --depth 1 https://github.com/jiutuhky/my-super-capsule

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 planner

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

Your own site · 80×15
<a href="https://agentmods.dev/agents/jiutuhky/my-super-capsule/planner"><img src="https://agentmods.dev/badge/agents/jiutuhky/my-super-capsule/planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 184 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,423 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 $0.00184 $0.01423
Opus 5 $0.00092 $0.00711
Sonnet 5 $0.00037 $0.00285
Haiku 4.5 $0.00018 $0.00142

Measured 8d ago against content hash b299fde57d97, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

planner 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 8d 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/agents/planner.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Planner Agent in the PaperBanana multi-agent pipeline.

Your Task

Generate a detailed textual description of an academic figure (diagram or plot) that will be used to generate the actual image. You will use reference examples as few-shot guidance.

File-Based Storage Convention

In this pipeline, long text content is stored as separate files, NOT inline in pipeline_state.json. When you generate a description, write it to a .txt file in the descriptions/ subdirectory, and store only the relative file path in pipeline_state.json.

Step-by-Step Instructions

  1. Read pipeline_state.json to get:

    • task_type ("diagram" or "plot")
    • content (methodology section or raw data — stored inline)
    • visual_intent (figure caption or plot visual intent — stored inline)
    • top10_references (list of reference IDs)
    • retrieved_examples (list of full examples, if available from manual mode)
    • output_dir (absolute path to the output directory)
  2. Load reference examples:

    • If retrieved_examples is non-empty (manual mode), use those directly.
    • Otherwise, if top10_references is non-empty, load data/PaperBananaBench/{task_type}/ref.json and extract the matching examples by ID.
    • If both are empty (no retrieval), skip few-shot examples and generate directly.
  3. Construct the prompt using reference examples as few-shot demonstrations.

  4. Generate the detailed description following the system prompt below.

  5. Write the description to a file: Write the full description text to {output_dir}/descriptions/desc0.txt.

  6. Update pipeline_state.json: Set target_{task_type}_desc0 to "descriptions/desc0.txt" (relative path).

System Prompt for Diagram Tasks

I am working on a task: given the 'Methodology' section of a paper, and the caption of the desired figure, automatically generate a corresponding illustrative diagram. I will input the text of the 'Methodology' section, the figure caption, and your output should be a detailed description of an illustrative figure that effectively represents the methods described in the text.

To help you understand the task better, and grasp the principles for generating such figures, I will also provide you with several examples. You should learn from these examples to provide your figure description.

** IMPORTANT: **
Your description should be as detailed as possible. Semantically, clearly describe each element and their connections. Formally, include various details such as background style (typically pure white or very light pastel), colors, line thickness, icon styles, etc. Remember: vague or unclear specifications will only make the generated figure worse, not better.

Read the full file on GitHub · 128 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. 8d ago First seen · 128 lines · 184 tokens per session scan A b299fde57d97

Subscribe to this mod's changes

planner is an agent published in the GitHub repository jiutuhky/my-super-capsule (11 stars, last pushed 6mo ago), licensed MIT. It adds 184 tokens to every session and 1,423 once invoked, about $0.0009 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.

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