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.
git clone --depth 1 https://github.com/jiutuhky/my-super-capsuleWrote 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/agents/jiutuhky/my-super-capsule/planner)<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.
<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>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 | $0.00184 | $0.01423 |
| Opus 5 | $0.00092 | $0.00711 |
| Sonnet 5 | $0.00037 | $0.00285 |
| Haiku 4.5 | $0.00018 | $0.00142 |
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.
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
-
Read
pipeline_state.jsonto 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)
-
Load reference examples:
- If
retrieved_examplesis non-empty (manual mode), use those directly. - Otherwise, if
top10_referencesis non-empty, loaddata/PaperBananaBench/{task_type}/ref.jsonand extract the matching examples by ID. - If both are empty (no retrieval), skip few-shot examples and generate directly.
- If
-
Construct the prompt using reference examples as few-shot demonstrations.
-
Generate the detailed description following the system prompt below.
-
Write the description to a file: Write the full description text to
{output_dir}/descriptions/desc0.txt. -
Update pipeline_state.json: Set
target_{task_type}_desc0to"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.
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.
- 8d ago First seen · 128 lines · 184 tokens per session scan A b299fde57d97
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.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
Geoprocessing Specialist
ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
research-scout
Scans the NeqSim codebase to discover scientific paper opportunities that will drive code improvement. Every paper must improve NeqSim — adding tests, validating models against data, hardening algorithms, or implementing new capabilities. Produces ranked, actionable topics that feed into the planner agent.
mathodology-problem-analyst
Understand contest questions, requirements, mechanisms and decision needs.
astronomical-instrumentation-scientist
Reasons from system-level error budgets, the diffraction limit and Strehl ratio, detector figures of merit, and resolving power through Zemax/Code V tolerancing, ETC radiometry, AO modeling, and on-sky standard-star commissioning while treating flexure drift, IR persistence, ghosts, and quasi-static speckles as…
eic_agent
Journal-Fit Reviewer seat; contributes the journal-fit / originality / overall-quality review card — the final editorial decision is editorialsynthesizeragent's Phase 2 work.