AgentScope Java is a framework for building distributed software agents that use language models to perform tasks over extended sessions. It is intended for enterprise applications needing agent coordination, controlled tool use, isolated workspaces, event streaming, and distributed session management. The catalogue entries provide skills and instructions for working with this framework.
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.
npx skills add agentscope-ai/agentscope-java --skill chart-renderinggit clone --depth 1 https://github.com/agentscope-ai/agentscope-javaWrote 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/agentscope-ai/agentscope-java/chart-rendering)<a href="https://agentmods.dev/skills/agentscope-ai/agentscope-java/chart-rendering"><img src="https://agentmods.dev/badge/skills/agentscope-ai/agentscope-java/chart-rendering/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/agentscope-ai/agentscope-java/chart-rendering"><img src="https://agentmods.dev/badge/skills/agentscope-ai/agentscope-java/chart-rendering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00081 | $0.00765 |
| Opus 5.5 | $0.00032 | $0.00306 |
| Sonnet 5.5 | $0.00016 | $0.00153 |
| Haiku 4.5 | $0.00008 | $0.00076 |
Grade A, and why
chart-rendering 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 1mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chart Rendering Skill
Repeatable SOP for turning a result set into a chart that actually communicates the point.
Steps
-
Match chart type to question. Decide before drawing:
Question shape Chart "How does X change over time?" Line (single series) or multi-line "Composition / share of total" Stacked bar, or 100% stacked area "Comparison across a small set of categories" Bar (horizontal if labels are long) "Relationship between two numeric variables" Scatter "Distribution of a single variable" Histogram or box plot "Cumulative total" Area If none fit, ask the user which view they want — do not default to "any chart will do".
-
Prepare the data. The query result from the [[sql-analysis]] Skill should already be tidy (one row per data point, one column per dimension). If not, reshape first using pandas (
pivot,melt,groupby). Save the cleaned frame toknowledge/cache/<topic>.csvso it can be re-rendered. -
Render with matplotlib. Write the script to a file under
scratch/charts/<topic>.pyand execute it viashell_run. Conventions:- Title — one sentence answering the question (not "Untitled chart").
- Axes — label both, include units (
Active users (count),Date (UTC)). - Legend — include only if there is more than one series.
- Colours — use the matplotlib default palette unless the user has a
stated brand colour in
knowledge/style.md. - Output — save as PNG (
bbox_inches='tight',dpi=150) underknowledge/charts/<topic>.png.
-
Show and explain. In the assistant reply, link the saved image with a markdown image embed (
) and write 2–3 sentences interpreting it — what the chart shows, the one thing the user should take away, and any caveat (incomplete latest data point, outliers excluded, etc.). -
Keep the script reproducible. The chart file is the artefact; the script under
scratch/charts/<topic>.pyis the source of truth. If the user asks for a tweak ("can we use a log y-axis?"), edit the script and re-run — do not regenerate from scratch.
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.
- 1mo ago First seen · 66 lines · 81 tokens per session scan A 653a5e764393
chart-rendering is a skill published in the GitHub repository agentscope-ai/agentscope-java (6,013 stars, last pushed today), licensed Apache-2.0. It adds 81 tokens to every session and 765 once invoked, about $0.0003 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.
Other skills, from other repositories
p5.js
Production pipeline for interactive and generative visual art using p5.js. Creates browser-based sketches, generative art, data visualizations, interactive experiences, 3D scenes, audio-reactive visuals, and motion graphics — exported as HTML, PNG, GIF, MP4, or SVG. Covers: 2D/3D rendering, noise and particle systems…
AudioCraft
PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.
ASCII Video
Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics…
Manim Video
Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations…
LM Evaluation Harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
Weights & Biases
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform.