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 synthesize-reportgit 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/synthesize-report)<a href="https://agentmods.dev/skills/agentscope-ai/agentscope-java/synthesize-report"><img src="https://agentmods.dev/badge/skills/agentscope-ai/agentscope-java/synthesize-report/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/synthesize-report"><img src="https://agentmods.dev/badge/skills/agentscope-ai/agentscope-java/synthesize-report.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 | $0.00029 | $0.00318 |
| Opus 5.5 | $0.00012 | $0.00127 |
| Sonnet 5.5 | $0.00006 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
synthesize-report 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.
What it actually says
Synthesize a report from sources
A playbook for producing a citation-grounded summary across one or more source files in the workspace.
When to use
The user asks for a summary, briefing, literature review, or comparison and the
material is reachable via list_files / read_file / grep_files.
Steps
- Inventory. Run
list_fileson the user-specified directory (or the whole workspace) and pick the files most likely to contain the answer. - Skim. Read each candidate file once. Note the headings and any obvious claim sentences.
- Extract. For every claim worth keeping, capture:
- the claim in one sentence,
- the shortest supporting quote (≤ 25 words), and
- the file path.
- Group. Cluster claims by topic, not by source file.
- Compose. Use the template in
AGENTS.mdto write the report. Prefer bulleted lists; reserve prose for the introduction and conclusion. - Self-check. Re-read the draft and remove any sentence that is not supported by a citation.
Output
Markdown report following the AGENTS.md template. End with a ## Sources
section listing every cited file with a one-line gloss.
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 · 36 lines · 29 tokens per session scan A 2c801e35b8dd
synthesize-report is a skill published in the GitHub repository agentscope-ai/agentscope-java (6,102 stars, last pushed today), licensed Apache-2.0. It adds 29 tokens to every session and 318 once invoked, about $0.0001 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…
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…
llama.cpp
Run LLM inference with llama.cpp on CPU, Apple Silicon, AMD/Intel GPUs, or NVIDIA — plus GGUF model conversion and quantization (2–8 bit with K-quants and imatrix). Covers CLI, Python bindings, OpenAI-compatible server, and Ollama/LM Studio integration. Use for edge deployment, M1/M2/M3/M4 Macs, CUDA-less…
Obliteratus
Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament…
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.
Segment Anything
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.