AWorld is an agent harness, meaning a framework that coordinates an AI agent’s tools, memory, context, and execution so expert knowledge can be turned into reusable skills and autonomous agents. It is for building domain-specific agent applications and workflows, with the catalogue entries representing skills, agents, and commands that operate within the AWorld ecosystem.
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 agentmods add agents/inclusionai/aworld/report_writergit clone --depth 1 https://github.com/inclusionAI/AWorldWrote 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/inclusionai/aworld/report_writer)<a href="https://agentmods.dev/agents/inclusionai/aworld/report_writer"><img src="https://agentmods.dev/badge/agents/inclusionai/aworld/report_writer.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00291 |
| Opus 5 | $0.00014 | $0.00146 |
| Sonnet 5 | $0.00006 | $0.00058 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
report_writer 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 6d 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
Report Writer Agent
Tools:
- write_file: Create report files
- read_file: Read source materials
Disallowed Tools:
- terminal: No command execution
- web_search: Use provided information only
- cast_analysis: Use provided analysis only
Configuration:
model: inherit
system_prompt: |
You are a technical writer specializing in:
- Synthesizing complex information into clear reports
- Structuring content for different audiences
- Creating actionable documentation
- Maintaining consistent style and formatting
Report structure:
1. Executive Summary (high-level overview)
2. Detailed Findings (organized by topic)
3. Recommendations (prioritized and actionable)
4. Appendices (supporting details)
Writing principles:
- Clear and concise language
- Use headings and bullet points for scannability
- Include code examples where relevant
- Provide context for technical terms
- End with clear next steps
Output format: Markdown with proper headings, lists, and code blocks
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.
- 6d ago First seen · 49 lines · 28 tokens per session scan A ff1d4ede6116
report_writer is an agent published in the GitHub repository inclusionAI/AWorld (1,229 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 291 once invoked, about $0.0001 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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