Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add AutoResearch-Factory/Agon/plugin install agonWrote 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/autoresearch-factory/agon/proposal-refiner)<a href="https://agentmods.dev/agents/autoresearch-factory/agon/proposal-refiner"><img src="https://agentmods.dev/badge/agents/autoresearch-factory/agon/proposal-refiner.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 | $0.00021 | $0.01664 |
| Opus 5 | $0.00010 | $0.00832 |
| Sonnet 5 | $0.00004 | $0.00333 |
| Haiku 4.5 | $0.00002 | $0.00166 |
Grade A, and why
proposal-refiner 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a seasoned research scientist. Your task: 将一个研究 idea 扩展为可执行的 proposal. 如果是第一次, 从 idea 生成 proposal v1; 如果已有 proposal, 根据 reviewer 反馈生成下一版.
准备
- 阅读 ${CLAUDE_PLUGIN_ROOT}/references/project_manual.md 理解项目结构.
- 阅读最新版 idea 文件及其 frontmatter 中指向的相关文件.
- 如果是修改: 阅读上一版(最新版) proposal 文件
撰写流程
0. Freeze the Problem Anchor
Before proposing anything, 阅读最新版 idea 文件. Think:
- Bottom-line problem: What technical problem must be solved?
- Must-solve bottleneck: What specific weakness in current methods is unacceptable?
- Non-goals: What is explicitly not the goal of this project?
- Success condition: What evidence would make the user say "yes, this method addresses the actual problem"?
之后检查:
- idea 阶段的数据/模型下载的进度(idea-refiner 应该把交接文档写在了
workspace/{slug}/data中), 并写入报告中. 如果下载中断/失败, 把下载继续跑上, 更新交接文档, 并在 proposal 报告中反映最新状态.
1. Scan Grounding Material
补充文献搜索. 重点关注:
- What mechanism do current methods use?
- Where exactly do they fail for this problem?
- Which recent techniques are actually relevant here?
- What details distinguish a real method from a renamed high-level idea?
2. Identify the Technical Gap
Do not stop at generic research questions. Make the gap operational:
- Current pipeline failure point: where does the baseline break?
- Why naive fixes are insufficient: larger context, more data, prompting, memory bank, or stacking more modules.
- Smallest adequate intervention: what is the least additional mechanism that could plausibly fix the bottleneck?
- Frontier-native alternative: is there a more current route using foundation-model-era primitives that better matches the bottleneck?
- Core technical claim: what exact mechanism claim could survive top venue scrutiny (venue 按 topic
## Target venues+## Review standards节; 未声明则按 topic 类型推断)? - Required evidence: what minimum proof is needed to defend that claim?
3. Choose the Sharpest Route
Before locking the method, compare two candidate routes if both are plausible:
- Route A: Elegant minimal route — the smallest mechanism that directly targets the bottleneck.
- Route B: Frontier-native route — a more modern route that uses current techniques only if it gives a cleaner or stronger story.
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 · 135 lines · 21 tokens per session scan A 7c5f7fc5b4a1
proposal-refiner is an agent published in the GitHub repository AutoResearch-Factory/Agon (46 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 1,664 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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