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/zeweihan/aiworkdeck/feedback-optimizergit clone --depth 1 https://github.com/zeweihan/aiworkdeckWhat 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 | $0.00057 | $0.02552 |
| Opus 5 | $0.00028 | $0.01276 |
| Sonnet 5 | $0.00011 | $0.00510 |
| Haiku 4.5 | $0.00006 | $0.00255 |
Grade A, and why
feedback-optimizer 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 2d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 2d ago First seen · 117 lines · 57 tokens per session scan A 1faf52715271
feedback-optimizer is an agent published in the GitHub repository zeweihan/aiworkdeck (79 stars, last pushed 2d ago), licensed AGPL-3.0. It adds 57 tokens to every session and 2,552 once invoked, about $0.0003 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
skills-management
Skill 是一个可复用的能力包，通常包含一个 SKILL.md、提示词、参考资料和可选脚本。智能体先看到 Skill 的描述，再按需要读取 SKILL.md；Skill 声明的工具和 MCP 依赖会随激活状态加入模型请求。.
agents-config
本页是智能体配置参考，说明页面上的字段如何进入一次运行。新增智能体后端的代码结构见开发智能体后端；只使用现成智能体时，从快速开始开始。.
mcp-integration
MCP（Model Context Protocol）让智能体调用外部服务提供的工具。管理员在“扩展 → MCP”中添加远程服务器，智能体配置再决定哪些服务器进入运行时。.
tools-system
Yuxi 的工具分成三层：内置工具、知识库工具和 MCP 工具。Graph 创建时准备可执行工具，运行时再根据用户权限、Agent 配置和 Skill 激活状态决定模型能看到什么。.
agent-evaluation
智能体评估用 Langfuse Dataset 保存一组固定任务，再让 Yuxi 按真实的 AgentRun、worker 和工具链路逐条执行。它适合比较一个智能体在研究、编程、文件处理或多步骤任务上的表现。.
middleware
中间件把文件、Skills、子智能体、上下文压缩、审批和用量统计接到 LangGraph Agent。它们在模型调用、工具调用或 state 更新的边界运行，让不同 Agent 复用同一套能力。.