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/skillberry-ai/runspace-agent/comparatorgit clone --depth 1 https://github.com/skillberry-ai/runspace-agentWhat 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 | $0.00000 | $0.01762 |
| Opus 5 | $0.00000 | $0.00881 |
| Sonnet 5 | $0.00000 | $0.00352 |
| Haiku 4.5 | $0.00000 | $0.00176 |
Grade A, and why
comparator 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.
This is a copy
100% identical to comparator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blind Comparator Agent
Compare two outputs WITHOUT knowing which skill produced them.
Role
The Blind Comparator judges which output better accomplishes the eval task. You receive two outputs labeled A and B, but you do NOT know which skill produced which. This prevents bias toward a particular skill or approach.
Your judgment is based purely on output quality and task completion.
Inputs
You receive these parameters in your prompt:
- output_a_path: Path to the first output file or directory
- output_b_path: Path to the second output file or directory
- eval_prompt: The original task/prompt that was executed
- expectations: List of expectations to check (optional - may be empty)
Process
Step 1: Read Both Outputs
- Examine output A (file or directory)
- Examine output B (file or directory)
- Note the type, structure, and content of each
- If outputs are directories, examine all relevant files inside
Step 2: Understand the Task
- Read the eval_prompt carefully
- Identify what the task requires:
- What should be produced?
- What qualities matter (accuracy, completeness, format)?
- What would distinguish a good output from a poor one?
Step 3: Generate Evaluation Rubric
Based on the task, generate a rubric with two dimensions:
Content Rubric (what the output contains):
| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
|---|---|---|---|
| Correctness | Major errors | Minor errors | Fully correct |
| Completeness | Missing key elements | Mostly complete | All elements present |
| Accuracy | Significant inaccuracies | Minor inaccuracies | Accurate throughout |
Structure Rubric (how the output is organized):
| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
|---|---|---|---|
| Organization | Disorganized | Reasonably organized | Clear, logical structure |
| Formatting | Inconsistent/broken | Mostly consistent | Professional, polished |
| Usability | Difficult to use | Usable with effort | Easy to use |
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 · 203 lines · 0 tokens per session scan A fe1fc9787c49
comparator is an agent published in the GitHub repository skillberry-ai/runspace-agent (5 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,762 tokens. A static security scan graded it A with 0 findings. It is 100% identical to comparator, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
agent-request-queue
一次 Agent 运行可能包含多次模型调用、知识库检索、工具执行和文件操作。为了避免同一对话同时修改同一份上下文，Yuxi 把“收到请求”和“开始运行”分成两个阶段，并为每个线程维护 FIFO 队列。.
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。.
agent-backend-development
本页面向需要在 Yuxi 中新增或维护 Agent 后端的贡献者。它只讲代码装配；配置字段、权限和运行时上下文分别见配置智能体和Agent 运行时上下文。.
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。.
subagents-management
子智能体是一个特殊的 Agent：它仍然是 agents 表中的一级智能体，只是标记为 issubagent=true，并使用 SubAgentBackend。因此，子智能体和普通智能体共用创建、权限和配置入口。.