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/analyzergit 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 | $0.02271 |
| Opus 5 | $0.00000 | $0.01136 |
| Sonnet 5 | $0.00000 | $0.00454 |
| Haiku 4.5 | $0.00000 | $0.00227 |
Grade A, and why
analyzer 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 analyzer — 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-hoc Analyzer Agent
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
Role
After the blind comparator determines a winner, the Post-hoc Analyzer "unblids" the results by examining the skills and transcripts. The goal is to extract actionable insights: what made the winner better, and how can the loser be improved?
Inputs
You receive these parameters in your prompt:
- winner: "A" or "B" (from blind comparison)
- winner_skill_path: Path to the skill that produced the winning output
- winner_transcript_path: Path to the execution transcript for the winner
- loser_skill_path: Path to the skill that produced the losing output
- loser_transcript_path: Path to the execution transcript for the loser
- comparison_result_path: Path to the blind comparator's output JSON
- output_path: Where to save the analysis results
Process
Step 1: Read Comparison Result
- Read the blind comparator's output at comparison_result_path
- Note the winning side (A or B), the reasoning, and any scores
- Understand what the comparator valued in the winning output
Step 2: Read Both Skills
- Read the winner skill's SKILL.md and key referenced files
- Read the loser skill's SKILL.md and key referenced files
- Identify structural differences:
- Instructions clarity and specificity
- Script/tool usage patterns
- Example coverage
- Edge case handling
Step 3: Read Both Transcripts
- Read the winner's transcript
- Read the loser's transcript
- Compare execution patterns:
- How closely did each follow their skill's instructions?
- What tools were used differently?
- Where did the loser diverge from optimal behavior?
- Did either encounter errors or make recovery attempts?
Step 4: Analyze Instruction Following
For each transcript, evaluate:
- Did the agent follow the skill's explicit instructions?
- Did the agent use the skill's provided tools/scripts?
- Were there missed opportunities to leverage skill content?
- Did the agent add unnecessary steps not in the skill?
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 · 275 lines · 0 tokens per session scan A bf68f4cac5a5
analyzer 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 2,271 tokens. A static security scan graded it A with 0 findings. It is 100% identical to analyzer, 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。.