Borrowing it
Nothing to install: this file belongs to xiaoyuge886/aigc. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/xiaoyuge886/aigc/main/.claude/skills/x_agent_skill/SKILL.mdgit clone --depth 1 https://github.com/xiaoyuge886/aigcWrote 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/skills/xiaoyuge886/aigc/x_agent_skill)<a href="https://agentmods.dev/skills/xiaoyuge886/aigc/x_agent_skill"><img src="https://agentmods.dev/badge/skills/xiaoyuge886/aigc/x_agent_skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xiaoyuge886/aigc/x_agent_skill"><img src="https://agentmods.dev/badge/skills/xiaoyuge886/aigc/x_agent_skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 432 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 432 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Server-Side Request Forgery · line 432 Code issues a request to a loopback, link-local, or private-range host. This can reach internal services not meant to be exposed and is a common SSRF pivot.Fix: Avoid requests to loopback/link-local/private hosts from skill code. If internal access is intended, document it and validate the target against an allowlist.
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 | $0.00064 | $0.03643 |
| Opus 5 | $0.00032 | $0.01821 |
| Sonnet 5 | $0.00013 | $0.00729 |
| Haiku 4.5 | $0.00006 | $0.00364 |
Grade B, and why
xagent scanned grade B with 2 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 9d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post('http://localhost:8000/api/chat', json={ Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.post('http://localhost:8000/api/chat', json={ How it starts
The opening of the file, as written. The whole thing — 542 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XAgent - 智能跨域分析助手
一个全面的Claude技能，实现XAgent的智能跨域分析能力，以SOP驱动的规划、多工具协调和多层次数据分析为特色。
概览
XAgent是一个精密的跨域智能助手，在各个领域提供结构化、高质量的分析。它使用标准作业程序（SOP）确保一致的、高质量的输出，并协调多个工具高效完成复杂任务。
核心特性
🎯 核心能力
- SOP驱动规划：基于经过验证的方法论自动生成结构化执行计划
- 多层次数据分析：描述性分析 → 异常检测 → 相关性分析 → 预测性分析
- 多工具协调：智能选择和编排多个分析工具
- Prompt工程：使用先进的prompt模板确保一致的高质量输出
- 跨域能力：支持不同专业领域的专业分析指导
- MCP服务集成：自动发现和使用外部MCP服务
- 对话记忆：跨多次交互维护上下文，支持持续分析
🔧 技术特性
- 模块化架构：六个专业组件协同工作
- 实时进度跟踪：步骤执行监控
- 错误处理与重试：健壮的错误恢复机制
- 性能优化：并行执行和智能缓存
- 可扩展设计：易于添加新工具和分析方法
安装
该技能是自包含的，除了Python 3.8+外不需要任何外部依赖。所有组件都包含在包中。
使用方法
基础使用
from xagent_skill import XAgent, XAgentConfig
# 使用默认配置初始化
agent = XAgent()
# 处理用户查询
result = agent.process_request(
query="分析销售数据趋势并识别增长机会",
user_id="user_001"
)
if result['guidance_prompt']:
print(f"生成了专业指导prompt，长度: {len(result['guidance_prompt'])}字符")
print(f"推荐的工具: {result['recommended_tools']}")
print(f"包含多层次规划: {result['metadata']['has_hierarchical_planning']}")
if result['hierarchical_plan']:
print(f"预计工时: {result['hierarchical_plan']['estimated_timeline']['total_estimated_hours']}小时")
高级使用
from xagent_skill import XAgent, XAgentConfig
# 自定义配置
config = XAgentConfig(
enable_sop_guidance=True,
enable_hierarchical_planning=True,
enable_tool_coordination=True,
enable_domain_expertise=True,
enable_analysis_guidance=True,
prompt_language="zh-CN",
detail_level="comprehensive"
)
agent = XAgent(config)
# 多轮对话示例
session_id = "analysis_session"
# 第一次查询
result1 = agent.process_request(
query="分析Q3销售业绩",
user_id="analyst_001",
session_id=session_id
)
# 后续查询，带上下文
result2 = agent.process_request(
query="重点关注表现不佳的区域并提出改进建议",
user_id="analyst_001",
session_id=session_id
)
# 复杂报告写作
result3 = agent.process_request(
query="基于以上分析，撰写一份完整的季度业绩分析报告",
user_id="analyst_001",
session_id=session_id
)
组件
1. SOP规划技能 (SOPPlanningSkill)
What ships with it
21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- core/file_system_loader.py 15 KB runs code
- data/domain_knowledge/finance/banking_industry.yaml 6.8 KB
- data/sop_templates/financial_analysis.json 4.7 KB
- demo_dynamic_file_loading.md 10 KB
- guidance/__init__.py 764 B runs code
- guidance/data_analysis_guidance.py 20 KB runs code
- guidance/domain_expertise_guidance.py 23 KB runs code
- guidance/dynamic_file_loading_guidance.py 13 KB runs code
- guidance/hierarchical_planning_guidance.py 29 KB runs code
- guidance/intermediate_file_guidance.py 12 KB runs code
- guidance/sop_planning_guidance.py 17 KB runs code
- guidance/tool_coordination_guidance.py 25 KB runs code
- guidance/x_agent_core.py 20 KB runs code
- PROJECT_STRUCTURE.md 3.6 KB
- prompts/java_prompts.yaml 16 KB
- README.md 8.2 KB
- references/data_schema.md 7.4 KB
- setup.py 2.5 KB runs code
- skill.json 4.9 KB
- templates/report_template.json 11 KB
- xagent_skill.py 2.1 KB runs code
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.
- 9d ago First seen · 542 lines · 64 tokens per session scan B c51379ebb4d6
xagent is a skill published in the GitHub repository xiaoyuge886/aigc (197 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 3,643 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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