Xpersona

Crawler Summary

luban-config-editor answer-first brief

Luban游戏配置表编辑器。当用户上传Excel文件并请求"修改配置表"、"编辑Luban表格"、"更新游戏配置"、"查看配置数据"时触发。支持符合Luban格式（含##var和##type行）的Excel表格的查询、更新、插入和删除操作。 --- name: luban-config-editor description: Luban游戏配置表编辑器。当用户上传Excel文件并请求"修改配置表"、"编辑Luban表格"、"更新游戏配置"、"查看配置数据"时触发。支持符合Luban格式（含##var和##type行）的Excel表格的查询、更新、插入和删除操作。 --- Luban 配置表编辑器 (v2.1) 采用 **"Query (精读) + Patch (微修)"** 模式处理 Luban 格式 Excel 配置表。 ⚠️ 强制约束 **严禁直接使用 pandas/openpyxl 操作 Excel 文件。** 必须通过脚本完成所有操作： - scripts/query.py — 查询（摘要/条件/分页/列裁剪） - scripts/patch.py — 修改（update/insert/delete + Diff预览） 核心原则 1. **最小化读取**：严 Capability contract not published. No trust telemetry is available yet. 4 GitHub stars reported by the source. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

luban-config-editor is best for general automation workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGitHubSafety: 94/100

luban-config-editor

Luban游戏配置表编辑器。当用户上传Excel文件并请求"修改配置表"、"编辑Luban表格"、"更新游戏配置"、"查看配置数据"时触发。支持符合Luban格式（含##var和##type行）的Excel表格的查询、更新、插入和删除操作。 --- name: luban-config-editor description: Luban游戏配置表编辑器。当用户上传Excel文件并请求"修改配置表"、"编辑Luban表格"、"更新游戏配置"、"查看配置数据"时触发。支持符合Luban格式（含##var和##type行）的Excel表格的查询、更新、插入和删除操作。 --- Luban 配置表编辑器 (v2.1) 采用 **"Query (精读) + Patch (微修)"** 模式处理 Luban 格式 Excel 配置表。 ⚠️ 强制约束 **严禁直接使用 pandas/openpyxl 操作 Excel 文件。** 必须通过脚本完成所有操作： - scripts/query.py — 查询（摘要/条件/分页/列裁剪） - scripts/patch.py — 修改（update/insert/delete + Diff预览） 核心原则 1. **最小化读取**：严

OpenClawself-declared

Public facts

5

Change events

1

Artifacts

0

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals4 GitHub stars

Capability contract not published. No trust telemetry is available yet. 4 GitHub stars reported by the source. Last updated 4/15/2026.

4 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Apr 15, 2026

Vendor

Ashramc

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. 4 GitHub stars reported by the source. Last updated 4/15/2026.

View Source

Setup snapshot

git clone https://github.com/AshramC/Luban-Config-Skill.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Ashramc

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Apr 15, 2026Source linkProvenance
Adoption (1)

Adoption signal

4 GitHub stars

profilemedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

6

Snippets

0

Languages

typescript

Parameters

Executable Examples

bash

pip install pandas openpyxl --break-system-packages

bash

python scripts/query.py --excel_path "data.xlsx" --summary

json

{
  "Items": {
    "rows": 2000,
    "pk": "id",
    "fields": [
      {"name": "id", "type": "int"},
      {"name": "rewards", "type": "map,int,int"}
    ]
  }
}

bash

python scripts/query.py --excel_path "data.xlsx" --sheet Items --where "id=1001"

bash

python scripts/query.py --excel_path "data.xlsx" --sheet Items --columns "id,name" --limit 100 --offset 0

python

import json

patch = {
    "Items": {
        "update": [
            {"id": 102, "active": False},
            {"id": 103, "rewards": {1001: 5, 1002: 10}}  # Map类型
        ],
        "insert": [
            {"id": 201, "name": "新道具", "tags": ["vip", "test"]}  # Array类型
        ],
        "delete": [103, 104]
    }
}

with open('patch.json', 'w', encoding='utf-8') as f:
    json.dump(patch, f, ensure_ascii=False, indent=2)

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Luban游戏配置表编辑器。当用户上传Excel文件并请求"修改配置表"、"编辑Luban表格"、"更新游戏配置"、"查看配置数据"时触发。支持符合Luban格式（含##var和##type行）的Excel表格的查询、更新、插入和删除操作。 --- name: luban-config-editor description: Luban游戏配置表编辑器。当用户上传Excel文件并请求"修改配置表"、"编辑Luban表格"、"更新游戏配置"、"查看配置数据"时触发。支持符合Luban格式（含##var和##type行）的Excel表格的查询、更新、插入和删除操作。 --- Luban 配置表编辑器 (v2.1) 采用 **"Query (精读) + Patch (微修)"** 模式处理 Luban 格式 Excel 配置表。 ⚠️ 强制约束 **严禁直接使用 pandas/openpyxl 操作 Excel 文件。** 必须通过脚本完成所有操作： - scripts/query.py — 查询（摘要/条件/分页/列裁剪） - scripts/patch.py — 修改（update/insert/delete + Diff预览） 核心原则 1. **最小化读取**：严

Full README

name: luban-config-editor description: Luban游戏配置表编辑器。当用户上传Excel文件并请求"修改配置表"、"编辑Luban表格"、"更新游戏配置"、"查看配置数据"时触发。支持符合Luban格式（含##var和##type行）的Excel表格的查询、更新、插入和删除操作。

Luban 配置表编辑器 (v2.1)

采用 "Query (精读) + Patch (微修)" 模式处理 Luban 格式 Excel 配置表。

⚠️ 强制约束

严禁直接使用 pandas/openpyxl 操作 Excel 文件。

必须通过脚本完成所有操作：

  • scripts/query.py — 查询（摘要/条件/分页/列裁剪）
  • scripts/patch.py — 修改（update/insert/delete + Diff预览）

核心原则

  1. 最小化读取：严禁无理由全量拉取。必须用 --columns 裁剪列，用 --limit/--offset 分页
  2. 内存逻辑：工具只负责基础筛选，复杂判断（如 age > 10）由 LLM 在读取数据后内存处理
  3. 预览先行：修改前优先用 --dry_run 查看 Diff
  4. 原子修改：Patch 中只包含主键和真正变化的字段

前置条件

pip install pandas openpyxl --break-system-packages

支持的数据类型

基础类型

| 类型定义 | 说明 | Excel示例 | Python映射 | |----------|------|-----------|------------| | int, int32, long | 整数 | 100 | int | | byte, short | 小整数 | 255 | int | | datetime | 时间戳 | 1704067200 | int | | float, double | 浮点数 | 3.14 | float | | bool | 布尔值 | true / 1 / yes / on | bool | | string, text | 文本字符串 | hello | str |

Bool兼容性：读取时兼容 true/false, 1/0, yes/no, t/f, on/off（不区分大小写）

容器类型

| 类型定义 | Excel格式 | Python映射 | |----------|-----------|------------| | array,int | 1,2,3 | [1, 2, 3] | | list,string | a,b,c | ["a", "b", "c"] | | set,int | 1,2,3 | [1, 2, 3] | | (array#sep=\|),int | 1\|2\|3 | [1, 2, 3] | | map,int,string | 1:a,2:b | {1: "a", 2: "b"} | | (map#sep=\|),int,int | 1:10\|2:20 | {1: 10, 2: 20} |

Map KV分隔符：读取时兼容 _、:、=，写入时统一使用 :

标准操作步骤

第一步：结构侦察

获取表名、主键、列名列表：

python scripts/query.py --excel_path "data.xlsx" --summary

输出示例：

{
  "Items": {
    "rows": 2000,
    "pk": "id",
    "fields": [
      {"name": "id", "type": "int"},
      {"name": "rewards", "type": "map,int,int"}
    ]
  }
}

第二步：外科手术式读取

场景A：已知主键

python scripts/query.py --excel_path "data.xlsx" --sheet Items --where "id=1001"

场景B：批量扫描（必须裁剪+分页）

python scripts/query.py --excel_path "data.xlsx" --sheet Items --columns "id,name" --limit 100 --offset 0

第三步：生成Patch

import json

patch = {
    "Items": {
        "update": [
            {"id": 102, "active": False},
            {"id": 103, "rewards": {1001: 5, 1002: 10}}  # Map类型
        ],
        "insert": [
            {"id": 201, "name": "新道具", "tags": ["vip", "test"]}  # Array类型
        ],
        "delete": [103, 104]
    }
}

with open('patch.json', 'w', encoding='utf-8') as f:
    json.dump(patch, f, ensure_ascii=False, indent=2)

第四步：安全预览

python scripts/patch.py --excel_path "data.xlsx" --patch_path patch.json --dry_run

输出 Diff：

{
  "Items": {
    "updated": 2,
    "diff": [
      "UPDATE pk=102: active: True -> False",
      "UPDATE pk=103: rewards: 1001:3,1002:5 -> 1001:5,1002:10"
    ]
  }
}

必须检查 Diff：是否修改了错误的行？值的变化是否合理？

第五步：执行

python scripts/patch.py --excel_path "data.xlsx" --patch_path patch.json

Query 参数速查

| 参数 | 说明 | 示例 | |------|------|------| | --summary | 获取所有Sheet结构 | --summary | | --sheet | 指定Sheet名 | --sheet Items | | --where | 主键条件 | --where "id=1001" | | --columns | 只返回指定列 | --columns "id,name" | | --limit | 最大返回行数 | --limit 100 | | --offset | 跳过前N行 | --offset 100 |

Patch 格式规范

{
  "SheetName": {
    "update": [{"pk": value, "field1": newValue}],
    "insert": [{"pk": value, "field1": value, ...}],
    "delete": [pk1, pk2, ...]
  }
}

| 操作 | 说明 | |------|------| | update | 必须含主键，只写变更字段，智能跳过无变化字段 | | insert | 必须含主键和必要字段 | | delete | 主键列表，软删除（标记#DELETED#） |

JSON生成规范

禁止手写JSON字符串，必须用 dict + json.dump

# ❌ 严禁
json_str = '{"name": "他说"你好""}'

# ✅ 正确
data = {"name": '他说"你好"'}
json.dump(data, f, ensure_ascii=False)

常见场景策略

| 场景 | ❌ 错误做法 | ✅ 正确做法 | |------|------------|------------| | 全表搜索 | 一次性query全表 | 分批 --limit 100 --offset N | | 修改某列 | 读取整行 select * | 只读主键+目标列 --columns "id,gold" | | 验证结果 | 修改后query全表 | 查看 --dry_run 的 diff 字段 |

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/snapshot"
curl -s "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/contract"
curl -s "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_REPOSactivepieces

Rank

70

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW
GITHUB_REPOScherry-studio

Rank

70

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSCopilotKit

Rank

70

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW
Agent hubMore from this sourceOpenClaw agents
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-17T02:15:19.651Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Ashramc",
    "href": "https://github.com/AshramC/Luban-Config-Skill",
    "sourceUrl": "https://github.com/AshramC/Luban-Config-Skill",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T03:13:52.962Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T03:13:52.962Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "4 GitHub stars",
    "href": "https://github.com/AshramC/Luban-Config-Skill",
    "sourceUrl": "https://github.com/AshramC/Luban-Config-Skill",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T03:13:52.962Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/ashramc-luban-config-skill/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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