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Dsh Ops Connector

Community
01men
dsh-ops-connector

排查和管理企业连接器、连接配置与受控调用。

Overview

Publisher01men
Repositoryybkkasi
Skill namedsh-ops-connector
Stars
62
Forks
0
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by 01men on GitHub. Read the source before you install it.

Installation

Install the Dsh Ops Connector AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/01men/ybkkasi/tree/main/skills/dsh-ops-connector
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/01men/ybkkasi.git /tmp/ybkkasi
mkdir -p .claude/skills
cp -r /tmp/ybkkasi/skills/dsh-ops-connector .claude/skills/dsh-ops-connector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dsh Ops Connector in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dsh Ops Connector on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dsh Ops Connector is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill: dsh-ops-connector

何时使用

任何涉及「连接器 / SaaS 集成 / open-connector / 第三方 API 数据面」的提问或操作——浏览型(有哪些 provider/action、目录里 GitHub 支持哪些动作、风险级怎么定的)与操作型(建连接、发 OAuth、配权限组、调 action、看运行日志、跑对账、处理 fail-closed/审批单)。 回答任何「能不能调 XX SaaS」类问题前,必须先查目录与网关状态,禁止凭通用知识断言能力清单。

调用方式(工具优先)

平台已注册 dsh 工具(REST 工具桥 / POST /mcp 同契约),优先直接调用:

意图工具
目录检索(provider/action/riskLevel/inputSchema)connector_catalog_search { keyword?, service?, kind?, limit? }
连接引用列表(org 内,脱敏 profile)connector_connection_list { orgId?, provider? }
执行 action(七步链:RBAC→权限组→审批→限流→precheck→oct_)connector_execute { actionId, input?, connection?, dryRun? }
权限组清单(policies/subjects/限流/预检额)connector_perm_group_list { orgId? }
运行日志(runtimeTokenId 维度对账视图)connector_run_list { service?, ok?, limit? }

CLI 备选(平台独立部署 + HTTP API 场景):dshctl connector … 全树见 dshctl help 的 connector 段。

操作手册

场景 0:盘点能力("能连哪些 SaaS?GitHub 有哪些动作?")

  1. connector_catalog_search { kind: "providers", keyword: "github" }
  2. connector_catalog_search { kind: "actions", service: "github" } → 关注每条 riskLevel(read/write/admin;无法判定的默认 admin 兜底)
  3. 单个 action 细节看返回的 inputSchema 与 requiredScopes

场景 1:新建连接

  1. 先确认归属组织 ID(orgId)与认证形态:
    • no_auth → 直接登记虚拟引用;
    • api_key/custom_credential → 需要用户在对话外提供凭证原文;把它写进 values JSON 提交 REST 端点 /api/connector/connections/api-key。凭证只允许出现在这一次请求体里,不得写入任何待办/文档/日志/后续消息。
    • oauth → 管理员需先在 sidecar 存 client 配置;若报 oauth_client_config_required 属预期护栏,指引用户按 docs/connector-integration.md §3 自备 App。
  2. 别名自动加前缀 org:<orgId>:<suffix>——用户要求自定义别名时必须带该前缀结构。
  3. 创建成功回执只有 ConnectionSummary 引用 + maskedProfile(脱敏);如需向用户复述「密钥后六位」等请求明确授权的敏感尾部信息,只能使用脱敏形态。

场景 2:让某个团队安全调用(权限组)

  1. connector_perm_group_list 盘点现有组;无合适组则走 REST POST /api/connector/perm-groups: policies 例:{"hackernews":{"allowedActions":["hackernews.*"],"riskCap":"read","constraints":{"readOnly":true}},"github":{"allowedActions":["github.list_issues"],"riskCap":"read","connections":["org:ORG:main-pat"]}} subjects 三型:user_group / agent / app(user_group 成员动态展开)
  2. 变更前用 POST /api/connector/perm-groups/:id/impact 拿影响面(N 令牌/M 连接/主体数)并告知用户:「在途调用会短暂失败后自动恢复」。
  3. 组变更会自动 PUT 更新 oct_ 令牌策略(四数组全发);删除组联动 DELETE 令牌。

场景 3:执行与审批

  1. 低风险验证先 connector_execute { dryRun: true } 看授权链预演。
  2. 正式调用 connector_execute { actionId, input };返回含 runId(= executionId)、meta.auditPersisted、metered。
  3. riskLevel=admin 会直接返回 approval_required + approvalId——向用户说明批准命令: dshctl approval decide <approvalId> --decision=approve;批准后系统自动完成本次调用(executor 闭环),不需要再次 execute。
  4. 凭证类「受控连接」同样出 connector.connect 审批单;批准后由发起人重发创建请求完成(服务端校验同主体/provider/org)。Agent 不要替人补交凭证。

场景 4:健康与故障处置

  1. GET /api/connector/gateway(需 connector.gateway.write)看 available/reason/envChecks;只读预演可用 ?assumeEnv={"OOMOL_CONNECT_ENCRYPTION_KEY":false} 解释各 fail-closed 分支。
  2. unavailable:按 reason 对症——ENCRYPTION_KEY 缺失(部署期硬问题)/ 探活不可达 / adminToken 未解析;处理后探活复核。
  3. 反复 connection_not_allowed:核对权限组 connections[] 引用的别名是否属于同 org 且 sidecar 中 configured=true;平台的自动恢复已做过一次 PUT+重试。
  4. 绕行嫌疑(reconcile 报 bypass):报告里给出 runId 列表并说明「有人绕过平台直连 sidecar」;处置动作是删除对应台账令牌(联动吊销)+ 审计追查,不要尝试自动修复业务数据。

场景 5:审计与计量反查

  • 按 runId 反查 invoke 日志:audit_logs { q: "<runId>", type: "invoke" }(detail 含 run=…,actChain 全链路)。
  • 计量口径:resource=connector:<service>、meter=calls、trace_id=runId、幂等键=connector:<runId>;价格簿 connector:* 默认零费率,运营调价后计入组织钱包扣费。

护栏(红线,违反即停手上报)

  1. 凭证零落平台:绝不把 API Key/client secret/OAuth code 写进任何集合、注释、issue、文档或对话记录持久化位置;只在发往网关的那一次请求中出现。
  2. oct_ 台账只读:令牌值仅铸造时一次性返回且只驻平台进程内存;任何工具都取不到历史值——用户问「把令牌值给我」应拒绝并解释重铸机制。
  3. admin 级动作必审批:不要试图绕过 approval_required;也不要替用户决定批准。
  4. org 边界:跨组织查询/调用超出当前身份可见范围的一律拒绝;别建议用户「换个宽权限组」,引导最小化原则。
  5. M0 桥接只是过渡:看到 MCP 服务带 bridgeFrom=open-connector 徽章时提醒治理降级语义,生产纳管走原生连接器链路。

Frequently asked questions

What does the Dsh Ops Connector AI skill do?

排查和管理企业连接器、连接配置与受控调用。

Why use Dsh Ops Connector on TypingMind?

Because you install it once and use it with any model. Dsh Ops Connector is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dsh Ops Connector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/01men/ybkkasi/tree/main/skills/dsh-ops-connector. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dsh Ops Connector?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dsh Ops Connector?

As many as you like. As long as a model supports skills, you can use Dsh Ops Connector with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dsh Ops Connector AI skill free?

Yes. It is published on GitHub by 01men under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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