skill_manage
Manage AI skills for FleetQ MCP server, including creating, updating, deleting, and configuring skills with guardrails, multi-model support, and execution controls.
Instructions
Manage AI skills. Actions: list, get, create, update, delete, versions, guardrail, multi_model, code_exec, browser. Note: supabase_edge_function not available in cloud.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Action to perform: list, get, create, update, delete, versions, guardrail, multi_model, code_exec, browser | |
| type | No | Filter by type: llm, connector, rule, hybrid | |
| limit | No | Max results to return (default 10, max 100) | |
| skill_id | Yes | The skill UUID | |
| name | Yes | Skill name | |
| description | No | Skill description | |
| prompt_template | No | System prompt template for LLM-backed skills | |
| data_classification | No | Data classification level: public, internal, confidential, restricted. | |
| step_id | No | For get_result. The playbook step UUID. | |
| workflow_node_id | No | For set_node_guardrail / remove_node_guardrail. The workflow node UUID. | |
| guardrail_skill_id | No | For set_node_guardrail. The guardrail skill UUID to attach. | |
| execution_id | No | For get_execution: the SkillExecution UUID. | |
| worktree_execution_id | No | For get_execution / get_diff: the WorktreeExecution UUID. | |
| status | No | For list_executions: filter by status (pending_approval, completed, failed, approved, rejected). |