feishuAgentAdd
Use this skill when users want to add a Feishu agent for OpenClaw, especially when they say things like “帮我增加一个名字叫xxx,用来做xxx的飞书agent”, want a guided prompt f...
Description
name: feishu-agent-add description: Use this skill when users want to add a Feishu agent for OpenClaw, especially when they say things like “帮我增加一个名字叫xxx,用来做xxx的飞书agent”, want a guided prompt flow, or want a one-command way to generate the matching OpenClaw config. license: MIT
feishu-agent-add
This skill is the conversational front end for the local script scripts/add_feishu_agent.py.
This project is designed for OpenClaw users, but the skill name intentionally stays short: feishu-agent-add.
When To Use
Use this skill when the user wants to:
- add a new Feishu-connected OpenClaw agent
- avoid hand-editing
openclaw.json - configure a new agent through a few follow-up questions
- get a ready-to-run one-line command for advanced usage
Core Rule
Do not hand-edit openclaw.json unless the user explicitly asks for manual fallback.
Prefer running:
python3 scripts/add_feishu_agent.py ...
The script is the execution core. This skill should mainly:
- understand the user's request
- ask only for missing required fields
- preview the plan
- run the script
- summarize the result and next steps
Required Inputs
Collect these fields before execution:
agent_namepurposeagent_id- if missing, propose one derived from the name
app_idapp_secret
These can use defaults unless the user says otherwise:
workspace_path- default:
~/.openclaw/workspace-{agent_id}
- default:
model- default: inherit from the current OpenClaw config
enable_agent_to_agent- default:
true
- default:
workspace_mode- default:
auto
- default:
init_templates- default:
true
- default:
Conversational Flow
1. Parse what the user already gave
For a request like:
帮我增加一个名字叫小红书运营,用来做内容选题和文案生成的飞书agent
extract:
agent_name = 小红书运营purpose = 内容选题和文案生成agent_id = xiaohongshuor another short slug candidate
If agent_id is missing, propose one instead of asking an open-ended question.
2. Ask the minimum follow-up questions
Only ask for the missing required fields. Prefer one compact message.
Typical follow-up:
- proposed
agent_id - Feishu
App ID - Feishu
App Secret
Only ask about optional fields if the user indicates they care.
3. Preview before execution
Before running the script, summarize:
- agent name
- agent id
- purpose
- workspace path
- whether agent-to-agent collaboration will be enabled
4. Run the script
Run from the skill directory:
python3 scripts/add_feishu_agent.py \
--agent-id <agent-id> \
--agent-name "<agent-name>" \
--purpose "<purpose>" \
--app-id <app-id> \
--app-secret <app-secret> \
--json-output \
--yes
Add optional flags only when needed:
--model <model>--workspace-path <path>--disable-agent-to-agent--workspace-mode cli|mkdir|auto--no-init-templates--dry-run
Advanced User Mode
If the user prefers a single terminal command, give them a ready-to-run example instead of a manual JSON recipe.
Use this pattern:
python3 scripts/add_feishu_agent.py \
--agent-id trader \
--agent-name "交易小助手" \
--purpose "股票和 ETF 分析" \
--app-id cli_xxx \
--app-secret secret_xxx \
--yes
Output Expectations
After execution, summarize:
- whether config was written successfully
- the workspace path
- whether starter files were initialized
- that OpenClaw should be restarted
- where to refine the agent identity, usually
SOUL.md
If the script fails, report the concrete reason and do not improvise partial manual edits unless the user asks for that fallback.
Notes
- The script already handles validation, backup, and config updates.
- Prefer
--dry-runfirst when the user asks for a preview. - If the user asks how to install or use this project, point them to
README.md.
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