Kimi Delegation Skill OpenClaw Skill - ClawHub
Do you want your AI agent to automate Kimi Delegation Skill workflows? This free skill from ClawHub helps with web & frontend development tasks without building custom tools from scratch.
What this skill does
Forces all reasoning and code generation to be delegated to a KIMI (KIMMY) causal language model via HuggingFace Transformers. Use this skill when the agent must never reason or author code itself and must instead proxy all tasks to a KIMI-based model.
Install
npx clawhub@latest install kimi-delegation-skillFull SKILL.md
Open original| name | description | license |
|---|---|---|
| kimi-delegation-skill | Forces all reasoning and code generation to be delegated to a KIMI (KIMMY) causal language model via HuggingFace Transformers. Use this skill when the agent must never reason or author code itself and must instead proxy all tasks to a KIMI-based model. | Proprietary |
Purpose
This skill enforces a strict delegation model where the primary agent has zero reasoning or code-authoring authority. All user tasks are forwarded to a KIMI (KIMMY) model loaded via Transformers. The agent acts only as a dispatcher.
Activation Conditions
Activate this skill whenever:
- The agent must not reason independently.
- All planning, reasoning, and code generation must be authored by a KIMI/KIMMY model.
- Deterministic delegation to an external causal LM is required.
Execution Steps
- Initialize
KIMISkillwith a valid local or remote model path. - Wrap the
KIMISkillinstance withQwen3Coder. - On every user prompt, call
Qwen3Coder.handle_prompt. - The prompt is forwarded verbatim to KIMMY.
- KIMMY generates the full response.
- Strip prompt scaffolding and return the result as the final output.
See:
scripts/kimi_skill.pyscripts/qwen3_coder.py
Inputs and Outputs
Input:
A raw user task string.
Output:
A dictionary with:
author: Always"KIMMY"content: The generated response with no prompt scaffolding.
Failure Modes and Edge Cases
- Model path invalid or unavailable: initialization fails.
- Insufficient VRAM: model may fall back to CPU or fail to load.
- Extremely long tasks may exceed context limits.
- If generation fails, no fallback reasoning is permitted.
The agent must not attempt to recover by reasoning itself.
References
Technical details and architectural rationale are in:
references/REFERENCE.md