Why We Need a Multi-Model Adapter
Different models are trained on different corpora, aligned with different methods, and use different reasoning paradigms. A prompt or skill that's optimal for Claude is rarely optimal for GPT, Gemini, DeepSeek-R1, or Kimi. Writing one prompt for all of them means only one model performs well — the others degrade in quality or output stability.
Where the Differences Come From
| Dimension | Description |
|---|---|
| Training data | Anthropic used lots of XML-styled data — Claude is highly sensitive to tag structure. OpenAI prefers Markdown instructions |
| Alignment | RLHF / DPO / Constitutional AI — each favors different prompt style (strict vs minimal) |
| Reasoning paradigm | Native reasoning models (o1 / R1 / QwQ) produce their own CoT — adding "think step by step" hurts quality |
| Context length | Kimi / Gemini have huge context → inline entire docs. Claude → progressive disclosure works better |
| Tool protocol | Anthropic tool_use / OpenAI function calling / Google function_declarations / Chinese platform DSL all differ |
| Language preference | Chinese models handle direct Chinese best; international models prefer Markdown + English |
The Cost of "One Prompt Fits All"
Common failure modes when swapping models:
- XML prompt written for Claude → dropped into GPT → quality drops, structure unstable
- "Let's think step by step" written for GPT → dropped into o1 / R1 → reasoning models get misled, waste reasoning budget
- Claude's progressive-disclosure skill → Kimi → wastes Kimi's huge context ability
- Chinese prompt unchanged → Gemini → noticeably worse than English equivalent
- Chinese workflow platforms (Coze / Bailian / Qianfan) need DAG DSL → a flat prompt cannot be deployed directly
CAUTION
"Barely runs" ≠ "performs optimally". A production-grade prompt/skill system must render differently per model.
PromptMan's Approach
PromptMan ships a "Canonical Skill Spec + Model Adapter" system:
Canonical (vendor-neutral) middle layer
Describe identity, goal, steps, inputs, output, constraints, examples, tools — without any vendor-specific syntax
Adapter layer
Every mainstream model (Claude / GPT / Gemini / Qwen / DeepSeek / Kimi / GLM / ERNIE / Doubao + reasoning variants) has a dedicated adapter that renders to its best-practice format
Unified entrypoint
renderSkill(spec, family) and a REST API — render one spec to all models in a single call
Playground
Visit /adapters to compare 12 model renderings live in the browser
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