AgentSkillsCN

prompt-craft

针对双模系统的设计提示工程技巧——模板结构、少样本设计、结构化输出提示，为小型（4B–8B）与大型（GPT-5、Claude）模型量身定制适应性策略

SKILL.md
--- frontmatter
name: prompt-craft
description: Prompt engineering techniques for dual-model systems — template structure, few-shot design, structured output prompting, model-adaptive strategies for both small (4B-8B) and large (GPT-5, Claude) models

Prompt Scaffold

code
[ROLE]        Who the model is / expertise
[CONTEXT]     Background, domain constraints
[TASK]        One clear instruction
[FORMAT]      Output structure with example
[CONSTRAINTS] Boundaries, edge cases, what to avoid
[EXAMPLES]    Few-shot demonstrations (if needed)

Claude: use XML tags. GPT: markdown headers. Small models: minimal structure, maximum explicitness.

Dual-Model Adaptation

For Small Models (4B-8B Ollama)

  • •Max 1000 tokens prompt length. Cut ruthlessly.
  • •One task per prompt. No compound instructions.
  • •Complete worked example is mandatory — the model mimics format.
  • •Every enum value listed explicitly: quality: one of "excellent", "good", "fair", "poor"
  • •No chain-of-thought unless tested (often hurts structured output).
  • •Positive instructions: "Write X" not "Don't do Y."
  • •Sandwich: format spec at start AND end.

For Large Models (GPT-5, Claude)

  • •Multi-section prompts with nuanced instructions.
  • •<thinking> tags for chain-of-thought (Claude).
  • •Few-shot optional — large models generalize from descriptions.
  • •Can handle "Don't X unless Y" conditional constraints.
  • •System prompt for persona, user prompt for task.

Few-Shot Design

  • •3-5 examples. More than 7 has diminishing returns.
  • •Include at least one edge case example.
  • •Show reasoning, not just input→output.
  • •Order: easy → medium → edge case.
  • •For small models: examples are the prompt. They define the contract.

Structured Output

  1. •Provide the exact schema with field descriptions.
  2. •Include a complete worked example (JSON, ready to copy).
  3. •Specify handling for missing/ambiguous fields.
  4. •For enums, list ALL valid values in the prompt.
  5. •Show good AND bad examples for critical fields:
code
## Dilemma ID Naming (CRITICAL)
GOOD: `host_benevolent_or_self_serving`
BAD: `host_motivation`

Defensive Patterns

  • •Sandwich: Repeat critical instructions at start and end.
  • •Validate → Feedback → Repair: Validate output, format structured errors, ask model to fix.
  • •Discuss → Freeze → Serialize: Separate exploration from structured output generation.
  • •Anti-pleasantry: "Do NOT end with 'Good luck!' or similar."

Systematic Testing

  1. •Define success criteria before iterating.
  2. •Test set of 10-20 diverse inputs including edge cases.
  3. •Change one thing at a time.
  4. •Track: prompt version, model, temperature, pass rate, failure modes.
  5. •Failure taxonomy: wrong format, hallucination, refusal, partial output, off-topic.