PromptMarketing
Find your landing page message
Connect a product’s value to a specific audience and write a clear first-screen message.
✳Claude◎ChatGPT✦Gemini
By Dave AIDave AI original
MIT · FreeView prompt
Installable skillMIT
Constraint Specification
Defining output format, length, tone, and content boundaries within prompts.
instructions.md
---
name: constraint-specification
description: Defining output format, length, tone, and content boundaries within prompts.
---
# Constraint Specification
Constraints are the rules that shape AI output — what format to use, how long to be, what to include, what to exclude. Well-specified constraints produce predictable, useful outputs. Vague constraints produce inconsistent results.
## Types of Constraints
**Format constraints:**
- Output structure (JSON, markdown, plain text, bullet points, prose)
- Section headings and organisation
- Required fields and optional fields
- Data types and schemas
**Length constraints:**
- Word count ranges (not exact numbers — models are bad at counting)
- Section length proportions ("spend 60% on analysis, 40% on recommendations")
- Minimum and maximum bounds
- Conciseness directives ("be brief" vs. "be thorough")
**Content constraints:**
- Topics to include and exclude
- Required information elements
- Prohibited content
- Source restrictions (only use provided context, don't use external knowledge)
**Tone constraints:**
- Formality level
- Emotional register
- Audience-appropriate language
- Voice and style guidelines
**Quality constraints:**
- Accuracy requirements ("cite sources", "flag uncertainty")
- Completeness requirements ("address all aspects of the question")
- Originality requirements ("don't repeat the question back")
- Actionability requirements ("every recommendation must be implementable")
## Writing Effective Constraints
- **Be specific**: "Keep responses under 200 words" beats "be concise"
- **Prioritise**: When constraints conflict, state which wins. "Accuracy over brevity."
- **Provide examples**: Show what a constrained output looks like
- **Test boundaries**: What happens at the edge of each constraint?
- **Separate hard and soft constraints**: Hard constraints must always be met. Soft constraints are preferences.
## Constraint Interactions
Constraints interact and can conflict:
- "Be thorough" vs. "Keep it under 100 words"
- "Be creative" vs. "Follow this exact format"
- "Be helpful" vs. "Don't give medical advice"
Resolve conflicts explicitly in the prompt. Don't make the model guess which constraint takes priority.
## Design Artefacts
- Constraint specification documents per output type
- Constraint priority hierarchies
- Constraint test cases (inputs designed to stress each constraint)
- Constraint violation examples (what bad looks like)
- Constraint evolution logs (how constraints changed and why)
---
name: constraint-specification
description: Defining output format, length, tone, and content boundaries within prompts.
---
# Constraint Specification
Constraints are the rules that shape AI output — what format to use, how long to be, what to include, what to exclude. Well-specified constraints produce predictable, useful outputs. Vague constraints produce inconsistent results.
## Types of Constraints
**Format constraints:**
- Output structure (JSON, markdown, plain text, bullet points, prose)
- Section headings and organisation
- Required fields and optional fields
- Data types and schemas
**Length constraints:**
- Word count ranges (not exact numbers — models are bad at counting)
- Section length proportions ("spend 60% on analysis, 40% on recommendations")
- Minimum and maximum bounds
- Conciseness directives ("be brief" vs. "be thorough")
**Content constraints:**
- Topics to include and exclude
- Required information elements
- Prohibited content
- Source restrictions (only use provided context, don't use external knowledge)
**Tone constraints:**
- Formality level
- Emotional register
- Audience-appropriate language
- Voice and style guidelines
**Quality constraints:**
- Accuracy requirements ("cite sources", "flag uncertainty")
- Completeness requirements ("address all aspects of the question")
- Originality requirements ("don't repeat the question back")
- Actionability requirements ("every recommendation must be implementable")
## Writing Effective Constraints
- **Be specific**: "Keep responses under 200 words" beats "be concise"
- **Prioritise**: When constraints conflict, state which wins. "Accuracy over brevity."
- **Provide examples**: Show what a constrained output looks like
- **Test boundaries**: What happens at the edge of each constraint?
- **Separate hard and soft constraints**: Hard constraints must always be met. Soft constraints are preferences.
## Constraint Interactions
Constraints interact and can conflict:
- "Be thorough" vs. "Keep it under 100 words"
- "Be creative" vs. "Follow this exact format"
- "Be helpful" vs. "Don't give medical advice"
Resolve conflicts explicitly in the prompt. Don't make the model guess which constraint takes priority.
## Design Artefacts
- Constraint specification documents per output type
- Constraint priority hierarchies
- Constraint test cases (inputs designed to stress each constraint)
- Constraint violation examples (what bad looks like)
- Constraint evolution logs (how constraints changed and why)
COMPATIBILITY & REFERENCES
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.