mcpField
C
constraint-specification

constraint-specification

github.com/owl-listenerAgent workflows191376 installsUpdated 20w ago
Repository

About constraint-specification

An agent skill for teams specifying constraints on AI outputs, including format, length, content, tone, and quality. It also covers prioritization, conflict resolution, and boundary testing to make outputs more predictable.

Key features

  1. 01Classifies format, length, and content constraints
  2. 02Specifies tone and quality requirements
  3. 03Sets constraint priorities
  4. 04Resolves conflicts between constraints
  5. 05Designs boundary test cases

Use cases

  1. 01Write requirements for structured outputs
  2. 02Set priorities among prompt requirements
  3. 03Test boundary cases for output constraints

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/owl-listener/ai-design-skills --skill constraint-specification

After installing, type/constraint-specificationin Claude Code to confirm it loaded.

Details

From the project README

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)

Comments

Markdown supported