pantheon-ai/acceptance-criteria
Write clear, testable acceptance criteria for user stories and feature delivery; use when defining done conditions, creating measurable requirements, applying INVEST checks, documenting negative scenarios, and aligning product, engineering, and QA on expected outcomes.
91
100%
Does it follow best practices?
Impact
91%
1.16xAverage score across 10 eval scenarios
Passed
No known issues
Evaluation results
84%
11%E-commerce Checkout Implementation
Checkout Flow Workflow
Given/When/Then format
73%
53%
Measurable timing
100%
100%
Observable outcomes
70%
80%
Negative scenarios
100%
100%
Single action When
80%
80%
Essential Given setup
70%
100%
User behavior scenarios
20%
60%
Out of scope section
100%
100%
Checkbox format
0%
100%
User outcome focus
60%
80%
98%
25%User Registration System
API Rule Validation
Rule-oriented format
60%
100%
Specific response codes
100%
100%
Form validation rules
100%
100%
Error handling details
91%
100%
Checkbox format usage
0%
100%
Negative scenarios
100%
100%
Measurable constraints
90%
100%
Independent requirements
75%
100%
User outcome focus
80%
100%
Stakeholder alignment
0%
60%
Scope boundaries
0%
100%
94%
13%Analytics Dashboard Performance
Performance Measurements
Specific timing requirements
100%
100%
Measurable quantities
100%
100%
Observable UI behavior
93%
93%
Rule-oriented format
50%
90%
Negative performance cases
100%
100%
Avoids vague terms
60%
70%
User outcome focus
87%
87%
Checkbox validation format
0%
100%
Multiple measurement types
100%
100%
Scope definition
50%
100%
88%
-2%User Onboarding Experience
INVEST Criteria Validation
INVEST validation applied
100%
100%
Given/When/Then sequences
100%
60%
Stakeholder alignment mention
100%
100%
Sequential step validation
80%
70%
Testable binary criteria
90%
90%
User behavior scenarios
62%
75%
Business value emphasis
100%
100%
Estimable clarity
87%
100%
Single iteration scope
50%
100%
Implementation flexibility
100%
100%
92%
18%Product Search Enhancement
Search Edge Cases
Rule-oriented format
83%
75%
Comprehensive edge cases
86%
93%
Input validation rules
91%
83%
Measurable result limits
33%
91%
Performance timing
100%
100%
Error handling scenarios
90%
100%
Result formatting rules
80%
100%
Checkbox validation format
0%
100%
Independent requirements
83%
83%
User outcome focus
66%
100%
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6