Recognises unstable requirement identifiers
Missing or malformed IDs, renumbering, or requirements deleted instead of retired. A signed-off document elsewhere may already cite the ID.
Can AI do this?
AI does this well Most of the tested AI models master this. 7 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 89% | Not shown |
| gemini-3.1-pro-preview | Flagship | 90% | Mastered |
| gpt-6-astra | Flagship | 91% | Mastered |
| claude-fable-5-1 | Flagship | 100% | Mastered |
| gemini-3.8-flash | Mid tier | 100% | Mastered |
| gpt-6.1-sol | Mid tier | 90% | Not shown |
| claude-sonnet-5-5 | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 0% | Not mastered |
| gpt-6-luna | Low cost | 91% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The missing, malformed, renumbered or deleted identifiers
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: DR-12; spreadsheet rows Overdraft limit and Interest rate type with no requirement number Acceptable: DR-012; every source row given an ID; obsolete requirements set to retired with superseded_by, never removed.
Also known as: Requirement identity not stable (data requirement)
Related knobits
Builds on
All 47 knobits of Capture data requirements.
Source: Review checklist 01-requirements, row 34; 29148 5.2.8 (identification); REQ-001, REQ-003, REQ-016