cocomo_estimate
Read-onlyIdempotent
Estimate software project effort in person-months using COCOMO II adapted for LLM-specific factors like reasoning complexity and human oversight.
Instructions
LLM-adapted COCOMO II parametric effort estimation.
Replaces traditional 17 human-labor cost drivers with 5 LLM-specific factors: reasoning complexity, context completeness, transformation impact, iterative cycles, and human oversight. Returns both nominal and LLM-adjusted person-months.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| kloc | Yes | Estimated thousands of lines of code. Count actual code, not comments/blank lines. | |
| reasoning_complexity | No | Multiplier for reasoning complexity of the codebase. 0.5 = trivial CRUD, 1.0 = average, 2.0 = novel algorithm/R&D. | |
| context_completeness | No | How complete is the context provided to the LLM? 0.5 = exhaustive specs, 1.0 = typical, 2.0 = vague requirements. | |
| transformation_impact | No | Scale of transformation relative to existing code. 0.5 = small patch, 1.0 = new module, 2.0 = architectural rewrite. | |
| iterative_cycles | No | Iteration overhead multiplier or literal cycle count. Multiplier scale: 0.5 = one-shot, 1.0 = typical debug loop, 2.0 = heavy back-and-forth. Values above 2.0 are accepted as literal cycle counts and normalized internally. | |
| human_oversight | No | Human review overhead multiplier. 0.5 = auto-merged, 1.0 = standard PR review, 2.0 = compliance/security review. | |
| task_type | No | Optional task type for feedback matching. | |
| ai_native | No | Degree of AI assistance: 0.0 = fully human, 1.0 = fully AI-native, 0.5 = hybrid. Accepts boolean for backward compatibility (true=1.0, false=0.0). |