neosloc

sloccount counted lines of code and fed them to COCOMO to estimate the effort to build software. With LLMs writing code, lines are cheap and that estimate has lost its meaning. What is still expensive is everything around the code: how easily other systems and agents can call it, run it, rely on it and change it safely, and the knowledge that exists only in the code and its history.

neosloc measures those things.

pip install neosloc
neosloc path/to/repo

What you get

Section	Question it answers	Page
Ten dimensions, levels 0–4	How integrable is this, and what exactly is missing?	Dimensions
Retrofit effort	How much work to make it solidly integrable?	Retrofit effort
neoCOCOMO value	What is it worth when an agent can re-type it?	Value
Make or buy	Is rebuilding it with agents cheaper than adopting it? Tokens, time and money.	Make or buy
Probe / judge / review	What happens when a model actually tries to integrate with it? Do models agree with the detectors?	LLM evaluators

The archive runs neosloc on public repositories every week and keeps every result.

Every level comes with evidence (the files that justify it) and gaps (what to do next), so the report can be used as a to-do list, not just a score.

Interface surface   ■■■□  3 solid      The framework generates a contract from code, so it tracks the implementation.
                      + routes:fastapi/flask: 15 route declarations in 3 files (app/routes/admin.py)
                      + generated-spec: FastAPI (auto OpenAPI) (app/main.py)
                      - Check the generated spec into the repo so changes are reviewable and diffable.
Contract stability  ■■□□  2 partial    Releases are versioned; the API itself has limited change management.
...
Integrability index (0-4, assessed dimensions):  1.80
Retrofit effort to level 3 (agent-assisted):     7.5 person-days

Value (neoCOCOMO, cost approach; see neosloc/value.py)
  Classic COCOMO (sloccount):    2.1 KSLOC -> 5.1 person-months, 4.7 months, $58k
  Behaviour captured:            65% (tests 0.42, contract 0.75, docs 1.00)
  ...
  Knowledge at risk:             4% of replacement cost lives only in code and history

Design principles

• No dependencies for the core. The static analysis is standard library only and runs on Python 3.8+, so it works on old servers and in CI without setup.
• Evidence over scores. There is no single headline number. The mean of the levels is printed next to them, never instead of them.
• Deterministic first, models second. The static report is reproducible. LLM evaluators are opt-in, budgeted, and reported separately, so you can see where they disagree with the detectors.
• Everything is a guess until calibrated. Thresholds and coefficients are module constants with their rationale in the code. See Calibration.