# code quality is a profile

def profile(src):

feats = extract(src)

for dim in DIMENSIONS:

level = ladder(feats, dim)

yield dim, level

# infer the intended shape

shape = nearest(centroid)

gap = target - actual

return Report(levels, gap)

# the model finds, the code decides

evidence = cite_lines(src)

assert deterministic(score)

class Dimension:

name: str

rungs: list[Rung]

qualified: bool

# code quality is a profile

def profile(src):

feats = extract(src)

for dim in DIMENSIONS:

level = ladder(feats, dim)

yield dim, level

# infer the intended shape

shape = nearest(centroid)

gap = target - actual

return Report(levels, gap)

# the model finds, the code decides

evidence = cite_lines(src)

assert deterministic(score)

class Dimension:

name: str

rungs: list[Rung]

qualified: bool

# code quality is a profile

def profile(src):

feats = extract(src)

for dim in DIMENSIONS:

level = ladder(feats, dim)

yield dim, level

# infer the intended shape

shape = nearest(centroid)

gap = target - actual

return Report(levels, gap)

# the model finds, the code decides

evidence = cite_lines(src)

assert deterministic(score)

class Dimension:

name: str

rungs: list[Rung]

qualified: bool

// validated, not asserted

const report = await facet(file)

for (const d of report.dims) {

if (d.level < d.target) {

climbs.push(d)

}

}

// one judge per dimension

const judge = route(dim)

const levels = score(features)

// your source is never stored

const vec = scrub(features)

const hash = digest(src)

function coherence(sections) {

return spread(sections)

}

// a throwaway script can pass

// validated, not asserted

const report = await facet(file)

for (const d of report.dims) {

if (d.level < d.target) {

climbs.push(d)

}

}

// one judge per dimension

const judge = route(dim)

const levels = score(features)

// your source is never stored

const vec = scrub(features)

const hash = digest(src)

function coherence(sections) {

return spread(sections)

}

// a throwaway script can pass

// validated, not asserted

const report = await facet(file)

for (const d of report.dims) {

if (d.level < d.target) {

climbs.push(d)

}

}

// one judge per dimension

const judge = route(dim)

const levels = score(features)

// your source is never stored

const vec = scrub(features)

const hash = digest(src)

function coherence(sections) {

return spread(sections)

}

// a throwaway script can pass

Sample report

ingest_events.py

python · profiled 6/15/2026, 2:30:00 AM

Closest profile: P2 THROWAWAY SCRIPT

14 validated · 0 provisional

This code looks built for getting a one-off job done fast. Facet reads every dimension against that profile, not a universal ideal. The dimensions this profile prioritises (marked priority) are what “good” means here; a low level on a dimension it does not prioritise is a deliberate trade, not a fault. A throwaway script that skips tests is passing; a security boundary that skips validation is not.

D1 Runtime performance3/5validated

D2 Memory efficiency2/5validated

D3 Readability3/5validated

D4 Maintainability2/5validated

D5 Robustness1/5validated

D6 Security2/5validated⚠

D7 Portability3/5validated

D8 Prototype economypriority5/5validated

D9 Testability1/5validated

D10 Observability2/5validated

D11 Auditability1/5validated

D12 Concurrency safety2/5validated

D13 API ergonomics2/5validated

D14 Resource cost3/5validated

A model reads the code and cites the lines it relies on; deterministic scoring, not the model, sets each level, so the same code always gets the same result, and each dimension is routed to the judge that proved most reliable at measuring it. Hover a dimension for what it measures, or read the methodology for why there is no single best.

Profile your own code

This is a read-only report. Run Facet on your own file the same way: free while in beta, and your source code is never stored.

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