cgh-classify

Human-trainable confidentiality classification for cgh. You label a few files, a lightweight local model (TF-IDF + naive Bayes, standard library only) generalizes to the rest, and the egress gate and guard enforce the result. Nothing ever leaves the machine.

pip install cgh-classify
cgh classify label payroll.xlsx            # mark confidential
cgh classify label README.md --not         # mark public
cgh classify train                         # fit + sweep the repo
cgh classify review                        # files the model is unsure about
cgh findings --key confidential

How labels and predictions interact

Source	Finding written	Effect on the egress gate
Human label, confidential	`confidential = true` (block)	blocked everywhere
Human label, public	`confidential = false`	allowlisted, including strict mode
Model prediction, confidential	`confidential = true` (block)	blocked everywhere
Model prediction, public	`confidential.predicted = false`	no effect

The asymmetry is deliberate: a model may block on its own say-so (worst case, a false positive costs a summary), but only a human label can clear a file in mode = "secure", where the gate is an allowlist.

Configuration

[plugin.classify]
# threshold = 0.7      # predict confidential above this probability
# uncertain_low = 0.35 # review window lower bound
# uncertain_high = 0.65

Your labels are the asset: they live in .codegraph/classify_labels.json, the trained model in .codegraph/classify_model.json, both machine-local and cheap to retrain (cgh classify train is instant on thousands of files).

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