acquaint.deslop

The deterministic half of deslop: find the patterns that make prose read as machine-written, scaled to the reader.

How strict the check is depends on the recipient’s tolerance of AI-sounding text, recorded as ai_tolerance in their style.md (tolerant, neutral, averse; anything else counts as unknown, which is neutral):

• tolerant enforces tier E only, with looser counts;

• neutral enforces E and W;

• averse enforces E, W and S, with the tightest counts.

Findings outside the enforced tiers are still reported, marked enforced: False. The tells catalogue itself lives in ductus (the read-side package that gauges how machine-written a text reads); this module reads it from there and layers the reader calibration on top, so the two halves cannot drift apart. It is still a keyword argument, so a list derived from the operator’s own writing replaces it without code changes.

>>> result = lint_text("Great question! This robust tool serves as a bridge.", tolerance="neutral")
>>> sorted({f["rule"] for f in result["findings"] if f["enforced"]})
['ai-vocabulary', 'chat-leftover', 'copula-avoidance']
>>> lint_text("Sending the export on Friday. Two sites, not five.")["ok"]
True

Functions

shipped_catalogue()	The default catalogue: ductus's tells and thresholds, this package's calibration.
lint_text(text, *[, tolerance, blocklist, ...])	Check a draft against the tells catalogue at a reader's tolerance: {"ok", "findings", "metrics", "relational"}.
normalize_tolerance(value)	A recorded ai_tolerance as one of TOLERANCES, with a warning when it was something else.
recipient_card(entity)	What the check needs from a recipient's writing card (style.md): tolerance, disclosure, blocklist, warnings.
text_metrics(text)	Counts the checks use: words, sentences, sentence-length variation, em-dash rate, headers, bold.

acquaint.deslop.lint_text(text, *, tolerance='neutral', blocklist=(), catalog=None)

Check a draft against the tells catalogue at a reader’s tolerance: {"ok", "findings", "metrics", "relational"}.

blocklist holds phrases this recipient’s card says never to use; each hit is tier E. catalog replaces the shipped catalogue (same schema as tells.yaml). An unrecognised tolerance is an error here; normalise recorded values first with normalize_tolerance().

Return type:

dict[str, Any]

acquaint.deslop.normalize_tolerance(value)

A recorded ai_tolerance as one of TOLERANCES, with a warning when it was something else.

Return type:

tuple[str, str | None]

>>> normalize_tolerance("Averse"), normalize_tolerance(None)
(('averse', None), ('unknown', None))
>>> normalize_tolerance("low")[0]
'unknown'

acquaint.deslop.recipient_card(entity)

What the check needs from a recipient’s writing card (style.md): tolerance, disclosure, blocklist, warnings.

ai_tolerance and disclosure come from the card’s frontmatter. Blocklist phrases are the items of its ## Blocklist section: the quoted phrase when an item quotes one, else the item without its source tag.

Return type:

dict[str, Any]

acquaint.deslop.shipped_catalogue()

The default catalogue: ductus’s tells and thresholds, this package’s calibration.

ductus owns the patterns and the shared rhythm thresholds – it is the read side, and a catalogue that disagreed with the one used to find machine writing would be worse than useless. What this package adds is the part ductus deliberately lacks: who tolerates what.

Return type:

dict[str, Any]

>>> catalogue = shipped_catalogue()
>>> sorted(catalogue)
['metrics', 'relational', 'rules', 'tolerance']
>>> len(catalogue["rules"]) > 10 and "averse" in catalogue["tolerance"]
True

acquaint.deslop.text_metrics(text)

Counts the checks use: words, sentences, sentence-length variation, em-dash rate, headers, bold.

Return type:

dict[str, float]

>>> text_metrics("One two three. Four five!")["sentences"]
2