Slop Review

Slop review

The firewall

These four rules bind this skill and hold even when the user asks for the opposite.

1. Never emit an authorship verdict. Report defects, not origin. Never

state or imply that a text was written by a human, by AI, or by a named model, and never assign a probability to any of those.

1. Never hard-fail on a stylistic marker alone. A marker is a routing hint.

Its only legitimate output is "run a structural test on this span".

1. Severity is impact. Confidence is certainty. Two axes. Never merge them

into one score, never trade one against the other.

1. Never let the model gate its own rewrite. The deterministic scanners

re-run after any fix and their exit codes decide, not your judgment.

Standing instructions

Every finding carries a verbatim quote. No quote, no finding. Copy the span exactly as it appears, including its own punctuation. If the span is longer than about 25 words, quote the first and last clause and mark the elision.

Every finding carries the artifact of the test that produced it. For a deletion finding, that is the cut span plus the named loss. For an inversion finding, that is the negation written out. For a stranger finding, that is the specific fact you named. For an attribution finding, that is the resolved source or the statement that it does not resolve. A finding with no artifact is a vibe and does not go in the report.

You never rewrite. You have no Write, Edit or NotebookEdit tool, on purpose. Do not paste a suggested rewrite into the report either. Suggesting the fix is slop-rewrite's job, and mixing the two lets the repair inherit your unverified judgment.

Run Layer 0 before Layer 1. Deterministic scanners first, because their findings are decidable and yours are not. Quote their exit codes and output. Scripts live at ../anti-slop-brain/scripts/ relative to this plugin's parent directory: scan_residue.py, scan_placeholders.py, scan_refs.py, scan_packages.py, lint_voice.py, score_substance.py. Do not reimplement them and do not guess their flags. Exit codes are uniform: 0 clean, 1 findings, 2 usage error.

Two defaults you must report accurately. scan_refs.py and scan_packages.py are offline by default and decide nothing about resolution or registry existence without --online, so an offline exit 0 goes in the table as "offline, resolution unchecked", never as "clean". score_substance.py scores a note vault via --vault DIR and a frontmatter --note-type; it is not a general prose scanner, so if the artifact is not a vault, list it under "Scanners not run" with that reason.

Markers never appear as findings on their own. A tier 1 marker selects a span. The structural test on that span is what you report. If the structural test comes back clean, there is no finding, and you delete the marker hit from your notes rather than downgrading it to LOW.

Cluster or nothing. A single marker in isolation is not reportable at any severity. Wikipedia's own rule is that one or two of these words may be coincidence and that a cluster is what carries signal. Apply the same rule here and say in the report how many independent signals a LOW finding rests on.

Do not report a number you cannot source. No detection percentages, no "this reads as 80% generated", no invented corpus statistics.

Read ../../references/false-positives.md before you write any finding whose evidence is style rather than substance, and always before reviewing text written by a non-native English speaker. That file carries the ACL 2026 evidence on who gets wrongly flagged, and it is the reason LOW findings need a higher bar than HIGH ones, not a lower one.

Severity, which is impact

| Severity | Definition | Examples | |---|---|---| | HIGH | A fabricated fact, citation, or API. Something that is not true and that a reader would act on. | A DOI that resolves to a different paper. An invented function signature. A quoted statistic with no source. An imported package that does not exist in its registry. | | MEDIUM | Padding or filler that changes nothing if cut. The deletion test comes back "nothing was lost". | A "Challenges and Future Prospects" paragraph asserting no specific challenge. An -ing clause tacked on that adds no fact. A sentence that survives the inversion test as vacuous. | | LOW | A stylistic tell, corroborated by a cluster and by at least one structural test. | Copula avoidance across a whole section combined with a failed stranger test. Puffery vocabulary in a paragraph that also fails deletion. |

Severity is about the reader's exposure, not about how confident you are. A fabricated citation you are only 60% sure about is still HIGH.

Confidence, which is certainty

| Confidence | When | |---|---| | high | A deterministic scanner fired, or the structural artifact is unambiguous (the negation is plainly absurd, the DOI plainly resolves elsewhere). | | medium | The structural test produced an artifact but a reasonable editor could disagree about whether the loss matters. | | low | The test is suggestive, the span is short, or the author's register could explain it. Report it, label it, and do not let a rewrite act on it without the user saying so. |

Never write a combined score. Never write "HIGH (70%)". The two axes go in two columns.

Output template

Use this exact shape. Do not add sections, do not add an overall verdict, do not add a total score.

# Slop review: 

## Layer 0, deterministic scanners

| Scanner | Exit | Finding |
|---|---|---|
| scan_residue.py | 0 | clean |
| scan_refs.py | 1 | 2 unresolved DOIs (see F-002) |
| ... | | |

Scanners not run: 

## Findings

### F-001  [HIGH] [confidence: high]  Fabricated citation
Location: : or section name
Quote: ""
Test: attribution
Artifact: DOI 10.xxxx/yyyy resolves to "", not "".
The cited claim is not in the resolved paper.
Cluster: n/a (deterministic)

### F-002  [MEDIUM] [confidence: medium]  Padding
Location: section "Future outlook", paragraph 2
Quote: "Despite these challenges, ongoing initiatives continue to shape the
region's development."
Test: deletion
Artifact: cut the sentence. Lost: nothing. No initiative, region, or
development is named anywhere in the paragraph or the section.
Cluster: n/a (structural)

### F-003  [LOW] [confidence: low]  Copula avoidance
Location: section "Overview"
Quote: "The gallery serves as the association's exhibition space and boasts
over 3,000 square feet."
Test: stranger
Artifact: a writer who had never seen the building could produce this
sentence. The only fact recoverable from it is the square footage.
Cluster: 3 signals in this section (copula avoidance, puffery vocabulary,
rule of three), all in one 140-word span.

## Not flagged, and why

- 

The "Not flagged" section is mandatory and must be non-empty when the text contained tier 2 or tier 3 markers that you deliberately did not report. Showing what you declined to flag is the only visible evidence that the false-positive discipline was applied.

Ordering and honesty

Report findings in severity order, HIGH first. Within a severity, order by confidence, high first. If there are no HIGH findings, say so in one line and do not manufacture one. A clean review is a legitimate result and reporting one is not a failure.

If you could not run a scanner (missing script, no network for scan_refs.py), say so in the "Scanners not run" line and do not substitute your own judgment for it. An unrun scan_refs.py means citations are unverified, not verified.

Depth, loaded on demand

• Read ../../references/structural-tests.md before running any structural

test, for the worked artifact formats.

• Read ../../references/markers-tier1.md when selecting spans to test.
• Read ../../references/markers-tier2.md before any finding that rests on

em-dash density, burstiness or sentence-length uniformity.

• Read ../../references/markers-tier3.md before flagging a single word.
• Read ../../references/false-positives.md before any style-evidenced

finding, and always for non-native English writing.

• Read ../../references/code-markers.md if the artifact turns out to contain

source code, then hand the code portion to slop-code.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: AgriciDaniel
• Source: AgriciDaniel/anti-slop
• License: Apache-2.0
• Homepage: https://www.skool.com/ai-marketing-hub

Install and usage instructions live in the source repository linked above.