honor-audit

name: honor-audit description: Audit a draft KTH submission for honor-code risk — copied content, missing citations, undisclosed AI use, group-attribution gaps, attendance-record issues.

A pre-submission read-through that flags concrete risks against Rules 1, 2, 4, and 5. Output is a punch list, not a verdict.

This skill does not detect plagiarism algorithmically. It surfaces patterns a careful human reader would notice and asks the author about them.

When to use

After the submission is drafted but before it is submitted.
After honor-disclose is filled in — the audit cross-checks the disclosure against the artefact.

Checks

Rule 4 — no copying

Passages with a stylistic shift from the surrounding text (different vocabulary range, sentence rhythm, formality).
Code blocks whose style differs from the rest of the file (naming, error handling, comment density).
Suspiciously polished sections that contradict the author's stated skill level or earlier drafts.
Verbatim or near-verbatim matches to sources cited without quotation marks.

For each hit: ask the author where it came from. If it is borrowed, require quotation/citation or a rewrite in the author's own words plus understanding (honor-defense-prep).

Rule 2 — disclosure completeness

Cross-check the disclosure block against the artefact:

Any AI or source contribution visible in the artefact but absent from the disclosure → flag.
Any disclosure entry with Understood? = no or partial → flag and route to defense prep.
Conversation history that suggests help received but not disclosed → flag (with the specific evidence).

Rule 1 — group accountability

For group submissions:

Is every member named?
Can every member defend every part? (Not "we divided the work" — Rule 1 makes accountability collective.)
Are there sections only one member touched and the others have not read?

Rule 5 — attendance integrity

If the submission interacts with attendance records (lab sign-offs, seminar tickets):

Are all listed attendees actually attended?
Are there signatures or sign-offs for people who were not present?

Cross-cutting

"AI-flavour" markers (em-dashes, hedging phrases, plausible-but-wrong citations) in submissions where AI use is undisclosed → flag.
Citations that the author cannot locate or summarise → flag (likely hallucinated).
Numbers in the text that disagree with numbers in tables/figures → flag (often a sign of partial rewrites by mixed authors/tools).

Output

A bulleted list, ordered by severity:

## Audit findings — <submission>

### Must fix before submission
- <issue> — <location> — <recommended action>

### Should resolve
- ...

### Worth a second look
- ...

### Clean
- <areas checked and found in order>

Then: explicitly ask the author to address each must fix item. Do not produce a "cleaned" version of the submission — the author must do that themselves (Rule 3).