# Academic Integrity Forensics

> Detects likely plagiarism or collusion patterns across submissions using multi-signal similarity analysis and produces evidence-first integrity reports for human review.

- Skill: `alainlebret/academic-integrity-forensics` (Agent Skill)
- Install (CLI): `npx skillmds@latest add alainlebret/academic-integrity-forensics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/alainlebret/academic-integrity-forensics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: alainlebret (https://skillmd.com/u/alainlebret)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/alainlebret/academic-integrity-forensics

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# Academic Integrity Forensics

## Goal
Identify suspicious similarity clusters across student submissions while preserving fairness, explainability, and teacher control.

## Inputs
- `mission.json`
- `rubric.md`
- `submissions/` and/or `evidence/`
- Optional logs or metadata (timestamps, commit traces, test traces)

## Produce
Write:
- `integrity-report.json` (global integrity assessment)
- `integrity-summary.csv` (per-student risk indicators)
- `integrity-cases/<case-id>.md` (cluster-level evidence sheets)

## Analysis signals
- token-level similarity after normalization
- n-gram fingerprint overlap
- control-flow keyword profile similarity
- shared rare-error patterns when available
- metadata anomalies when available

## Output semantics
`integrity-report.json` must include:
- `overall_status`: `pass` | `review` | `fail`
- `pair_findings`: suspicious pair list with scores and triggered signals
- `clusters`: grouped suspicious submissions
- `method`: scoring and thresholds used
- `policy_note`: explicit statement that no automatic grade penalty is applied

## Rules
- Never auto-penalize students based on similarity signals alone.
- Always provide evidence and alternative explanations.
- Exclude obvious boilerplate/template regions when possible.
- Require human attestation before disciplinary action.