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Academic integrity forensics

Skill alainlebret/claude-agents/higher-ed-teaching-agents/skills/academic-integrity-forensics

Modular skill and workflow ecosystems for AI coding assistants. Structured, multi-agent pipelines for specialized professional domains.

Install
npx -y skills add alainlebret/claude-agents --skill academic-integrity-forensics

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

SKILL.md

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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.

Keep looking

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