decontamination-check

name	decontamination-check
description	Detect training-eval overlap against benchmark sets before dataset publication
namespace	training-complete
category	quality
platforms	["claude","copilot","cursor","factory","windsurf","warp","codex","opencode","openclaw","hermes"]
commandHint	{"argumentHint":"<dataset-version> [--targets <file>] [--mode <exact-ngram|fuzzy|semantic>] [--threshold <n>]"}

decontamination-check

Detect overlap between training examples and benchmark evaluation sets (MMLU, GSM8K, HumanEval, HELM, MT-Bench, AlpacaEval) before a dataset version is published. Produces a per-target overlap report and feeds the decontamination-gate lint rule.

Per ADR-022 D8, decontamination is a first-class pipeline stage — not an optional post-hoc check. Eval execution (running benchmarks against trained models) is out of scope for this skill; that work is delegated to the separate matric-eval project (see #849 and docs/matric-eval-integration.md).

When to Use

• As a publication gate before dataset-version seals a new dataset release
• On-demand against an already-published dataset when a new benchmark is added to the target set
• After any ingest operation that pulled from a source suspected to overlap with public eval sets (web scrapes, synthetic pipelines seeded on public prompts)
• When migrating an old dataset into the pipeline to verify cleanliness

Parameters

<dataset-version> (required)

Either a dataset version ID (e.g., v2026.4) or a filesystem path to a directory of example records.

--targets <file> (optional)

Path to a decontamination-targets.yaml file. Default: training-complete/schemas/decontamination-targets.yaml — ships with the 6 default targets below. User-declared targets are unioned with defaults (not replaced) unless the config explicitly sets override_defaults: true.

--mode <exact-ngram|fuzzy|semantic> (optional)

Detection strategy. Default: exact-ngram.

• exact-ngram — hash-based n-gram overlap with configurable N. Fast and deterministic. Recommended default.
• fuzzy — edit-distance-based overlap (Levenshtein ratio). Catches near-duplicates with minor paraphrasing, whitespace drift, or typo variants.
• semantic — embedding cosine similarity threshold (default ≥ 0.95). Catches deeper paraphrases and translation variants. Requires an embedding model; slowest mode.

--threshold <n> (optional)

Maximum acceptable overlap count per target. Default: 0 (zero tolerance). Override per-target in the targets config.

--ngram-size <n> (optional)

N-gram size for exact-ngram mode. Default: 13 per REF-442 convention.

--report <path> (optional)

Output report path. Default: .aiwg/training/reports/decontamination-<version>.md.

Operation

1. Load targets — parse the targets config, union user-declared entries with shipped defaults (or replace if override_defaults: true).
2. Index candidate examples — load all examples at <dataset-version> via the memory-ingest consumer interface; normalize whitespace, casing per target config.
3. Run detection per target — for each target entry, execute the requested mode(s) against the target's eval set (resolved from eval_set_path).
4. Collect overlap counts — record matched example IDs, matched target items, and a representative excerpt for each match.
5. Compare against thresholds — per-target threshold takes precedence; falls back to --threshold.
6. Emit report — render templates/decontamination-report.md with per-target pass/fail and top-10 overlap samples.
7. Log event — append a decontamination-check event to .aiwg/activity.log via memory-log-append. The lint rule (#843) consumes this event.

Default Target Set

Ships in schemas/decontamination-targets.yaml:

ID	Name	Source
MMLU	Massive Multitask Language Understanding	Hendrycks et al. 2021
GSM8K	Grade School Math 8K	Cobbe et al. 2021
HumanEval	Code synthesis benchmark	Chen et al. 2021
HELM	Holistic Evaluation of Language Models	Liang et al. 2022
MT-Bench	Multi-turn chat benchmark	Zheng et al. 2023
AlpacaEval	Instruction-following leaderboard	REF-450

User-declared targets are unioned into this set. Eval set data itself is NOT shipped — each target's eval_set_path points at a HuggingFace dataset identifier or local path that the operator must provision.

Eval Execution — Delegated

This skill detects contamination only. Running benchmark evaluations against a trained model is delegated to the separate matric-eval project (#849). See docs/matric-eval-integration.md for the integration contract. The two projects share the same target config schema to keep "what we checked for leakage" and "what we evaluate against" in lockstep.

Output

.aiwg/training/reports/decontamination-<version>.md rendered from templates/decontamination-report.md, containing:

• Summary table (target × overlap count × threshold × passed/failed)
• Per-target detail with top-10 overlap samples
• Recommendation section
• Reproducibility block (mode, ngram-size, embedding model, random seed)

Acceptance Criteria

• Detects all 10 intentionally-leaked examples in the test fixture (test/fixtures/decontamination/seeded-overlap.jsonl)
• Completes a 10K-example scan in under 5 minutes on default hardware (exact-ngram mode, 13-gram)
• Report overlap counts match fixture ground truth exactly

Integration

• decontamination-gate (#843) — lint rule reads the decontamination-check log event and blocks dataset-version publication on failure
• dataset-version (#844) — records decontamination report hash in the dataset manifest for provenance
• matric-eval (#849) — shares target config schema; eval execution is out of scope here

Examples

# Default: check v2026.4 against shipped target set
decontamination-check v2026.4

# Fuzzy mode with custom threshold
decontamination-check v2026.4 --mode fuzzy --threshold 2

# Semantic mode with user-extended targets
decontamination-check v2026.4 --mode semantic --targets config/my-targets.yaml

# On-demand check of a directory of examples
decontamination-check examples/candidate-batch/ --ngram-size 13

Schema Reference

• @agentic/code/frameworks/training-complete/schemas/decontamination-targets.yaml — target set schema and defaults
• @agentic/code/frameworks/training-complete/schemas/example-record.yaml — candidate example record format
• @agentic/code/frameworks/training-complete/templates/decontamination-report.md — output report template

Delegation

• Eval execution: matric-eval project (#849)
• Kernel lint wiring: @agentic/code/addons/semantic-memory/skills/memory-lint/SKILL.md
• Log event append: @agentic/code/addons/semantic-memory/skills/memory-log-append/SKILL.md
• Authorization on remediation: @agentic/code/addons/aiwg-utils/rules/human-authorization.md

References

• REF-442 Benchmark Contamination — n-gram methodology, 13-gram default
• REF-449 LM Evaluation Harness — target set conventions
• REF-450 AlpacaEval — instruction-following leaderboard
• ADR-022 D8 — decontamination as first-class pipeline stage
• Issue #842 — this skill
• Issue #843 — decontamination-gate lint rule
• Issue #849 — matric-eval integration