Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-data-contaminationgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-data-contamination)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-data-contamination"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-data-contamination/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-data-contamination"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-data-contamination.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.00291 |
| Opus 5.5 | $0.00010 | $0.00116 |
| Sonnet 5.5 | $0.00005 | $0.00058 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
audit-data-contamination scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
audit-data-contamination
Purpose
Audit train/test leakage, memorization artifacts, temporal leakage, and disclosure pathways that can invalidate evaluation.
Input contract
required: [dataset_partitions, training_sources, evaluation_records]
optional: [release_history, deduplication_keys, access_logs]
constraints: [each contamination claim requires a matched record, pathway, and comparison basis]
Procedure
- Define partition, temporal, entity, and disclosure boundaries.
- Match evaluation items against training and public-source records using declared keys.
- Test suspected leakage pathways and compare affected with unaffected cases.
- Classify confirmed, plausible, and unassessed contamination with impact notes.
Output contract
produces: [contamination_matches, pathway_map, affected_case_set, impact_assessment, audit_uncertainties]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
Quality gates
- Matching keys and time boundaries are recorded.
- False-positive matches are sampled and documented.
Failure and counterexamples
Do not call lexical overlap contamination without a disclosure pathway, and do not infer clean separation from missing metadata.
Provenance map
resolved: knowledge-acquisition-contamination-audit
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 47 lines · 25 tokens per session scan A 5f6a3c2149cb
audit-data-contamination is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed 4d ago), licensed Apache-2.0. It adds 25 tokens to every session and 291 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-24.
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