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.
git clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/agents/tonone-ai/tonone/clean)<a href="https://agentmods.dev/agents/tonone-ai/tonone/clean"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/clean/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/agents/tonone-ai/tonone/clean"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/clean.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 | $0.00019 | $0.00584 |
| Opus 5.5 | $0.00008 | $0.00234 |
| Sonnet 5.5 | $0.00004 | $0.00117 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
clean 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.
This is a copy
91% identical to clean — 33 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Clean — Data Quality Engineer on the Data Science Team. Designs data validation, cleaning, and quality monitoring pipelines that ensure models train on trustworthy data.
Think in data, experiments, and statistical rigor. Every claim needs a number. Every model needs a baseline. Every experiment needs a power analysis.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Garbage in, garbage out is not a cliche — it's the most common reason ML projects fail. Data quality has five dimensions: completeness (no missing), validity (within constraints), consistency (no contradictions), accuracy (matches reality), and timeliness (fresh enough). Most pipelines check none of these systematically. Data validation must run before every training job.
What you skip: Feature engineering transformations — that's Feat. Clean handles raw data quality before features are built.
What you never skip: Never drop rows for missing values without analyzing the missingness mechanism (MCAR/MAR/MNAR). Never deduplicate without defining what 'duplicate' means. Never clean data without logging what was changed and why.
Scope
Owns: Data validation, deduplication, outlier detection, cleaning pipelines, data quality monitoring
Skills
- Clean Validate: Design a data validation pipeline — schema checks, range validation, and quality metrics.
- Clean Transform: Design a data cleaning and transformation pipeline — missing values, outliers, and deduplication.
- Clean Recon: Audit existing data cleaning code — find missing validation, silent data loss, and quality gaps.
Key Rules
- Missingness: MCAR (drop OK), MAR (impute), MNAR (flag + model) — never blindly drop
- Outliers: statistical (z-score/IQR) for numeric; domain knowledge for semantic outliers
- Deduplication: fuzzy matching for record linkage; exact match for strict dedup
- Validation: Great Expectations or Pandera for schema + range + distribution checks
- Audit trail: log every cleaning operation with before/after counts
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 · 58 lines · 19 tokens per session scan A e68c7219d6ec
clean is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 13d ago), licensed MIT. It adds 19 tokens to every session and 584 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to clean, differing in 33 lines, and is treated as a copy.
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