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 aiopshwang/data-analysis-ml-agent-skills --skill auditing-data-and-ground-truthgit clone --depth 1 https://github.com/aiopshwang/data-analysis-ml-agent-skillsWrote 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/aiopshwang/data-analysis-ml-agent-skills/auditing-data-and-ground-truth)<a href="https://agentmods.dev/skills/aiopshwang/data-analysis-ml-agent-skills/auditing-data-and-ground-truth"><img src="https://agentmods.dev/badge/skills/aiopshwang/data-analysis-ml-agent-skills/auditing-data-and-ground-truth/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/aiopshwang/data-analysis-ml-agent-skills/auditing-data-and-ground-truth"><img src="https://agentmods.dev/badge/skills/aiopshwang/data-analysis-ml-agent-skills/auditing-data-and-ground-truth.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 | $0.00060 | $0.00731 |
| Opus 5 | $0.00030 | $0.00365 |
| Sonnet 5 | $0.00012 | $0.00146 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
auditing-data-and-ground-truth 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Data and Ground Truth
Determine what the data actually represents and whether its labels can support the intended claim. The outcome is an evidence-backed readiness judgment, not a generic profile report.
Anchor the audit
State the intended decision, entity, row grain, target, prediction moment, outcome window, and population. If these are not known, surface that uncertainty before interpreting columns or labels.
Preserve source data unchanged. Work from read-only inputs or documented snapshots, and keep every correction, exclusion, or derived label traceable to its source.
Inspect in risk order
- Source and lineage: Identify origin, owner, extraction time, filters, transformations, versions, and competing sources of truth.
- Structure: Measure row and entity counts, schema, types, key uniqueness, duplicates, missingness, ranges, and category cardinality. Report both counts and rates where scale matters.
- Meaning: Confirm units, code sets, null semantics, sentinel values, event meanings, and whether one row means what the analysis assumes.
- Relationships: Test key uniqueness on each side before joining. State expected and observed cardinality, unmatched rates, row multiplication, and aggregation effects.
- Time: Separate event time, record time, update time, and availability time. Check impossible orderings, late arrivals, backfills, and future knowledge.
- Ground truth: Trace each label from operational event to encoded value. Verify annotator or system provenance, adjudication, label maturity, class definitions, coverage, disagreement, and missing-label behavior.
- Reconciliation: Compare raw records with independent records or a stratified sample. Do not let agreement with a downstream table validate the upstream process that created both.
Read references/audit-playbook.md when selecting concrete tests or grading the severity of a finding.
Protect semantics
- Do not silently interpret missing as negative, absence as zero, or the latest value as historically available.
- Do not deduplicate until the real-world uniqueness rule is established.
- Do not repair values in place; produce a derived correction with a reason and impact count.
- Do not accept labels merely because they are named
target,truth,gold, orfinal. - Do not average away disagreements that reveal a definition or annotation problem.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 48 lines · 60 tokens per session scan A 887e758cca97
auditing-data-and-ground-truth is a skill published in the GitHub repository aiopshwang/data-analysis-ml-agent-skills (12 stars, last pushed 15d ago), licensed MIT. It adds 60 tokens to every session and 731 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
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