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 agentmods add commands/datarails/dr-claude-code-plugins-re/data-checkgit clone --depth 1 https://github.com/Datarails/dr-claude-code-plugins-reWhat 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 | $0.00016 | $0.00165 |
| Opus 5 | $0.00008 | $0.00082 |
| Sonnet 5 | $0.00003 | $0.00033 |
| Haiku 4.5 | $0.00002 | $0.00016 |
Grade A, and why
data-check 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 2d 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
Data Quality Check
This command is a thin launcher — the single maintained data-quality recipe
lives in the datarails-financeos:anomalies skill. Do not improvise workflow steps here.
Invoke the datarails-financeos:anomalies skill with the user's arguments (a table name or
id if they gave one; otherwise the skill discovers the financials table itself).
Related skills for the handoff:
- Field-level statistical profiling (ranges, null rates, cardinality) →
datarails-financeos:profile. - A full data-quality Excel workbook deliverable →
datarails-financeos:anomalies-report.
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.
- 2d ago First seen · 18 lines · 16 tokens per session scan A 996291b8c9cc
data-check is a command published in the GitHub repository Datarails/dr-claude-code-plugins-re (3 stars, last pushed 3d ago), licensed MIT. It adds 16 tokens to every session and 165 once invoked, about $0.0001 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.
Other commands, from other repositories
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clarify
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specify
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analyze
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constitution
Create or update the project constitution from interactive or provided principle inputs.