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 skills/datarails/dr-claude-code-plugins-re/testnpx skills add Datarails/dr-claude-code-plugins-re --skill testgit 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 | $0.00053 | $0.03558 |
| Opus 5 | $0.00026 | $0.01779 |
| Sonnet 5 | $0.00011 | $0.00712 |
| Haiku 4.5 | $0.00005 | $0.00356 |
Grade A, and why
dr-test 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.
How it starts
The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API Diagnostic & Field Compatibility Test
Test which fields work with the aggregation API for a specific environment. Discovers field compatibility and suggests sibling alternatives for failed fields, then reports the results to the user.
This skill is self-contained: discovering which fields work as aggregation dimensions is its whole job, so it finds the financials table and its candidate fields itself (inline, Step 2). It does not depend on a saved profile, a learn step, or any prior setup — and it does not write its findings to disk; it reports them in-conversation so the current session (and any skill that reuses the discovered table/fields) can act on them.
Purpose
The Datarails aggregation API works for nearly all fields — an all-PASS result is the norm — but occasionally a field fails per-client (500 errors). This skill:
- Discovers the financials table and its candidate categorical fields
- Tests each candidate field against aggregation
- Reports pass/fail with timing
- Discovers sibling alternatives for those that fail
- Reports the full compatibility result to the user (which fields work as dimensions, which 500, and the suggested sibling for each failure)
Arguments
No arguments required. Uses the currently authenticated environment.
What this skill discovers and reports
This skill discovers everything it needs inline and reports the result — it does not persist anything to disk.
What It Discovers (inline, Step 2)
- The financials table to test against (
list_data_models) - The candidate categorical fields to test (
list_aliased_fieldsif the table has an alias, elseget_fields_by_id)
Alias coverage is per field, not per table. A table having an alias does not mean its fields are aliased — real orgs often expose only a handful of aliased fields (e.g. ~5 of ~185 on a mapped financials table), and the load-bearing fields (
amount,scenario, account groups, dates) are frequently not among them. Treat the alias/by-id choice per field:get_fields_by_id(<id>)returns every field with its numericidand itsalias(empty if none). Address a field by alias (via the*_by_aliastools) when it has one, else by numericid(via the*_by_idtools). By-id always works — never abandon the query because the aliased set is thin.
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 · 309 lines · 53 tokens per session scan A d079e9ceb1b2
dr-test is a skill published in the GitHub repository Datarails/dr-claude-code-plugins-re (3 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 3,558 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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