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/tablesnpx skills add Datarails/dr-claude-code-plugins-re --skill tablesgit clone --depth 1 https://github.com/Datarails/dr-claude-code-plugins-reWrote 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/datarails/dr-claude-code-plugins-re/tables)<a href="https://agentmods.dev/skills/datarails/dr-claude-code-plugins-re/tables"><img src="https://agentmods.dev/badge/skills/datarails/dr-claude-code-plugins-re/tables.svg" alt="Measured on agentmods" 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 | $0.00109 | $0.02118 |
| Opus 5 | $0.00055 | $0.01059 |
| Sonnet 5 | $0.00022 | $0.00424 |
| Haiku 4.5 | $0.00011 | $0.00212 |
Grade A, and why
dr-tables 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 4d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datarails Table Discovery
Explore Finance OS tables - list available tables, view schemas, and understand data structure.
Workflow
Step 1: Verify Authentication
If any Datarails tool call fails with an authentication or connection error, tell the user to click the "+" button next to the prompt, select Connectors, find Datarails, and click Connect. Then STOP.
Step 2: Handle Request
List all tables (no arguments):
- Use
mcp__datarails-finance-os__list_data_models - Each entry carries both a numeric
idand analias(empty when the table has no business alias) — note both, they drive which schema/field tools to use next - Present tables in a formatted list with IDs, aliases, and names
- Group by category if available
View specific table (with table_id):
- If the table has an alias, use
mcp__datarails-finance-os__list_aliased_fields(business-friendly field aliases); otherwise usemcp__datarails-finance-os__get_fields_by_id(capture each field's numericid) - For a quick data overview, run
mcp__datarails-finance-os__profile_numeric_fields(table_id)(stats per numeric field) andmcp__datarails-finance-os__profile_categorical_fields(table_id, fields=[...])— always pass an explicitfieldslist of business dimensions taken from the schema just fetched (account-hierarchy levels, scenario, entity/department-like, dates); called bare the tool profiles upload/mapping metadata columns, not business data. The tool caps at 5 fields per call and silently drops the rest — an explicit list longer than 5 is still truncated, so batch into calls of ≤5 and merge the results before presenting them as the table's overview - Present schema in a readable table format
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.
- 4d ago First seen · 149 lines · 109 tokens per session scan A 1fd5a2e443c2
dr-tables is a skill published in the GitHub repository Datarails/dr-claude-code-plugins-re (3 stars, last pushed 5d ago), licensed MIT. It adds 109 tokens to every session and 2,118 once invoked, about $0.0005 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 skills, from other repositories
convex-add
Add a capability to the CURRENT Convex app — consults the served Convex capability catalog for always-current procedures (billing, crons, auth, agent, search, …); falls back to built-in hosting or @convex-dev component search. TRIGGER when the user runs /add, or asks to add hosting/publishing or any backend capability…
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
cwv-optimizer
Diagnose and fix Core Web Vitals issues on AEM Edge Delivery Services pages. Goes deeper than generic CWV advice by understanding EDS-specific performance patterns including the 100KB LCP budget, E-L-D loading phases, block rendering behavior, and third-party script impact. Produces specific fixes for LCP, CLS, and…
multi-expert-analyzer
针对通用问题进行多领域专家联合分析, 综合稿产生前必经 fact-checker 与 red-team 两道独立校验。适用场景: 用户提出跨领域或不确定领域的复杂问题, 需要从多个专家角度分别搜证并相互校验后综合成文, 例如该不该买房、该不该跳槽、是否进入某个赛道等。触发关键词: 多角度分析、专家分析、综合分析、多视角、跨领域分析、从不同角度看、深度分析。问题只属于单一明确领域时, 优先使用该领域的专门 skill, 例如纯财务用 finance-core-analysis、纯技术用 software-architect。输出 (全部 markdown 保存到当前项目 markdown/ 目录): (1) 每位专家的中间分析稿…