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 Datarails/dr-claude-code-plugins-re --skill revenue-trendsgit 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/revenue-trends)<a href="https://agentmods.dev/skills/datarails/dr-claude-code-plugins-re/revenue-trends"><img src="https://agentmods.dev/badge/skills/datarails/dr-claude-code-plugins-re/revenue-trends/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/datarails/dr-claude-code-plugins-re/revenue-trends"><img src="https://agentmods.dev/badge/skills/datarails/dr-claude-code-plugins-re/revenue-trends.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 | $0.00040 | $0.05390 |
| Opus 5 | $0.00020 | $0.02695 |
| Sonnet 5 | $0.00008 | $0.01078 |
| Haiku 4.5 | $0.00004 | $0.00539 |
Grade A, and why
dr-revenue-trends 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 9d 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 — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revenue Trends
Analyze revenue patterns over time using real aggregated monthly data —
growth rates, peak/trough months, composition by sub-category, and
overall direction. Built on the aggregation start→poll tools
(start_aggregation_by_alias → get_aggregation_result_by_alias, or
the by-id twins) — no row cap, real totals, not samples.
This skill is self-contained: it discovers the client's financials table and field names itself (Step 2). It does not depend on a saved profile, a learn step, or any prior setup — every Datarails environment names its table and fields differently, so discovery happens inline, once per conversation.
Workflow
Step 1: Verify Authentication
If a tool call fails with an auth or connection error, tell the user to connect via the Connectors UI ("+" → Connectors → Datarails → Connect), then stop.
Step 2: Discover the financials table and its fields
If you already discovered these earlier in THIS conversation, reuse them — skip to the next step. Discovery is cheap but not free; do it once per conversation, then carry the values forward.
-
list_data_models. Pick the financials table: the one whose name (or alias) matches/financial|cube|p&?l|ledger|gl/i; if none match, the largest by row count. Note both its numericidand itsalias(the alias may be empty). Prefer the alias path when an alias exists — friendlier field names, far fewer tokens. -
Fields. If the table has an alias,
list_aliased_fields(<alias>); otherwiseget_fields_by_id(<financials_table_id>)(capture each field's numericid— the by-id tools address fields by id). Bind these by case-insensitive match on the field alias/name (respecting the noted type):<amount_field>— numeric:^amount$→transaction_amount→value<scenario_field>— categorical:^scenario$→^version$<date_field>— date/timestamp:reporting_date→posting_date→^date$<account_l1_field>—dr_acc_l1→account_l1→account_group_l1<account_l2_field>—dr_acc_l2→account_l2(optional, for--breakdown)
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.
- 9d ago First seen · 326 lines · 40 tokens per session scan A 60da547c66a7
dr-revenue-trends is a skill published in the GitHub repository Datarails/dr-claude-code-plugins-re (3 stars, last pushed 10d ago), licensed MIT. It adds 40 tokens to every session and 5,390 once invoked, about $0.0002 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
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
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
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
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.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.