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/intelligencenpx skills add Datarails/dr-claude-code-plugins-re --skill intelligencegit 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 | $0.00083 | $0.08710 |
| Opus 5 | $0.00042 | $0.04355 |
| Sonnet 5 | $0.00017 | $0.01742 |
| Haiku 4.5 | $0.00008 | $0.00871 |
Grade A, and why
dr-intelligence 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 — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FP&A Intelligence Workbook
Generate a comprehensive 10-sheet FP&A intelligence workbook with auto-detected insights, recommendations, and professional Excel formatting.
This is the most powerful financial analysis skill — it answers real business questions, not just data dumps. All data is pulled via MCP tools and the workbook is built locally with openpyxl. No server-side rendering.
Excel Context — Routing Preamble
Before any data pull, establish whether this skill is running in a live Excel context (Claude for Excel with the Datarails Add-In loaded) and route accordingly.
Detect — never infer from the user's wording. A sheet list containing __dr_agent
means the add-in is loaded. Confirm with the agent.get_session probe, which you run by
executing Office.js through the execute_office_js tool (see the Excel Context Contract
in CLAUDE.md, §Transport) — it is not an MCP tool and has no MCP equivalent.
A failed probe is a normal detection result, not an error: it means "no bridge here",
which is the expected outcome in Claude Code. Do not surface it, do not retry it, and do
not apply this skill's connection-error or Connectors-UI guidance to it — that guidance is
about datarails-finance-os connector calls only.
A successful probe means Excel context, on either transport. The bridge serves two
add-in tracks and their session payloads differ: Flex (Office.js task pane) exposes
isLoggedIn; the COM desktop add-in — the majority of live workbooks — exposes
isConnected instead, and isConnected: false is not a login failure, an error, or a
reason to stop or send the user anywhere. It merely means the workbook isn't connected
to a Datarails file, which matters only to drilldown_* / create_dynamic_range (the
bridge skill gates those itself). Only Flex's explicit isLoggedIn: false means
sign-in is needed.
Route by the target of the request, not by whether a workbook is open.
- Org / server data — which tables, models and fields exist, aggregations, raw rows,
distinct values, metrics, profiling — always the
datarails-finance-osMCP connector, even in Excel. The bridge cannot answer these. - Workbook actions — refresh, drill a cell, insert a DR function, read what a cell
returns, publish, submit — always the add-in bridge. Never a native Excel recalc
(
calculate(), F9): it does not pull Datarails data and silently yields stale values.
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 · 483 lines · 83 tokens per session scan A b9659bf9d4c2
dr-intelligence is a skill published in the GitHub repository Datarails/dr-claude-code-plugins-re (3 stars, last pushed 3d ago), licensed MIT. It adds 83 tokens to every session and 8,710 once invoked, about $0.0004 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.