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/reconciliationnpx skills add Datarails/dr-claude-code-plugins-re --skill reconciliationgit 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/reconciliation)<a href="https://agentmods.dev/skills/datarails/dr-claude-code-plugins-re/reconciliation"><img src="https://agentmods.dev/badge/skills/datarails/dr-claude-code-plugins-re/reconciliation.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 | $0.00070 | $0.07464 |
| Opus 5 | $0.00035 | $0.03732 |
| Sonnet 5 | $0.00014 | $0.01493 |
| Haiku 4.5 | $0.00007 | $0.00746 |
Grade A, and why
dr-reconcile 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.
This is a copy
86% identical to dr-insights — 560 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Consistency Reconciliation
Cross-check Finance OS data against itself using independently-sourced numbers — the same aggregate through two different API families, the balance-sheet identity, hierarchy roll-ups, and scenario/period checksums. Every check compares two numbers that arrive by different routes; nothing is ever compared against a re-read of the same aggregate (which would always "agree" and prove nothing).
Honest scope: these are data-pipeline & mapping consistency checks, not source-system reconciliation. A clean pass means the data in Finance OS is internally consistent across endpoints, grains, and slices — it does not prove the numbers match the ERP/GL. State this in the report output too.
Essential for month-end close and financial validation.
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.
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 · 437 lines · 70 tokens per session scan A 98f74d576526
dr-reconcile is a skill published in the GitHub repository Datarails/dr-claude-code-plugins-re (3 stars, last pushed 5d ago), licensed MIT. It adds 70 tokens to every session and 7,464 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to dr-insights, differing in 560 lines, and is treated as a copy.
Other skills, from other repositories
sector-rotation
行业轮动分析——申万行业景气度评分、行业动量排名、产业链传导、估值/盈利/资金流多维比较框架.
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…
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
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.
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…