anthropics/financial-services

clean-data-xls

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns.

Zobacz źródło
Oryginalny dokument Skill

Treść z repozytorium z zachowaniem nagłówków, przykładów, kodu, tabel, linków i obrazów.

Clean Data

Clean messy data in the active sheet or a specified range.

Environment

  • If running inside Excel (Office Add-in / Office JS): Use Office JS directly (Excel.run(async (context) => {...})). Read via range.values, write helper-column formulas via range.formulas = [["=TRIM(A2)"]]. The in-place vs helper-column decision still applies.
  • If operating on a standalone .xlsx file: Use Python/openpyxl.

Workflow

Step 1: Scope

  • If a range is given (e.g. A1:F200), use it
  • Otherwise use the full used range of the active sheet
  • Profile each column: detect its dominant type (text / number / date) and identify outliers

Step 2: Detect issues

IssueWhat to look for
Whitespaceleading/trailing spaces, double spaces
Casinginconsistent casing in categorical columns (usa / USA / Usa)
Number-as-textnumeric values stored as text; stray $, ,, % in number cells
Datesmixed formats in the same column (3/8/26, 2026-03-08, March 8 2026)
Duplicatesexact-duplicate rows and near-duplicates (case/whitespace differences)
Blanksempty cells in otherwise-populated columns
Mixed typesa column that's 98% numbers but has 3 text entries
Encodingmojibake (é, ’), non-printing characters
Errors#REF!, #N/A, #VALUE!, #DIV/0!

Step 3: Propose fixes

Show a summary table before changing anything:

ColumnIssueCountProposed Fix

Step 4: Apply

  • Prefer formulas over hardcoded cleaned values — where the cleaned output can be expressed as a formula (e.g. =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.
  • Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding/mojibake repair)
  • For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first
  • After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category
  • Report a before/after summary of what changed
z tego samego repozytorium

Więcej Skills

Wszystkie Skills
anthropics
Oficjalne

initiating-coverage

Create institutional-quality equity research initiation reports through a 5-task workflow. Tasks must be executed individually with verified prerequisites - (1) company research, (2) financial modeling, (3) valuation analysis, (4) chart generation, (5) final report assembly. Each task produces specific deliverables (markdown docs, Excel models, charts, or DOCX reports). Tasks 3-5 have dependencies on earlier tasks.

instalacje
2
GitHub Stars
38,5 tys.
Aktualizacja
21 wrz
anthropics
Oficjalne

bond-futures-basis

Analyze the bond futures basis by pricing futures, identifying the cheapest-to-deliver, and comparing with yield curves to assess delivery option value and basis trading opportunities. Use when analyzing bond futures, computing the basis, identifying CTD bonds, calculating implied repo rates, or evaluating basis trades.

instalacje
1
GitHub Stars
38,5 tys.
Aktualizacja
21 wrz
anthropics
Oficjalne

catalyst-calendar

Build and maintain a calendar of upcoming catalysts across a coverage universe — earnings dates, conferences, product launches, regulatory decisions, and macro events. Helps prioritize attention and position ahead of events. Triggers on "catalyst calendar", "upcoming events", "what's coming up", "earnings calendar", "event calendar", or "catalyst tracker".

instalacje
1
GitHub Stars
38,5 tys.
Aktualizacja
21 wrz
anthropics
Oficjalne

datapack-builder

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financ

instalacje
1
GitHub Stars
38,5 tys.
Aktualizacja
21 wrz