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Treasury Bulletin data extraction tool + verification checklist + CPI + compute recipes
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Review board — verify the analyst's answer before final submission
Python computation formulas for Treasury data analysis. Use for percent change, CAGR, stdev, kurtosis, VaR, regression, Theil index, and other statistical calculations.
CPI-U annual averages (1929-2024) for inflation adjustment. Use when question mentions inflation, CPI, constant dollars, or real dollars.
How to read Treasury Bulletin data files in /app/resources/. Use when exploring resources or when JSON parsing fails.
U.S. fiscal year boundaries and calendar year conversions. Use when question mentions fiscal year, FY, or when you need to determine which months belong to a fiscal year.
How to accurately extract values from Treasury Bulletin tables. Use when reading tables with many columns, similar row labels, or when you need to verify you have the right cell.
| name | officeqa |
| description | Treasury Bulletin data extraction tool + verification checklist + CPI + compute recipes |
A combined search + table parser is ready at:
EXT="$HOME/Library/Application Support/goose/extensions/officeqa"
python3 "$EXT/q.py" "keyword" # find which files have the keyword
python3 "$EXT/q.py" "keyword" --row "defense" # search + extract matching rows
python3 "$EXT/q.py" "keyword" --row "1940" --col "defense" # search + specific cell
python3 "$EXT/q.py" FILE --rows # list all row labels
python3 "$EXT/q.py" FILE --cols # list all column headers
python3 "$EXT/q.py" FILE --row "defense" # extract all columns for matching rows
python3 "$EXT/q.py" FILE --row "1940" --col "yr" # specific cell
python3 "$EXT/q.py" FILE --row "def" --fuzzy # fuzzy match
Output is vertical: ROW: label then column = value per line. Units shown at top.
1913:9.9 1914:10.0 1915:10.1 1916:10.9 1917:12.8 1918:15.1 1919:17.3 1920:20.0 1921:17.9 1922:16.8 1923:17.1 1924:17.1 1925:17.5 1926:17.7 1927:17.4 1928:17.2 1929:17.2 1930:16.7 1931:15.2 1932:13.6 1933:12.9 1934:13.4 1935:13.7 1936:13.9 1937:14.4 1938:14.1 1939:13.9 1940:14.0 1941:14.7 1942:16.3 1943:17.3 1944:17.6 1945:18.0 1946:19.5 1947:22.3 1948:24.1 1949:23.8 1950:24.1 1951:26.0 1952:26.5 1953:26.7 1954:26.9 1955:26.8 1956:27.2 1957:28.1 1958:28.9 1959:29.1 1960:29.6 1961:29.9 1962:30.2 1963:30.6 1964:31.0 1965:31.5 1966:32.4 1967:33.4 1968:34.8 1969:36.7 1970:38.8 1971:40.5 1972:41.8 1973:44.4 1974:49.3 1975:53.8 1976:56.9 1977:60.6 1978:65.2 1979:72.6 1980:82.4 1981:90.9 1982:96.5 1983:99.6 1984:103.9 1985:107.6 1986:109.6 1987:113.6 1988:118.3 1989:124.0 1990:130.7 1991:136.2 1992:140.3 1993:144.5 1994:148.2 1995:152.4 1996:156.9 1997:160.5 1998:163.0 1999:166.6 2000:172.2 2001:177.1 2002:179.9 2003:184.0 2004:188.9 2005:195.3 2006:201.6 2007:207.3 2008:215.3 2009:214.5 2010:218.1 2011:224.9 2012:229.6 2013:233.0 2014:236.7 2015:237.0 2016:240.0 2017:245.1 2018:251.1 2019:255.7 2020:258.8 2021:271.0 2022:292.7 2023:304.7 2024:314.2 real = nominal × (target_cpi / source_cpi)
python3 -c "print(sum([v1,...,v12]))"python3 -c "print((NEW-OLD)/OLD*100)"python3 -c "import statistics; print(statistics.stdev([...]))"statistics.pstdev([...])r=statistics.linear_regression(xs,ys); print(r.slope,r.intercept)statistics.correlation(xs,ys)print(((END/START)**(1/YEARS)-1)*100)