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mikkelkrogsholm / API_MAPPING_PLAYBOOK.md
Created October 5, 2025 14:13
Automated API Mapping Playbook

Automated API Mapping Playbook

What Is This?

A simple way to automatically discover and document any website's API by giving Claude Code a single prompt. The AI will:

  • Navigate the website and capture all network traffic
  • Analyze API endpoints, authentication, and parameters
  • Generate complete OpenAPI 3.0 specifications
  • Create code examples in multiple languages
  • Produce comprehensive documentation
@mikkelkrogsholm
mikkelkrogsholm / gist:403fa538f5ee228b3ff13ef11e9f6cfc
Created November 2, 2022 08:56
Valgdata - Afstemningssteder - 02-11-2022-09:56
This file has been truncated, but you can view the full file.
letter,party,votes,votes_dif,nummer,navn,afstemningsstednavn,afstemningsstedadresseid,afstemningsstedadressebetegnelse,afstemningssted_adgangspunkt_x,afstemningssted_adgangspunkt_y,kommunekode,kommunenavn,regionskode,regionsnavn,opstillingskredsnummer,opstillingskredsnavn,storkredsnummer,storkredsnavn,valglandsdelsbogstav,valglandsdelsnavn
A,Socialdemokratiet,1668,112,1,1. Østerbro,Østerbrohuset,0a3f507b-381f-32b8-e044-0003ba298018,"Århusgade 103, 2100 København Ø",12.58791133,55.70749319,0101,København,1084,Region Hovedstaden,1,Østerbro,1,København,A,Hovedstaden
B,Radikale Venstre,822,-1040,1,1. Østerbro,Østerbrohuset,0a3f507b-381f-32b8-e044-0003ba298018,"Århusgade 103, 2100 København Ø",12.58791133,55.70749319,0101,København,1084,Region Hovedstaden,1,Østerbro,1,København,A,Hovedstaden
C,Det Konservative Folkeparti,519,-62,1,1. Østerbro,Østerbrohuset,0a3f507b-381f-32b8-e044-0003ba298018,"Århusgade 103, 2100 København Ø",12.58791133,55.70749319,0101,København,1084,Region Hovedstaden,1,Østerbro,1,København,A,H
@mikkelkrogsholm
mikkelkrogsholm / weather_energy_since_2021.csv
Created September 21, 2022 07:38
Data with energy prices and weather data from 2021
date price_mean price_min price_max price_sd temperature dew_point humidity wind_speed pressure precipitation
2021-01-01 47.875 41 57 5.033042989994267 3 2 92.1 11.3 1005.75743400001 0
2021-01-02 49.333333333333336 40 59 5.715476066494083 1 -0 91.5 7.7 1012.5302113333432 0
2021-01-03 33.958333333333336 29 46 5.320012803733094 2 -0 86.3 22.5 1022.6893773333434 0
2021-01-04 44.458333333333336 30 60 11.401674807053439 1 -2 82.2 28.6 1022.6893773333434 0
2021-01-05 46.791666666666664 32 61 11.598272610015332 2 -2 79.4 27.2 1022.6893773333434 0
2021-01-06 42.291666666666664 33 56 7.686686212376322 2 0 90.5 30.1 1012.5302113333432 0
2021-01-07 62.375 41 86 14.53575053027056 2 1 93.1 21.1 1009.1438226666768 0
2021-01-08 69 45 94 18.342158613660924 2 1 93.1 18.2 1012.5302113333432 0
2021-01-09 55.041666666666664 45 65 6.348427855137369 2 -0 87.1 14 1019.3029886666768 0
@mikkelkrogsholm
mikkelkrogsholm / forecast_ads.csv
Created November 25, 2020 19:41
Script and Analytical Data Set for forecasting Covid in Denmark
date regionnavn tests cases pospct rm
2020-03-04 Hovedstaden 32 2 0.0625 0.11133660616743324
2020-03-05 Hovedstaden 36 10 0.2777777777777778 0.12637420015239564
2020-03-06 Hovedstaden 49 0 0 0.17998937478667856
2020-03-07 Hovedstaden 48 8 0.16666666666666666 0.21897701285265764
2020-03-08 Hovedstaden 76 12 0.15789473684210525 0.2549464006077597
2020-03-09 Hovedstaden 157 71 0.45222929936305734 0.23185372267637208
2020-03-10 Hovedstaden 279 116 0.4157706093189964 0.24419940168871773
2020-03-11 Hovedstaden 280 88 0.3142857142857143 0.23092312343548096
2020-03-12 Hovedstaden 155 18 0.11612903225806452 0.22144888839733617
@mikkelkrogsholm
mikkelkrogsholm / world_in_data_epidemic.R
Created November 7, 2020 10:24
Code comparing covid cases and deaths in Denmark
# Load the tidyverse
library(tidyverse)
# Load the data from Our World in Data
csv_url <- "https://covid.ourworldindata.org/data/owid-covid-data.csv"
data <- read_csv(csv_url)
# Filter the data to only get Denmark and data after feb 27
pd <- data %>%
@mikkelkrogsholm
mikkelkrogsholm / gist:1ed3b7b3629f9f4023ec859b75a848a3
Created October 28, 2020 10:07
Code for comparing danish covid deaths with others deaths
library(tidyverse)
library(lubridate)
# JH Data
newnames <- c("province", "country", "lat", "lon")
url_global_deaths = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
global_deaths_raw <- read_csv(url_global_deaths)
@mikkelkrogsholm
mikkelkrogsholm / estimate_r.R
Created May 7, 2020 06:40
## Infrastructure notes ########################################################
# I am running this in docker image rocker/tidyverse:3.6.1
# Load packages
library(tidyverse)
library(EpiEstim)
## Fetch data from the covid19data API #########################################
url <- "https://api.covid19data.dk/ssi_newly_hospitalized"
@mikkelkrogsholm
mikkelkrogsholm / prediction.R
Created May 4, 2020 08:43
This is the code used to create my prediction of COVID 19 hospitalisation needs in Denmark
library(tidyverse)
options(scipen = 999)
# http://epirecip.es/epicookbook/chapters/sir/r_desolve
# Load deSolve library
library(deSolve)
################################################################################
@mikkelkrogsholm
mikkelkrogsholm / waterposts.csv
Created July 26, 2019 16:36
This csv holds data on public and free water posts in Denmarks three largest cities
lat lng name vejnavn husnr postnr postnrnavn kommunekode
56.155787 10.20136 Mølleparken Åboulevarden 100 8000 Aarhus C 0751
56.1585 10.205833 Klostertorv Klostertorvet 10 8000 Aarhus C 0751
56.17001 10.220779 Risskov Dronning Margrethes Vej 16 8200 Aarhus N 0751
56.129284 10.203796 Mindeparken Carl Nielsens Vej 15B 8000 Aarhus C 0751
56.152705 10.201608 Ridehuset Frederiks Allé 6A 8000 Aarhus C 0751
56.150825 10.204959 Banegårdspladsen Banegårdspladsen 4A 8000 Aarhus C 0751
56.139098 10.207785 Tangkrogen Strandvejen 19 8000 Aarhus C 0751
56.156581 10.209509 Sct. Clemens Torv Skt. Clemens Torv 1 8000 Aarhus C 0751
55.657398 12.635994 Amager Strandvej - helårsåben Amager Strandvej 168 2300 København S 0101
@mikkelkrogsholm
mikkelkrogsholm / ep2019.csv
Created May 27, 2019 11:44
We can't make this file beautiful and searchable because it's too large.
election,constituency,place,party_letter,party,votes,dif_votes
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,A,Socialdemokratiet,1.023,376
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,B,Radikale Venstre,142,61
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,C,Det Konservative Folkeparti,127,-73
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,F,SF - Socialistisk Folkeparti,225,61
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,I,Liberal Alliance,37,-35
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,N,Folkebevægelsen mod EU,163,-74
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,O,Dansk Folkeparti,497,-569
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,V,"Venstre, Danmarks Liberale Parti",1.100,640
Europa-Parlamentsvalg 2019,Frederikshavnkredsen,Skagen,Ø,Enhedslisten - De Rød-Grønne,88,88