Agent-almanac build-parameterized-report

install
source · Clone the upstream repo
git clone https://github.com/pjt222/agent-almanac
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/pjt222/agent-almanac "$T" && mkdir -p ~/.claude/skills && cp -r "$T/i18n/caveman-ultra/skills/build-parameterized-report" ~/.claude/skills/pjt222-agent-almanac-build-parameterized-report-5ce1fd && rm -rf "$T"
manifest: i18n/caveman-ultra/skills/build-parameterized-report/SKILL.md
source content

Build Parameterized Report

Reports that accept params → many customized variations from single template.

Use When

  • Same report for diff depts, regions, time periods
  • Client-specific reports from template
  • Dashboards filtered to specific subsets
  • Recurring reports w/ diff ins

In

  • Required: Report template (Quarto or R Markdown)
  • Required: Param defs (names, types, defaults)
  • Optional: Param values list for batch
  • Optional: Out dir for generated reports

Do

Step 1: Define Params in YAML

Quarto (

report.qmd
):

---
title: "Sales Report: `r params$region`"
params:
  region: "North America"
  year: 2025
  include_forecast: true
format:
  html:
    toc: true
---

R Markdown (

report.Rmd
):

---
title: "Sales Report"
params:
  region: "North America"
  year: 2025
  include_forecast: true
output: html_document
---

YAML header has

params:
block w/ named params, each w/ default of correct type.

If err: Render fails w/ "object 'params' not found" → ensure

params:
block indented correctly under YAML frontmatter. Quarto:
params
at top level, not nested under
format:
.

Step 2: Use Params in Code

```{r}
#| label: filter-data

data <- full_dataset |>
  filter(region == params$region, year == params$year)

nrow(data)
```

## Overview for `r params$region`

This report covers the `r params$region` region for `r params$year`.

```{r}
#| label: forecast
#| eval: !expr params$include_forecast

# This chunk only runs when include_forecast is TRUE
forecast_model <- forecast::auto.arima(data$sales)
forecast::autoplot(forecast_model)
```

Chunks ref params via

params$name
, conditional chunks use
#| eval: !expr params$flag
for Quarto. Inline R expressions like
`r params$region`
render dynamic text.

If err:

params$name
returns NULL → verify name matches exactly YAML ↔ code ref (case-sensitive). Check default values correct type.

Step 3: Render w/ Custom Params

Single:

# Quarto
quarto::quarto_render(
  "report.qmd",
  execute_params = list(region = "Europe", year = 2025)
)

# R Markdown
rmarkdown::render(
  "report.Rmd",
  params = list(region = "Europe", year = 2025),
  output_file = "report-europe-2025.html"
)

Single report renders w/ custom params overriding YAML defaults. Out file at specified path.

If err: Quarto fails → check

quarto
CLI installed + on PATH. R Markdown fails → verify
rmarkdown
installed. Param names in
execute_params
(Quarto) or
params
(R Markdown) match YAML defs exactly.

Step 4: Batch Render

regions <- c("North America", "Europe", "Asia Pacific", "Latin America")
years <- c(2024, 2025)

# Generate all combinations
combinations <- expand.grid(region = regions, year = years, stringsAsFactors = FALSE)

# Render each
purrr::pwalk(combinations, function(region, year) {
  output_name <- sprintf("report-%s-%d.html",
    tolower(gsub(" ", "-", region)), year)

  quarto::quarto_render(
    "report.qmd",
    execute_params = list(region = region, year = year),
    output_file = output_name
  )
})

One HTML per region-year combination.

If err: Check param names match exactly YAML ↔ code. Ensure all values valid.

Step 5: Param Validation

#| label: validate-params

stopifnot(
  "Region must be a valid region" = params$region %in% valid_regions,
  "Year must be numeric" = is.numeric(params$year),
  "Year must be reasonable" = params$year >= 2020 && params$year <= 2030
)

Validation chunk runs at start of each render, stops w/ informative err if param out of range or wrong type.

If err:

stopifnot()
unhelpful msgs → switch to explicit
if (!cond) stop("message")
for clearer diagnostics.

Step 6: Organize Out

# Create output directory
output_dir <- file.path("reports", format(Sys.Date(), "%Y-%m"))
dir.create(output_dir, recursive = TRUE, showWarnings = FALSE)

# Render with output path
quarto::quarto_render(
  "report.qmd",
  execute_params = list(region = region),
  output_file = file.path(output_dir, paste0("report-", region, ".html"))
)

Out files to date-stamped subdir w/ descriptive names (e.g.,

reports/2025-06/report-europe.html
).

If err:

dir.create()
fails → check parent dir exists + writable. Windows: verify path length ≤ 260 chars.

Check

  • Renders w/ default params
  • Renders w/ each custom set
  • Params validated before processing
  • Out files named descriptively
  • Conditional sections render based on params
  • Batch completes for all combinations

Traps

  • Name mismatch: YAML names must exactly match
    params$name
    in code
  • Type coercion: YAML may parse
    year: 2025
    as int but code expects char. Be explicit
  • Conditional eval: Use
    #| eval: !expr params$flag
    not
    eval = params$flag
    in Quarto
  • File overwriting: No unique names → each render overwrites prev
  • Memory in batch: Long batches accumulate mem. Use
    callr::r()
    for isolation

  • create-quarto-report
    — base Quarto doc setup
  • generate-statistical-tables
    — tables that adapt to params
  • format-apa-report
    — parameterized academic reports