Annotate Source Files

Add PUT workflow annotations to source files so putior can extract structured workflow data and generate Mermaid diagrams.

When to Use

• After analyzing a codebase with

  analyze-codebase-workflow

  and having an annotation plan
• Adding workflow documentation to new or existing source files
• Enriching auto-detected workflows with manual labels and connections
• Documenting data pipelines, ETL processes, or multi-step computations

Inputs

• Required: Source files to annotate
• Required: Annotation plan or knowledge of the workflow steps
• Optional: Style preference: single-line or multiline (default: single-line)
• Optional: Whether to use

  put_generate()

  for skeleton generation (default: yes)

Procedure

Step 1: Determine Comment Prefix

Each language has a specific comment prefix for PUT annotations. Use

get_comment_prefix()

to find the correct one.

library(putior)

# Common prefixes
get_comment_prefix("R")    # "#"
get_comment_prefix("py")   # "#"
get_comment_prefix("sql")  # "--"
get_comment_prefix("js")   # "//"
get_comment_prefix("ts")   # "//"
get_comment_prefix("go")   # "//"
get_comment_prefix("rs")   # "//"
get_comment_prefix("m")    # "%"
get_comment_prefix("lua")  # "--"

Expected: A string like

"#"

,

"--"

,

"//"

, or

"%"

.

Line and block comments: putior detects annotations in both line comments (

//

,

#

,

--

) and C-style block comments (

/* */

,

/** */

). For JS/TS, both

//

and

/* */

blocks are scanned. Python triple-quote strings (

''' '''

) are not detected — use

#

for Python annotations.

On failure: If the extension is not recognized, the file language may not be supported. Check

get_supported_extensions()

for the full list. For unsupported languages, use

#

as a conventional default.

Step 2: Generate Annotation Skeletons

Use

put_generate()

to create annotation templates based on auto-detected I/O.

# Print suggestions to console
put_generate("./src/etl/")

# Single-line style (default)
put_generate("./src/etl/", style = "single")

# Multiline style for complex annotations
put_generate("./src/etl/", style = "multiline")

# Copy to clipboard for pasting
put_generate("./src/etl/", output = "clipboard")

Example output for an R file:

# put id:'extract_data', label:'Extract Customer Data', input:'customers.csv', output:'raw_data.internal'

Example output for SQL:

-- put id:'load_data', label:'Load Customer Table', output:'customers'

Expected: One or more annotation comment lines per source file, pre-filled with detected function names and I/O.

On failure: If no suggestions are generated, the file may not contain recognizable I/O patterns. Write annotations manually based on your understanding of the code.

Step 3: Refine Annotations

Edit the generated skeletons to add accurate labels, connections, and metadata.

Annotation syntax reference:

<prefix> put id:'unique_id', label:'Human Readable Label', input:'file1.csv, file2.rds', output:'result.parquet, summary.internal'

Fields:

• id

  (required): Unique identifier, used for node connections

• label

  (required): Human-readable description shown in diagram

• input

  : Comma-separated list of input files or variables

• output

  : Comma-separated list of output files or variables

• .internal

  extension: Marks in-memory variables (not persisted between scripts)

• node_type

  : Controls Mermaid node shape and class styling. Values:

  • "input"

    — stadium shape

    ([...])

    for data sources and configuration

  • "output"

    — subroutine shape

    [[...]]

    for generated artifacts

  • "process"

    — rectangle

    [...]

    for processing steps (default)

  • "decision"

    — diamond

    {...}

    for conditional logic

  • "start"

    /

    "end"

    — stadium shape

    ([...])

    for entry/terminal nodes

Example with

node_type

:

# put id:'config', label:'Load Config', node_type:'input', output:'config.internal'
# put id:'transform', label:'Apply Rules', node_type:'process', input:'config.internal', output:'result.rds'
# put id:'report', label:'Generate Report', node_type:'output', input:'result.rds'

Multiline syntax (for complex annotations):

# put id:'complex_step', \
#   label:'Multi-line Label', \
#   input:'data.csv, config.yaml', \
#   output:'result.parquet'

Block comment syntax (for

//

-prefix languages only: JS, TS, Go, Rust, C, C++, Java, etc.):

Languages that use

//

for line comments also support PUT annotations inside

/* */

and

/** */

block comments. Use

* put

as the line prefix inside the block body:

/* put id:'init', label:'Initialize Config', output:'config.internal' */

/**
 * put id:'process', \
 *   label:'Process Records', \
 *   input:'config.internal, records.json', \
 *   output:'results.json'
 */
function processRecords(config, records) {
  // ...
}

JSDoc-style annotations are particularly useful when documenting workflow steps alongside API documentation:

/**
 * Transform raw sensor data into normalized readings.
 * put id:'normalize', label:'Normalize Sensor Data', input:'raw_readings.json', output:'normalized.parquet'
 */
export function normalizeSensorData(readings: SensorReading[]): NormalizedData {
  // ...
}

Note: Block comment annotations are not supported for

#

-prefix languages (R, Python, Shell) or

--

-prefix languages (SQL, Lua). Use only line comments for those languages. Block-originated annotations do not support backslash continuation across lines.

Cross-file data flow (connecting scripts via file-based I/O):

# Script 1: extract.R
# put id:'extract', label:'Extract Data', output:'raw_data.internal, raw_data.rds'
data <- read.csv("source.csv")
saveRDS(data, "raw_data.rds")

# Script 2: transform.R
# put id:'transform', label:'Transform Data', input:'raw_data.rds', output:'clean_data.parquet'
data <- readRDS("raw_data.rds")
arrow::write_parquet(clean, "clean_data.parquet")

Expected: Annotations refined with accurate IDs, labels, and I/O fields that reflect actual data flow.

On failure: If unsure about I/O, use

.internal

extension for in-memory intermediates and explicit file names for persisted data.

Step 4: Insert Annotations into Files

Place annotations at the top of each file or immediately above the relevant code block.

Placement conventions:

1. File-level annotation: Place at the top of the file, after any shebang line or file header comment
2. Block-level annotation: Place immediately above the code block it describes
3. Multiple annotations per file: Use for files with distinct workflow phases

Example placement in an R file:

#!/usr/bin/env Rscript
# ETL Extract Script
#
# put id:'read_source', label:'Read Source Data', input:'raw_data.csv', output:'df.internal'

df <- read.csv("raw_data.csv")

# put id:'clean_data', label:'Clean and Validate', input:'df.internal', output:'clean.rds'

df_clean <- df[complete.cases(df), ]
saveRDS(df_clean, "clean.rds")

Use the Edit tool to insert annotations into existing files without disturbing surrounding code.

Expected: Annotations inserted at appropriate locations in each source file.

On failure: If annotations break syntax highlighting in the editor, ensure the comment prefix is correct for the language. PUT annotations are standard comments and should not affect code execution.

Step 5: Validate Annotations

Run putior's validation to check annotation syntax and connectivity.

# Scan annotated files
workflow <- put("./src/", validate = TRUE)

# Check for validation issues
print(workflow)
cat(sprintf("Total nodes: %d\n", nrow(workflow)))

# Verify connections by checking input/output overlap
inputs <- unlist(strsplit(workflow$input, ",\\s*"))
outputs <- unlist(strsplit(workflow$output, ",\\s*"))
connected <- intersect(inputs, outputs)
cat(sprintf("Connected data flows: %d\n", length(connected)))

# Generate diagram to visually inspect
cat(put_diagram(workflow, theme = "github", show_source_info = TRUE))

# Merge with auto-detected for maximum coverage
merged <- put_merge("./src/", merge_strategy = "supplement")
cat(put_diagram(merged, theme = "github"))

Expected: All annotations parse without errors. The diagram shows a connected workflow.

put_merge()

fills in any gaps from auto-detection.

On failure: Common validation issues:

• Missing closing quote:

  id:'name

  →

  id:'name'

• Using double quotes inside:

  id:"name"

  →

  id:'name'

• Duplicate IDs across files: each

  id

  must be unique across the entire scanned directory
• Backslash continuation on the wrong line: the

  \

  must be the last character before newline

Validation

• Every annotated file has syntactically valid PUT annotations

• put("./src/")

  returns a data frame with the expected number of nodes
• No duplicate

  id

  values across the scanned directory

• put_diagram()

  produces a connected flowchart (not all isolated nodes)
• Multiline annotations (if used) parse correctly with backslash continuation

• .internal

  variables appear only as outputs, never as cross-file inputs
• Files excluded via

  exclude

  parameter do not appear in the workflow (e.g.,

  put("./src/", exclude = "test_")

  skips test helpers)

Common Pitfalls

• Quote nesting errors: PUT annotations use single quotes:

  id:'name'

  . Double quotes cause parsing issues when the annotation is inside a string context.
• Duplicate IDs: Every

  id

  must be globally unique within the scanned scope. Use a naming convention like

  <script>_<step>

  (e.g.,

  extract_read

  ,

  transform_clean

  ).
• .internal as cross-file input:

  .internal

  variables exist only during script execution. To pass data between scripts, use a persisted file format (

  .rds

  ,

  .csv

  ,

  .parquet

  ) as the output of one script and input of the next.
• Missing connections: If the diagram shows disconnected nodes, check that output filenames in one annotation exactly match input filenames in another (including extensions).
• Wrong comment prefix: Using

  #

  in a SQL file or

  //

  in Python will cause the annotation to be treated as code, not a comment. Always verify with

  get_comment_prefix()

  .
• Forgetting multiline continuation: When using multiline annotations, every continued line must end with

  \

  and the next line must start with the comment prefix.
• Python triple-quote strings: putior does not scan Python triple-quote strings (

  ''' '''

  ,

  """ """

  ). Always use

  #

  for Python PUT annotations.
• Meta-pipeline annotations: If you annotate a build script that also scans for annotations (e.g., a script that calls

  put()

  and

  put_diagram()

  ), the script's own annotations will appear in the generated diagram. Either exclude the file from scanning (see

  generate-workflow-diagram

  Common Pitfalls) or avoid placing PUT annotations in the build script itself.

Related Skills

• analyze-codebase-workflow

  — prerequisite: produces the annotation plan this skill follows

• generate-workflow-diagram

  — next step: generate the final diagram from annotations

• install-putior

  — putior must be installed before annotating

• configure-putior-mcp

  — MCP tools provide interactive annotation assistance