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'

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

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