Word Letter Frequency

Quick start

1. Identify the input text (typically one word or a short phrase). Default behavior lowercases the text and ignores non-letters so repeated letters like a/A are merged.
2. Run scripts/count_letters.py "<text>" to get a frequency table. Use the optional flags when needed:
   • --case-sensitive keeps uppercase and lowercase separate.
   • --include-non-alpha counts digits/punctuation as-is.
   • --json returns machine-friendly JSON for downstream processing.
3. Summarize the counts for the user. Include clarifying notes (e.g., whether you ignored punctuation) when relevant.

Script reference

scripts/count_letters.py

Lightweight CLI/utility that powers this skill. It exposes two layers:

• CLI usage: python3 scripts/count_letters.py "balloon" --json
• Import usage: from scripts.count_letters import count_letters and call count_letters(text, case_sensitive=False, include_non_alpha=False) to get a collections.Counter.

Sample CLI output (default options):

$ python3 scripts/count_letters.py "balloon"
Character  Count
---------  -----
a          1
b          1
l          2
o          2
n          1

Sample JSON output (good for embedding directly into responses):

$ python3 scripts/count_letters.py "AaB!" --case-sensitive --include-non-alpha --json
{"A": 1, "a": 1, "B": 1, "!": 1}

Response patterns

• Concise summary: “balloon contains b×1, a×1, l×2, o×2, n×1 (case-insensitive, punctuation ignored).”
• Tabular snippet: Mirror the script’s table for readability. Mention any options you used.
• JSON / dict: When the user wants structured data, reuse the script’s --json flag.

Edge cases & tips

• Make sure to state how you treated uppercase letters and punctuation, especially when the counts differ depending on options.
• If the input contains no alphabetic characters and --include-non-alpha is not set, the script intentionally reports “(no characters were counted)”. Explain why in the response.
• For multiple words, either run the script once on the full phrase (default) or note that the skill focuses on short strings; if the request expands to full documents, escalate to a general text-analysis workflow instead.