Template Processing logo

Template Processing

Organization
cxcscmu
template-processing

Process templates with placeholder substitution and conditional sections

Overview

Publishercxcscmu
RepositorySkillLearnBench
Skill nametemplate-processing
Stars
91
Forks
7
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cxcscmu on GitHub. Read the source before you install it.

Installation

Install the Template Processing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cxcscmu/SkillLearnBench/tree/main/skills/b1-one-shot-claude-haiku-4-5/offer-letter-generator/template-processing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench.git /tmp/SkillLearnBench
mkdir -p .claude/skills
cp -r /tmp/SkillLearnBench/skills/b1-one-shot-claude-haiku-4-5/offer-letter-generator/template-processing .claude/skills/template-processing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Template Processing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Template Processing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Template Processing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Template Processing: Placeholders and Conditionals

Overview

When filling document templates, you often need to:

  1. Replace placeholders like {{PLACEHOLDER}} with actual data
  2. Handle conditional sections (show/hide content based on data)
  3. Clean up formatting markers

This skill covers patterns for template processing in both text and document contexts.

Placeholder Patterns

Basic Placeholders

{{CANDIDATE_FULL_NAME}}
{{POSITION}}
{{START_DATE}}
{{BASE_SALARY}}

Parsing from JSON

python
import json

with open('employee_data.json', 'r') as f:
    data = json.load(f)

# Access placeholder values
name = data['CANDIDATE_FULL_NAME']
position = data['POSITION']

Conditional Section Pattern

Template Format

{{IF_CONDITION_NAME}}
  Content to show if CONDITION_NAME is "Yes"
{{END_IF_CONDITION_NAME}}

Processing Conditionals

python
def process_conditional_section(text, condition_key, condition_value):
    """Remove conditional markers based on condition value"""
    start_marker = f'{{{{IF_{condition_key}}}}}'
    end_marker = f'{{{{END_IF_{condition_key}}}}}'

    # Find section
    start_idx = text.find(start_marker)
    end_idx = text.find(end_marker)

    if start_idx == -1 or end_idx == -1:
        return text  # No conditional section found

    # Extract the content between markers
    before = text[:start_idx]
    content = text[start_idx + len(start_marker):end_idx]
    after = text[end_idx + len(end_marker):]

    if condition_value.lower() == 'yes':
        # Keep content, remove markers
        return before + content + after
    else:
        # Remove entire section
        return before + after

Workflow for Template Processing

Step 1: Load Data

python
import json
from docx import Document

with open('employee_data.json', 'r') as f:
    data = json.load(f)

doc = Document('template.docx')

Step 2: Replace Basic Placeholders

python
def replace_placeholders(doc, data):
    """Replace all {{PLACEHOLDER}} with data values"""

    # Replace in paragraphs
    for para in doc.paragraphs:
        para_text = para.text
        for key, value in data.items():
            placeholder = f'{{{{{key}}}}}'
            para_text = para_text.replace(placeholder, str(value))

        # Update paragraph (handle text fragmentation)
        if para_text != para.text:
            for run in para.runs:
                run.text = ''
            para.text = para_text

    # Replace in tables
    for table in doc.tables:
        for row in table.rows:
            for cell in row.cells:
                cell_text = cell.text
                for key, value in data.items():
                    placeholder = f'{{{{{key}}}}}'
                    cell_text = cell_text.replace(placeholder, str(value))

                if cell_text != cell.text:
                    for para in cell.paragraphs:
                        for run in para.runs:
                            run.text = ''
                        para.text = cell_text
                    cell_text = cell.text  # Reset after first cell

Step 3: Handle Conditionals

python
def process_conditionals(doc, data):
    """Process {{IF_*}}...{{END_IF_*}} sections"""

    for para in doc.paragraphs:
        para_text = para.text

        # Find all conditional markers
        import re
        pattern = r'{{\s*IF_(\w+)\s*}}(.*?){{\s*END_IF_\1\s*}}'

        def replace_conditional(match):
            condition_key = match.group(1)
            content = match.group(2)

            condition_value = data.get(condition_key, 'No')

            if condition_value == 'Yes':
                return content.strip()
            else:
                return ''

        new_text = re.sub(pattern, replace_conditional, para_text, flags=re.DOTALL)

        if new_text != para_text:
            for run in para.runs:
                run.text = ''
            para.text = new_text

Important Considerations

  1. Text Fragmentation in Word: Placeholders might be split across multiple runs. Always work at the paragraph level (para.text) not individual run level.

  2. Order of Operations:

    • First handle conditionals (remove sections)
    • Then replace placeholders
  3. Multiline Content: When using regex for conditionals, use re.DOTALL flag to match across newlines

  4. Data Type Conversion: Convert all data values to strings when replacing:

    python
    placeholder_value = str(data[key])
  5. Preserve Formatting: The simple approach loses formatting. For preserving formatting:

    • Replace in individual runs while checking full paragraph
    • Or use a more sophisticated run-level replacement with formatting preservation

Complete Example

python
import json
import re
from docx import Document

def fill_offer_letter(template_path, data_path, output_path):
    # Load data
    with open(data_path) as f:
        data = json.load(f)

    doc = Document(template_path)

    # Process all paragraphs
    for para in doc.paragraphs:
        text = para.text

        # Handle conditionals first
        pattern = r'{{\s*IF_(\w+)\s*}}(.*?){{\s*END_IF_\1\s*}}'
        def replace_cond(m):
            key = m.group(1)
            content = m.group(2)
            return content.strip() if data.get(key) == 'Yes' else ''

        text = re.sub(pattern, replace_cond, text, flags=re.DOTALL)

        # Replace placeholders
        for key, value in data.items():
            text = text.replace(f'{{{{{key}}}}}', str(value))

        # Update paragraph
        if text != para.text:
            for run in para.runs:
                run.text = ''
            para.text = text

    # Similarly for tables
    for table in doc.tables:
        for row in table.rows:
            for cell in row.cells:
                for para in cell.paragraphs:
                    text = para.text
                    pattern = r'{{\s*IF_(\w+)\s*}}(.*?){{\s*END_IF_\1\s*}}'
                    text = re.sub(pattern, replace_cond, text, flags=re.DOTALL)
                    for key, value in data.items():
                        text = text.replace(f'{{{{{key}}}}}', str(value))
                    if text != para.text:
                        for run in para.runs:
                            run.text = ''
                        para.text = text

    doc.save(output_path)

Frequently asked questions

What does the Template Processing AI skill do?

Process templates with placeholder substitution and conditional sections

Why use Template Processing on TypingMind?

Because you install it once and use it with any model. Template Processing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Template Processing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cxcscmu/SkillLearnBench/tree/main/skills/b1-one-shot-claude-haiku-4-5/offer-letter-generator/template-processing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Template Processing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Template Processing?

As many as you like. As long as a model supports skills, you can use Template Processing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Template Processing AI skill free?

Yes. It is published on GitHub by cxcscmu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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