How Humanizer Detects and Rewrites Shallow -ing Analysis in AI Writing

Humanizer treats "shallow -ing analysis" as a core AI-writing-pattern detection, using a three-step rule-based pipeline to identify ornamental -ing phrases and rewrite them into concise factual statements while preserving the original meaning.

AI-generated text frequently relies on vague gerund phrases that sound analytical but lack substance. The blader/humanizer repository eliminates this fluff through a deterministic system specifically designed to handle shallow -ing analysis. This open-source tool interprets static markdown rules to strip ornamental language without changing underlying facts.

Pattern Definition in SKILL.md

The shallow -ing analysis detection logic is codified in SKILL.md under the heading ### 3. Shallow analysis with –ing phrases. This section defines specific Words to watch, including highlighting, underscoring, emphasizing, ensuring, reflecting, contributing, cultivating, encompassing, and showcasing. When one of these gerunds follows a simple factual clause, the parser flags the sequence for rewriting.

The Three-Step Rewrite Pipeline

Humanizer processes flagged text through a deterministic pipeline that ensures accuracy and clarity.

Step 1: Pattern Matching

The engine scans input for the Words to watch listed in SKILL.md. If an -ing phrase follows a simple factual clause, the system marks it as a shallow-analysis instance requiring intervention.

Step 2: Fact Preservation

Before any rewrite, Humanizer verifies that the original claim’s facts remain unchanged. The rewrite must never add, remove, or modify factual content; it may only trim the unnecessary -ing clause. This constraint ensures that all substantive meaning is retained while removing stylistic cruft.

Step 3: Natural-Language Re-write

The flagged clause is converted into a concise statement that conveys the same meaning without the ornamental -ing phrase. For example:

• Before: "The temple's color palette of blue, green, and gold resonates with the region's natural beauty, reflecting the community's deep connection to the land."
• After: "The temple is painted blue, green, and gold, colors meant to evoke Texas bluebonnets and the Gulf of Mexico."

Rule-Based Architecture

Humanizer’s engine is deliberately rule-based, performing no machine-learning inference at runtime. All patterns, including the shallow -ing analysis rule, are stored as static markdown data interpreted by a lightweight parser. This design guarantees deterministic, reproducible edits and makes the skill portable across different agent platforms, as documented in AGENTS.md.

Implementation Examples

You can invoke Humanizer as a Python function or via YAML configuration for agent integration.

Python API Usage

from humanizer import humanize

raw_text = """
The temple's color palette of blue, green, and gold resonates with the region's natural beauty,
reflecting the community's deep connection to the land.
"""

clean_text = humanize(raw_text)
print(clean_text)

# → The temple is painted blue, green, and gold, colors meant to evoke Texas bluebonnets and the Gulf of Mexico.

YAML Agent Configuration

humanizer:
  input: |
    The temple's color palette of blue, green, and gold resonates with the region's natural beauty,
    reflecting the community's deep connection to the land.
  mode: default

Summary

• Humanizer identifies shallow -ing analysis by matching text against specific gerunds defined in SKILL.md under the section ### 3. Shallow analysis with –ing phrases.

• The system uses a strict fact preservation policy to ensure rewrites only remove ornamental language, never altering underlying information.

• The architecture is rule-based and deterministic, relying on static markdown files rather than runtime machine learning for predictable, portable performance.

Frequently Asked Questions

What exactly constitutes "shallow -ing analysis"?

Shallow -ing analysis refers to the AI-writing habit of appending gerund phrases like "reflecting," "highlighting," or "ensuring" to factual statements. These phrases create a false sense of depth or analysis without adding substantive information, often making prose sound generic or robotic.

Where is the shallow -ing analysis pattern defined in the Humanizer codebase?

The pattern is defined in the SKILL.md file at the root of the blader/humanizer repository, specifically under the markdown heading ### 3. Shallow analysis with –ing phrases. This file acts as the single source of truth for all AI-writing-pattern detections.

Does Humanizer use machine learning to detect these patterns?

No. According to the source code architecture described in the repository, Humanizer operates as a rule-based system with no machine-learning inference at runtime. The lightweight parser applies static rules from markdown files, ensuring deterministic and reproducible edits across different environments.

Can Humanizer modify the factual content of a sentence during a rewrite?

Absolutely not. The fact preservation step explicitly forbids adding, removing, or modifying factual content. The system is constrained to trimming unnecessary -ing clauses only, ensuring that the rewrite conveys the exact same factual information as the original text.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}