How Humanizer Detects and Eliminates Common Chatbot Residue and Chat Wrappers

Humanizer detects chatbot residue by matching input text against static, regex-style pattern lists defined in SKILL.md, then eliminates wrappers through surgical deletion of conversational scaffolding and neutral rewriting of knowledge-limit disclaimers.

The blader/humanizer repository provides a data-driven text processing engine that identifies and removes machine-generated conversational artifacts. Unlike complex neural approaches, Humanizer relies on explicit pattern definitions—called tells—to detect common AI residue and produce clean, neutral prose. Every piece of text is treated as a draft that must be inspected against these predefined signatures before finalization.

The Detection Strategy: Static Tells in SKILL.md

Humanizer treats every input as a draft requiring inspection for recurring patterns that language models habitually insert. Two specific tells target the conversational leakage that creates chatbot residue:

Identifying Conversational Residue (Tell 22)

Under the Leftovers from the chat and the draft section in SKILL.md (lines 16–25), Humanizer watches for phrases such as “I hope this helps,” “Great question!,” and “Let me know”. These openings and closings function as chat wrappers that signal machine-generated scaffolding. When the detector finds any of these phrases, it removes the surrounding wrapper while preserving the core content untouched.

Spotting Knowledge-Limit Disclaimers (Tell 23)

Also defined in SKILL.md (lines 26–33), this tell targets boilerplate sentences mentioning training cut-offs or qualifying statements with “as of …,” “likely,” or “it is believed that.” Upon detection, Humanizer either drops the disclaimer entirely or rewrites the sentence to state the lack of source, ensuring it never fabricates information to fill the gap.

The Four-Step Elimination Workflow

The detection and elimination process follows a strict pipeline implemented in the processing engine:

1. Mark the tells – The system scans the entire input, matching each line against the regular-expression-style watch-lists. This list is static and requires no external libraries; it is pure pattern matching defined directly in the markdown file.
2. Draft the rewrite – After identifying a tell, Humanizer rewrites the segment by deleting the wrapper or converting the disclaimer into a neutral statement. The rewrite never adds new facts.
3. Check the draft – A second validation pass ensures no residual chatbot phrasing remains and that the rewrite still conveys the original meaning.
4. Produce the final version – The cleaned text is returned to the caller, optionally accompanied by a short list of remaining tells that could not be resolved.

Data-Driven Pattern Configuration

Because the tell definitions live in SKILL.md, the detection logic is entirely data-driven. Updating or expanding the list of chatbot residues merely involves editing the markdown file; the runtime engine automatically respects the new patterns without requiring code changes or redeployment. This architecture decouples the detection criteria from the execution logic, allowing non-developers to refine the filter by adjusting static text patterns.

Practical Implementation Examples

The following snippets demonstrate how Humanizer transforms input text by removing chatbot residue and knowledge-limit wrappers.

Removing greeting and closing wrappers:

Input:
Great question! Here is an overview of the French Revolution. It began in 1789 …
I hope this helps! Let me know if you’d like more detail.

Output:
The French Revolution began in 1789 …

Stripping knowledge-limit disclaimers:

Input:
While specific details about the company’s founding are not extensively documented, it appears to have been established sometime in the 1990s.

Output:
The company’s founding date is not documented in the available sources.

Using Humanizer programmatically:

from humanizer import Humanizer  # the skill loader

text = """
I hope this helps! The new policy — announced without warning — affects thousands.
"""

clean = Humanizer().process(text)
print(clean)

# → "The new policy, announced without warning, affects thousands."

Summary

• Chatbot residue and chat wrappers are detected via static tell definitions stored in SKILL.md (lines 16–33).
• The elimination workflow follows four strict steps: marking tells, drafting rewrites, checking drafts, and producing final output.
• Pattern matching requires no external libraries; it operates through pure regex-style matching against the markdown-defined lists.
• The system is data-driven, allowing updates to detection patterns by simply editing SKILL.md.

Frequently Asked Questions

What specific phrases does Humanizer target as chatbot residue?

According to SKILL.md lines 16–25, Humanizer targets conversational wrappers such as “I hope this helps,” “Great question!,” and “Let me know”. These phrases indicate automated conversational scaffolding that must be stripped to reveal the core content.

How does Humanizer handle knowledge-limit disclaimers?

When Humanizer detects phrases like “as of …” or “likely” defined in SKILL.md lines 26–33, it either deletes the disclaimer entirely or rewrites the sentence to neutrally state the lack of available sources. The system never fabricates information to replace the removed qualification.

Where are the detection patterns defined in the codebase?

All detection patterns reside in SKILL.md at the repository root. This file contains the complete watch-lists for tells 22 and 23, along with rewrite guidelines, making the detection logic fully data-driven and editable without modifying the runtime code.

Can the detection list be customized without changing the source code?

Yes. Because the tell definitions live entirely within the markdown file, you can add new chatbot residue patterns or knowledge-limit qualifiers by editing SKILL.md. The Humanizer engine automatically respects these new patterns during the next execution without requiring a code deployment.

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