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ahmedak

defluff

by ahmedak
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Python
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    • slop-guard

      sofiawroblewski
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      quality
      A
      maintenance
      Scores prose from 0 to 100 for formulaic AI writing patterns using purely programmatic rules, with no LLM judge or API calls. Enables agents to lint text or files and receive a score, band label, specific violations with surrounding context, and concrete advice for each hit.
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    • slop-guard

      Prawit65
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      license
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      quality
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      Enables agents to analyze inline prose or files on disk for formulaic AI writing patterns, returning a 0-100 score, band label, specific violations with context, and concrete advice. Uses purely programmatic, configurable rules with no LLM judge or API calls.
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    • slop-guard

      pedro0040
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      license
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      quality
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      maintenance
      Enables agents to score prose from 0-100 for formulaic AI writing tells using pure rule-based heuristics, exposing tools to analyze inline text or files on disk and return violation details, band labels, and concrete advice without any LLM judge or network calls.
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    • slop-guard

      Nomura22872
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      license
      Not graded
      quality
      A
      maintenance
      Enables agents to score prose from 0-100 for formulaic AI writing tells using purely programmatic rule-based heuristics, with no LLM judge or API calls. Returns a numeric score, band label, specific violations with surrounding context, per-category counts, and concrete advice, and can also analyze a file path directly.
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    • slop-guard

      YannisGreen055
      F
      license
      Not graded
      quality
      A
      maintenance
      Analyzes text and files for formulaic AI writing patterns, returning a 0-100 score, band label, specific violations with context, and concrete revision advice. Runs entirely locally with rule-based heuristics, requiring no LLM judge or API calls.
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    • avoid-ai-writing-mcp

      conorbronsdon
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      Provides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.
      2
      2 npm
      3
      MIT
    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A4.3/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool targets a distinct operation: detection, addition to lexicon, and ignoring phrases. No overlap in functionality.

    Naming Consistency5/5

    All tools follow the consistent 'slop_verb' pattern (slop_add, slop_detect, slop_ignore), making names predictable and clear.

    Tool Count5/5

    With 3 tools, the server covers the core operations for a slop detection lexicon (add, detect, ignore). It is well-scoped and not excessive.

    Completeness4/5

    The set covers detection, addition, and whitelisting, but misses removal of phrases from the lexicon and listing the current lexicon, which are minor gaps.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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