slop-guard 0.1.0

Rule-based prose linter for formulaic AI writing patterns.

slop-guard

A rule-based prose linter that scores text 0--100 for formulaic AI writing patterns. No LLM judge, no API calls. Pure regex.

It runs ~80 compiled patterns against your text and returns a numeric score, a list of specific violations with surrounding context, and concrete advice for each hit.

Installation

Requires uv.

Run without installing (recommended for MCP setups):

uvx slop-guard

Install a persistent command:

uv tool install slop-guard

Then run:

slop-guard

You can pin versions for reproducibility:

uvx slop-guard==0.1.0

Upgrade an installed tool:

uv tool upgrade slop-guard

Run from source

From a local checkout:

uv run slop-guard

This starts a stdio-based MCP server from the current repository.

Wire into Claude Code

Add to your .mcp.json:

{
  "mcpServers": {
    "slop-guard": {
      "command": "uvx",
      "args": ["slop-guard"]
    }
  }
}

If you want a fixed release, pin it in args, for example: ["slop-guard==0.1.0"].

Tools

check_slop(text) -- Analyze a string. Returns JSON.

check_slop_file(file_path) -- Read a file from disk and analyze it. Same output, plus a file field.

What it catches

The linter checks for overused vocabulary (adjectives, verbs, nouns, hedging adverbs), stock phrases and filler, structural patterns (bold-header-explanation blocks, long bullet runs, triadic lists, bold-term bullet runs, bullet-heavy formatting), tone markers (meta-communication, false narrativity, sentence-opener tells, weasel phrases, AI self-disclosure), rhythm monotony (uniform sentence length), em dash and elaboration colon density, contrast pairs, setup-resolution patterns, and repeated multi-word phrases (4-8 word n-grams appearing 3+ times).

Scoring uses exponential decay: score = 100 * exp(-lambda * density), where density is the weighted penalty sum normalized per 1000 words. Claude-specific categories (contrast pairs, setup-resolution, pithy fragments) get a concentration multiplier. Repeated use of the same tic costs more than diverse violations.

Scoring bands

Score	Band
80-100	Clean
60-79	Light
40-59	Moderate
20-39	Heavy
0-19	Saturated

Output

Both tools return JSON with this structure:

score          0-100 integer
band           "clean" / "light" / "moderate" / "heavy" / "saturated"
word_count     integer
violations     array of {type, rule, match, context, penalty}
counts         per-category violation counts
total_penalty  sum of all penalty values
weighted_sum   after concentration multiplier
density        weighted_sum per 1000 words
advice         array of actionable strings, one per distinct issue

violations[].type is always "Violation" for typed records.

Benchmark snapshot

Example score distribution from benchmark/us_pd_newspapers_histogram.py on PleIAs/US-PD-Newspapers (first 9,001 rows of one local shard):

Example score-vs-length scatter plot from benchmark/us_pd_newspapers_scatter.py on the same shard:

License

MIT