sloppy

name	sloppy
description	Detect and fix AI prose tells (slop) in text. Two-layer system: fast regex-based detection via the sloppy CLI, plus LLM contextual review and rewriting. Use when reviewing, editing, checking, or cleaning up prose that needs to read as human-written. Also use when generating content that should avoid AI patterns, or building a quality gate for AI-generated text.
allowed-tools	["Bash(sloppy:*)","Bash(cat:*)","Bash(mktemp:*)","Read","Write","Grep","Glob"]
user-invocable	true
argument-hint	[file to review, or 'prompt' to generate a chat/system prompt]
metadata	{"author":"bradleydwyer","version":"1.0.1","status":"experimental"}

Sloppy — AI Prose Detection & Repair

Two-layer anti-slop system. Layer 1 is deterministic regex detection via the sloppy CLI — fast (<30ms), consistent, handles counting and statistical analysis that LLMs can't do reliably. Layer 2 is LLM contextual review — interprets flags in context, catches what regex misses, produces rewrites.

When to Use This Skill

• Reviewing AI-generated prose before publishing
• Editing drafts that sound "too AI"
• Generating content that needs to read as human-written
• Checking your own writing for absorbed AI patterns
• Building content pipelines that need a quality gate
• Generating a voice directive to prevent slop at generation time

Installation

The sloppy CLI must be available on PATH.

Homebrew (recommended):

brew install bradleydwyer/tap/sloppy

From source (requires Rust toolchain):

cargo install --git https://github.com/bradleydwyer/sloppy --tag v1.0.1

Verify installation:

sloppy analyze -q <<< "test"

If the command is not found, install it before proceeding.

Mode Detection

Determine the mode from the user's request:

User Says	Mode	Entry Point
"check this", "review this text", "is this sloppy?"	Analyze	Step 1 → full workflow
"fix this", "clean this up", "rewrite this"	Fix	Step 1 → Step 4 (produce rewrite)
"voice directive", "prevention prompt", "anti-slop prompt", "give me a prompt"	Prompt	Prompt Generation section
file path or pasted text with no other instruction	Analyze	Step 1 → full workflow

---

Workflow

Step 1: Run the Detector

If the user provides a file path, analyze it directly. If they paste text, write it to a temp file first.

# File path (|| true prevents non-zero exit code on FAIL — read pass/fail from JSON)
sloppy analyze -f json path/to/file.md || true

# From pasted text
TMPFILE=$(mktemp /tmp/sloppy_XXXXXXXX)
cat > "$TMPFILE" << 'SLOP_EOF'
[pasted text here]
SLOP_EOF
sloppy analyze -f json "$TMPFILE" || true

Parse the JSON output:

• score: 0–100 (0 = clean, 100 = maximum slop)
• passed: true/false against threshold (default 30)
• flags: array with check_name, description, location, severity
• check_scores: per-check penalty breakdown (penalty, max, flags count)
• summary.checks_triggered: which of the 15 checks fired
• summary.warnings / summary.info: counts by severity

Report the score and pass/fail to the user immediately. Don't bury it in analysis.

Step 2: Interpret Flags in Context

Read references/checks.md for the full check reference.

For each flag from the detector, explain:

1. Why this pattern reads as AI-generated — not just that it was detected
2. Where it appears — quote the surrounding context from the location field
3. Whether it's a true positive or false positive in this specific context

False positive judgment is critical. The detector can't distinguish:

• "landscape" used literally vs. metaphorically
• "testament" in religious/legal context vs. as filler
• "foster" as a proper name vs. as a verb
• "robust" in a genuine engineering context vs. as a vague superlative
• "revolutionary" in historical context vs. as a promotional adjective
• A rule-of-three that's genuinely the right rhetorical choice

Mark clear false positives explicitly. Don't count them toward the effective score.

Step 3: Contextual Review (Beyond Regex)

Read references/contextual-review.md for the full list.

Look for AI tells the detector doesn't cover:

• Hedging language: "it could be argued", "in many ways", "to some extent"
• Balanced-perspective equivocation: both-sidesing where a stance is needed
• Generic abstractions: "innovation", "collaboration", "community" without specifics
• Subtle sycophancy: "That's a really interesting point", "Excellent observation!" (the most obvious chatbot artifacts are now caught by Layer 1, but subtler forms still need judgment)
• Structural paragraph rhythm: paragraphs that all follow the same topic-evidence-transition structure even when sentence counts differ (basic length uniformity is now caught by Layer 1)
• Subtle hedging: "One might argue", "It is generally the case that" (the most formulaic openers are now caught by Layer 1)
• False gravitas: inflating mundane observations into profundity
• Dramatic isolated fragments: single-sentence paragraphs for emphasis (AI overuses this)

Present contextual findings separately from detector flags. Be specific — quote the text, explain the problem.

Step 4: Produce Revised Text

Only if the user requested a rewrite, or if the score fails the threshold and the user is in Fix mode.

Rewrite the full text addressing all true-positive flags and contextual issues:

• Don't merely swap flagged words. Restructure sentences so they don't need those words.
• Replace copulative inflation ("serves as") with direct verbs ("is"), but also consider if the whole sentence needs rethinking.
• When eliminating rule-of-three triplets, ask whether the list is necessary at all. A single vivid specific is often stronger.
• For trailing participles, rewrite as a new sentence or restructure the clause.
• Vary sentence length aggressively. Mix fragments under 6 words with compound sentences over 25 words.
• Anchor in specific, concrete, unusual details over generic abstractions.
• Take committed stances. No balanced-perspective hedging.
• No formulaic conclusions. End when done. Don't summarize.

Step 5: Re-check

Run the detector again on the revised text:

sloppy analyze -f json /tmp/slop_review_revised.md || true

Report the new score. If it still fails the threshold, iterate on remaining flags. Maximum 3 rewrite iterations before presenting the best version and noting remaining issues.

---

Prompt Generation (Prevention)

When the user wants to prevent slop at generation time rather than catch it after:

# Prompt for writing clean prose (paste into any chat window)
sloppy prompt generate

# Prompt for cleaning up existing sloppy text
sloppy prompt cleanup

# Raw system-level constraint block (for API system prompts)
sloppy prompt system

# Any of the above with --copy to copy to clipboard
sloppy prompt generate --copy

The generate and cleanup modes produce chat-ready prompts that work when pasted into ChatGPT, Claude, Gemini, or any other chat interface. The system mode produces a raw constraint block for API system prompts. All modes reflect custom .sloppy.toml settings if present.

---

Configuration

The detector is configurable via .sloppy.toml in the project root.

# Create a template config
sloppy config --init

# View the fully resolved config (defaults + overrides)
sloppy config --dump

Users can: add/remove banned words, adjust penalty weights per check, change the pass/fail threshold, or disable checks entirely. All config is optional — everything works with zero configuration.

---

CLI Quick Reference

# Analyze a file (human-readable output)
sloppy analyze file.md

# Analyze with JSON output (for programmatic use)
sloppy analyze -f json file.md

# Analyze from stdin
echo "text" | sloppy analyze

# Quiet mode — score and pass/fail only
sloppy analyze -q file.md

# Custom threshold
sloppy analyze -t 20 file.md

# Disable specific checks
sloppy analyze --disable burstiness --disable rule_of_three file.md

# Run only one check
sloppy analyze --only lexical_blacklist file.md

# Analyze multiple files
sloppy analyze *.md

# Generate a prompt for clean writing (paste into any chat)
sloppy prompt generate

# Generate a prompt for cleaning up sloppy text
sloppy prompt cleanup

# Generate raw system prompt constraints
sloppy prompt system

# Any prompt mode with clipboard copy
sloppy prompt cleanup --copy

# Initialize config
sloppy config --init

# Dump resolved config
sloppy config --dump

---

Tips

• Score ≤ 10 is the goal for polished prose. 10–30 is acceptable for internal docs.
• Don't chase score 0. Some flagged patterns are legitimate in context. Judge false positives.
• Use --disable for domain-specific exceptions. Technical docs might legitimately use "robust" — disable lexical_blacklist or customize the word list.
• The prompt command is the highest-leverage output. One prompt pasted into a chat window or system prompt prevents hundreds of downstream fixes.
• JSON output + jq makes sloppy composable in pipelines: sloppy analyze -f json file.md | jq '.flags[] | .check_name'
• Run on your own prompts and system messages too. AI slop in prompts begets AI slop in outputs.