Skip to content
cccskills
FreePublished on GitHub

unslop

Post-process AI-generated text through the unslop CLI to strip AI writing patterns before publishing

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md4.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

unslop — Strip AI Writing Patterns via CLI

Overview

unslop is a CLI tool that post-processes text to remove AI writing patterns programmatically. Unlike skills that ask the agent to avoid AI-isms, unslop runs as a deterministic pipeline step: pipe text in, get clean text out. Use it as a final pass before committing docs, publishing posts, or sending any AI-generated content to production.

The --deterministic flag makes output reproducible — same input always produces same output. The --stdin flag reads from stdin, enabling shell pipeline composition.

When to Use This Skill

  • When you have AI-generated text ready to publish and want a final cleanup pass
  • When working in a shell pipeline where text quality needs to be enforced automatically
  • When writing commit hooks or CI steps that validate content before it ships
  • When you need reproducible text normalization across multiple runs

Setup

Install once:

pipx install unslop
# or
uv tool install unslop

Verify:

unslop --version

How It Works

Step 1: Pipe Text Through unslop

Standard cleanup (may vary slightly between runs):

echo "This leverages cutting-edge AI to deliver robust solutions." | unslop --stdin

Deterministic cleanup (same input → same output every run):

echo "This leverages cutting-edge AI to deliver robust solutions." | unslop --stdin --deterministic

Step 2: Use in Shell Pipelines

Pipe the output of any command through unslop:

cat draft.md | unslop --stdin --deterministic > clean.md

Or chain with other tools:

cat draft.md | unslop --stdin --deterministic | pbcopy   # macOS: copy clean text to clipboard

Step 3: Integrate into Commit Hooks or CI

Add to a pre-commit hook or CI step to enforce quality gates on any generated content before it ships:

# In .git/hooks/pre-commit or a CI script
CONTENT=$(cat docs/changelog.md)
CLEANED=$(echo "$CONTENT" | unslop --stdin --deterministic)
if [ "$CONTENT" != "$CLEANED" ]; then
  echo "Changelog contains AI writing patterns. Run: cat docs/changelog.md | unslop --stdin --deterministic > docs/changelog.md"
  exit 1
fi

Examples

Example 1: Clean a Draft Document

cat blog-post-draft.md | unslop --stdin --deterministic > blog-post-final.md

Example 2: Inline Cleanup During Writing

# Write content, pipe through unslop, write result back
cat README.md | unslop --stdin > README.clean.md && mv README.clean.md README.md

Example 3: Validate Before Submitting a PR

# Check if any generated docs need cleanup
for f in docs/*.md; do
  ORIGINAL=$(cat "$f")
  CLEANED=$(echo "$ORIGINAL" | unslop --stdin --deterministic)
  [ "$ORIGINAL" != "$CLEANED" ] && echo "Needs cleanup: $f"
done

Best Practices

  • ✅ Use --deterministic in CI and automation to ensure reproducible output
  • ✅ Run on the final draft, not intermediate iterations
  • ✅ Combine with the avoid-ai-writing skill for both generation-time guidance and post-processing
  • ❌ Don't run on code files — unslop targets prose, not source code
  • ❌ Don't skip review after unslop: automated cleanup can occasionally change meaning; read the output

Limitations

  • Processes prose only — not code, JSON, or structured data
  • Does not catch factual errors or substantive writing issues
  • Some replacements may not fit every context; review the output before publishing
  • Requires Python tooling such as pipx or uv for standalone CLI installation

Security & Safety Notes

  • unslop reads from stdin and writes to stdout — no file system side effects by default
  • --deterministic mode is local and does not make LLM API calls
  • Default LLM mode may use ANTHROPIC_API_KEY or the Claude CLI; use --deterministic for sensitive local files and CI gates
  • Safe to run in CI pipelines and commit hooks when pinned to deterministic mode

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

See all skills by bouclem

Report a problem with this skill