How to Eliminate Vague Declaratives in AI-Generated Writing Using Stop Slop

The stop-slop repository provides a Claude-compatible skill that automatically detects and removes vague declaratives—sentences that assert importance without concrete subjects—by enforcing specific transformation rules and maintaining curated blocklists of prohibited phrases.

Dealing with vague declaratives in AI-generated writing requires precise pattern detection and corrective guidance. The hardikpandya/stop-slop repository implements a lightweight, modular system that integrates directly into Claude workflows to identify and eliminate these ambiguous assertions. By combining declarative rules with reference data, this skill ensures AI outputs specify exact subjects rather than hiding behind abstract generalizations.

What Are Vague Declaratives?

Vague declaratives are sentences that claim significance, depth, or complexity without naming the concrete subject responsible for those qualities. These constructions frequently appear in AI-generated text as filler that sounds authoritative while conveying little information.

Common patterns include assertions like "The reasons are structural," "The stakes are high," or "The implications are significant." These phrases trigger reader skepticism because they rely on nominalizations and empty subjects rather than specific actors, metrics, or causal mechanisms.

Core Architecture of the Stop Slop Skill

The stop-slop implementation follows a deliberately lightweight three-tier architecture designed for immediate deployment in Claude environments.

SKILL.md and the Core Rules

The SKILL.md file serves as the primary configuration and instruction set. It declares the skill name, description, and core rules that drive transformation logic.

Rule 4 ("Be specific") specifically targets vague declaratives and instructs the model to replace them with concrete details. This rule operates as the primary filter during the editing phase, flagging any sentence that asserts importance without providing measurable evidence or named entities.

Reference Data Inventories

Two supplemental directories contain the actual detection patterns:

• references/phrases.md – Maintains the complete blocklist of banned expressions, including the dedicated Vague Declaratives section that enumerates exact string patterns to eliminate (e.g., phrases beginning with "The reasons are..." or "The stakes...").

• references/structures.md – Catalogs higher-level structural clichés that often harbor vague statements, targeting sentence constructions that obscure agency or specificity.

These markdown files function as living documentation that Claude references during the revision cycle, allowing precise matching against known problematic patterns.

Integration Methods for Claude Workflows

The skill supports multiple integration patterns depending on your deployment environment.

Claude Code Integration

For local Claude projects, add the skill folder to enable automatic rule loading:

# Clone the skill folder into your Claude project workspace

git clone https://github.com/hardikpandya/stop-slop.git ./skills/stop-slop

# Register the skill with Claude Code

clause set-skill ./skills/stop-slop/SKILL.md

Once registered, Claude Code automatically applies the core rules during editing sessions, checking draft content against the references/phrases.md blocklist.

System Prompt Embedding

For API usage or custom prompt engineering, embed the skill definition directly:

You are using the Stop Slop skill. Apply all core rules from https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md.
If you encounter a vague declarative such as “The implications are significant”, replace it with a concrete statement (e.g., “The policy increases revenue by 12%”).

This approach loads the rule logic without requiring file system access, making it suitable for serverless environments or third-party Claude integrations.

Programmatic Reference Loading

Access the raw phrase inventories programmatically to build custom linting tools:

import requests

# Load the phrases file directly from GitHub

url = "https://raw.githubusercontent.com/hardikpandya/stop-slop/main/references/phrases.md"
phrases_md = requests.get(url).text

# Extract the vague declarative list

vague_lines = [line for line in phrases_md.splitlines()
               if line.startswith("- \"The")]
print("Vague declaratives to watch for:", vague_lines)

This pattern enables CI/CD pipelines to validate content against the same standards used by the Claude skill.

Validation and Quality Assurance

After transformation, the skill enforces consistency through structured self-audits. The Quick Checks table in SKILL.md prompts a binary verification: "Vague declarative?"

When this check returns positive, the writer must supply the missing concrete element—transforming "The reasons are structural" into "The database schema lacks foreign key constraints, causing orphaned records." This feedback loop prevents regression between drafts and maintains specificity standards across AI-generated content.

Summary

• Vague declaratives assert importance without naming concrete subjects, undermining AI-generated credibility.
• The stop-slop skill uses SKILL.md to enforce Rule 4 ("Be specific") and eliminate these patterns.
• references/phrases.md and references/structures.md provide machine-readable blocklists of prohibited constructions.
• Integration options include Claude Code native loading, system prompt embedding, and programmatic API access.
• The Quick Checks validation table ensures post-transformation quality control.

Frequently Asked Questions

How does stop-slop define a vague declarative specifically?

According to the repository's references/phrases.md, a vague declarative is any sentence beginning with determiner phrases like "The reasons are," "The stakes are," or "The implications are" followed by abstract adjectives without concrete referents. The skill flags these patterns and demands replacement with specific subjects and measurable outcomes.

Can I use stop-slop with AI models other than Claude?

While the skill format is optimized for Claude's skill framework, the underlying rules in SKILL.md and the pattern lists in references/phrases.md are platform-agnostic. You can embed the rule descriptions in GPT-4, Llama, or other system prompts, though you may need to adapt the loading mechanism for non-Claude environments.

What happens if a sentence contains both a vague declarative and valid technical information?

The skill's transformation logic prioritizes specificity over preservation. When Rule 4 triggers on a vague declarative, the model must extract the valid technical information and reconstruct the sentence with explicit subjects. For example, "The structural issues cause failures" becomes "The missing index constraints cause query timeouts exceeding 30 seconds."

Where can I find before-and-after examples of these transformations?

The references/examples.md file contains a curated set of before-and-after transformations illustrating how the skill handles vague declaratives in context. These examples demonstrate the specific-to-abstract conversion patterns that distinguish high-quality AI writing from generic slop.

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