Review AI Writing

Detect AI-generated writing patterns across developer text artifacts using parallel subagents.

Usage

/beagle-docs:review-ai-writing [--all] [--category <name>] [path]

Flags:

• --all

  - Scan entire codebase (default: changed files from main)

• --category <name>

  - Only check specific category:

  content|vocabulary|formatting|communication|filler|code_docs

• Path: Target directory (default: current working directory)

Instructions

1. Parse Arguments

Extract flags from

$ARGUMENTS

• --all

  - Full codebase scan

• --category <name>

  - Filter to specific category
• Path - Target directory

2. Load Skills

Load required skills:

Skill(skill: "beagle-docs:review-ai-writing")
Skill(skill: "beagle-core:review-verification-protocol")

3. Determine Scope

# Default: changed files from main
git diff --name-only $(git merge-base HEAD main)..HEAD

# If --all flag: scan all text artifacts
find . -type f \( -name "*.md" -o -name "*.py" -o -name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.jsx" -o -name "*.go" -o -name "*.rs" -o -name "*.java" -o -name "*.rb" -o -name "*.swift" -o -name "*.kt" -o -name "*.ex" -o -name "*.exs" \) ! -path "*/node_modules/*" ! -path "*/.git/*" ! -path "*/vendor/*" ! -path "*/__pycache__/*" ! -path "*/dist/*" ! -path "*/build/*"

If no files found, exit with: "No files to scan. Check your branch has changes or use --all."

4. Check for Existing LLM Artifacts Review

# Check if llm-artifacts review exists to avoid double-flagging
if [ -f .beagle/llm-artifacts-review.json ]; then
  echo "Found existing llm-artifacts review — will skip overlapping findings"
fi

Parse existing findings from

.beagle/llm-artifacts-review.json

verbose_comment

over_documentation

5. Classify Files by Type

Partition files into three groups:

*.md

*.py

*.ts

*.tsx

*.js

*.jsx

*.go

*.rs

*.java

*.rb

*.swift

*.kt

*.ex

*.exs

For Git artifacts, collect recent commits:

# Commits on current branch not in main
git log --format="%H %s" $(git merge-base HEAD main)..HEAD

6. Spawn Parallel Subagents

If total items >= 4, spawn up to 3 subagents via

Task

--category

Subagent 1: Prose Agent

Scope: Markdown files only Check: All 6 pattern categories Instructions:

1. Load

   beagle-docs:review-ai-writing

   skill
2. Read each markdown file
3. Scan for all pattern categories
4. Apply false positive checks from the skill
5. Return findings in the structured format

Subagent 2: Code Docs Agent

Scope: Source code files Check: vocabulary, communication, filler, code_docs categories Instructions:

1. Load

   beagle-docs:review-ai-writing

   skill
2. Extract docstrings and comments from each file
3. Scan for applicable pattern categories
4. Skip code itself — only check text in comments and docstrings
5. Return findings in the structured format

Subagent 3: Git Agent

Scope: Commit messages and PR descriptions Check: content, vocabulary, communication, filler categories Instructions:

1. Load

   beagle-docs:review-ai-writing

   skill
2. Read commit messages from the branch
3. If on a PR branch, read the PR description via

   gh pr view --json body

4. Scan for applicable pattern categories
5. Use synthetic paths:

   git:commit:<sha>

   with line 0,

   git:pr:<number>

   with line 0
6. Return findings in the structured format

7. Consolidate Findings

Wait for all subagents to complete, then:

1. Merge all findings into a single list
2. Remove duplicates (same file:line and type)
3. Remove findings that overlap with

   .beagle/llm-artifacts-review.json

4. Assign unique IDs (1, 2, 3...)
5. Group by category for display

8. Write JSON Report

Create

.beagle

mkdir -p .beagle

Write findings to

.beagle/ai-writing-review.json

{
  "version": "1.0.0",
  "created_at": "2025-01-15T10:30:00Z",
  "git_head": "abc1234",
  "scope": "changed",
  "files_scanned": 12,
  "commits_scanned": 5,
  "findings": [
    {
      "id": 1,
      "category": "vocabulary",
      "type": "ai_vocabulary_high",
      "file": "README.md",
      "line": 15,
      "original_text": "This library leverages cutting-edge algorithms to facilitate seamless data processing.",
      "description": "High-signal AI vocabulary: leverage, cutting-edge, facilitate, seamless",
      "suggestion": "This library uses streaming algorithms for fast data processing.",
      "risk": "Low",
      "fix_safety": "Safe",
      "fix_action": "rewrite"
    },
    {
      "id": 2,
      "category": "code_docs",
      "type": "tautological_docstring",
      "file": "src/auth.py",
      "line": 42,
      "original_text": "\"\"\"Get the user by ID.\"\"\"",
      "description": "Docstring restates function name get_user_by_id without adding value",
      "suggestion": "\"\"\"Raises UserNotFound if ID doesn't exist.\"\"\"",
      "risk": "Medium",
      "fix_safety": "Needs review",
      "fix_action": "rewrite"
    },
    {
      "id": 3,
      "category": "communication",
      "type": "chat_leak",
      "file": "git:commit:abc1234",
      "line": 0,
      "original_text": "Certainly! Here's the updated authentication flow",
      "description": "Chat leak in commit message: starts with 'Certainly! Here's'",
      "suggestion": "Update authentication flow",
      "risk": "Low",
      "fix_safety": "Safe",
      "fix_action": "rewrite"
    }
  ],
  "summary": {
    "total": 3,
    "by_category": {
      "vocabulary": 1,
      "code_docs": 1,
      "communication": 1
    },
    "by_risk": {
      "Low": 2,
      "Medium": 1
    },
    "by_fix_safety": {
      "Safe": 2,
      "Needs review": 1
    }
  }
}

9. Display Summary

## AI Writing Review

**Scope:** Changed files from main
**Files scanned:** 12 | **Commits scanned:** 5

### Findings by Category

#### Vocabulary (1 issue)

1. [README.md:15] **AI vocabulary** (Low, Safe)
   - High-signal AI vocabulary: leverage, cutting-edge, facilitate, seamless
   - Suggestion: Rewrite with simple words

#### Code Docs (1 issue)

2. [src/auth.py:42] **Tautological docstring** (Medium, Needs review)
   - Docstring restates function name without adding value
   - Suggestion: Add meaningful information or delete

#### Communication (1 issue)

3. [git:commit:abc1234:0] **Chat leak** (Low, Safe)
   - Commit message starts with "Certainly! Here's"
   - Suggestion: Rewrite as imperative commit message

### Summary Table

| Category | Safe | Needs Review | Total |
|----------|------|--------------|-------|
| Vocabulary | 1 | 0 | 1 |
| Code Docs | 0 | 1 | 1 |
| Communication | 1 | 0 | 1 |
| **Total** | **2** | **1** | **3** |

### Next Steps

- Run `/beagle-docs:humanize-beagle` to apply fixes
- Run `/beagle-docs:humanize-beagle --dry-run` to preview changes first
- Review the JSON report at `.beagle/ai-writing-review.json`

10. Verification

Before completing, verify:

1. JSON validity: Confirm

   .beagle/ai-writing-review.json

   exists and is parseable
2. Subagent success: All spawned subagents completed without errors
3. Git HEAD captured: The

   git_head

   field is non-empty
4. No double-flagging: Cross-check against

   .beagle/llm-artifacts-review.json

   if it exists

# Verify JSON is valid
python3 -c "import json; json.load(open('.beagle/ai-writing-review.json'))" 2>/dev/null && echo "Valid JSON" || echo "Invalid JSON"

If any verification fails, report the error and do not proceed.

Output Format for Each Finding

[FILE:LINE] ISSUE_TITLE
- Category: content | vocabulary | formatting | communication | filler | code_docs
- Type: specific_pattern_name
- Original: "the problematic text"
- Suggestion: "the improved text" or "delete"
- Risk: Low | Medium
- Fix Safety: Safe | Needs review

Rules

• Always load

  beagle-docs:review-ai-writing

  and

  beagle-core:review-verification-protocol

  first
• Use

  Task

  tool for parallel subagents when >= 4 items to scan
• Every finding MUST have file:line reference (use synthetic paths for git artifacts)
• Do not flag false positives listed in the skill
• Do not duplicate findings from

  .beagle/llm-artifacts-review.json

• Create

  .beagle

  directory if needed
• Write JSON report before displaying summary

Reference Material

AI Writing Detection for Developer Text

Detect patterns characteristic of AI-generated text in developer artifacts. These patterns reduce trust, add noise, and obscure meaning.

Pattern Categories

references/content-patterns.md

references/vocabulary-patterns.md

references/formatting-patterns.md

references/communication-patterns.md

references/filler-patterns.md

references/code-docs-patterns.md

Scope

Scan these artifact types:

*.md

*.py

*.ts

*.js

*.go

*.swift

*.rs

*.java

*.kt

*.rb

*.ex

git log

git:commit:<sha>

git:pr:<number>

What NOT to Scan

• Generated code (lock files, compiled output, vendor directories)
• Third-party content (copied license text, vendored docs)
• Code itself (variable names, string literals used programmatically)
• Test fixtures and mock data

Detection Rules

High-Confidence Signals (Always Flag)

These patterns are strong indicators of AI-generated text:

1. Chat leaks — "Certainly!", "I'd be happy to", "Great question!", "Here's" as sentence opener
2. Cutoff disclaimers — "As of my last update", "I cannot guarantee"
3. High-signal AI vocabulary — delve, utilize (as "use"), whilst, harnessing, paradigm, synergy
4. "This noun verbs" in docstrings — "This function calculates", "This method returns"
5. Synthetic openers — "In today's fast-paced", "In the world of"
6. Sycophantic code comments — "Excellent approach!", "Great implementation!"

Medium-Confidence Signals (Flag in Context)

Flag when 2+ appear together or pattern is repeated:

1. Low-signal AI vocabulary clusters — 3+ words from the low-signal list in one section
2. Formulaic structure — Rigid intro-body-conclusion in a README section
3. Heading restatement — First sentence after heading restates the heading
4. Excessive hedging — "might potentially", "could possibly", "it seems like it may"
5. Synonym cycling — Same concept called different names within one section
6. Boldface overuse — More than 30% of sentences contain bold text

Low-Confidence Signals (Note Only)

Mention but don't flag as issues:

1. Emoji in technical docs — May be intentional project style
2. Filler phrases — Some are common in human writing too
3. Generic conclusions — May be appropriate for summary sections
4. Commit inflation — Some teams prefer descriptive commits

False Positive Warnings

Do NOT flag these as AI-generated:

__init__.py

Integration

With

beagle-core:review-verification-protocol

Before reporting any finding:

1. Read the surrounding context (full paragraph or function)
2. Confirm the pattern is AI-characteristic, not just formal writing
3. Check if the project has established conventions that match the pattern
4. Verify the suggestion improves clarity without changing meaning

With

beagle-core:llm-artifacts-detection

Code-level patterns (tautological docstrings, obvious comments) overlap with

llm-artifacts-detection

• review-ai-writing

  focuses on writing style (how it reads)

• llm-artifacts-detection

  focuses on code artifacts (whether it should exist at all)
• If

  .beagle/llm-artifacts-review.json

  exists, skip findings already captured there

Output Format

Report each finding as:

[FILE:LINE] ISSUE_TITLE
- Category: content | vocabulary | formatting | communication | filler | code_docs
- Type: specific_pattern_name
- Original: "the problematic text"
- Suggestion: "the improved text" or "delete"
- Risk: Low | Medium
- Fix Safety: Safe | Needs review

Risk Levels

• Low — Filler phrases, obvious comments, emoji. Removing improves clarity with no meaning change.
• Medium — Vocabulary swaps, structural changes, docstring rewrites. Meaning could shift if done carelessly.

Fix Safety

• Safe — Mechanical replacement or deletion. No judgment needed.
• Needs review — Rewrite requires understanding context. Human should verify the replacement preserves intent.