Review Ai Writing

Detect AI-generated writing patterns in developer text — docs, docstrings, commit messages, PR descriptions, and code comments. Use when reviewing any text artifact for authenticity and clarity, or when the user mentions ai writing, ai-generated or robotic writing, text that sounds like AI or ChatGPT, or writing quality. Builds on the docs-style core writing principles.

Detect AI-generated writing patterns across developer text artifacts, parallelizing across artifact groups when the agent supports it.

Usage

Invoke the review-ai-writing skill with optional flags: 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 the review-verification-protocol skill before reporting findings. The AI-writing pattern catalog lives in this file's Reference Material section and the references/*.md files — read the categories you intend to check.

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 if present. When consolidating, skip any finding where both the file:line and pattern type match an existing llm-artifacts finding (specifically verbose_comment and over_documentation types).

5. Classify Files by Type

Partition files into three groups:

Group	File Types	Patterns to Check
Prose	*.md	All 6 categories
Code Docs	*.py, *.ts, *.tsx, *.js, *.jsx, *.go, *.rs, *.java, *.rb, *.swift, *.kt, *.ex, *.exs	vocabulary, communication, filler, code_docs
Git	Commit messages, PR descriptions	content, vocabulary, communication, filler

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. Scan Each Artifact Group

There are three artifact groups below (Prose, Code Docs, Git). If the agent supports subagents and total items >= 4, dispatch one subagent per in-scope group in parallel (up to 3); otherwise run the same group instructions sequentially yourself — identical output either way. If --category is set, handle only the matching category. Every subagent (or sequential pass) reads this skill's Reference Material and the relevant references/*.md patterns before scanning.

Group 1: Prose

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

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

Group 2: Code Docs

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

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

Group 3: Git

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

1. Read commit messages from the branch
2. If on a PR branch, read the PR description via gh pr view --json body
3. Scan for applicable pattern categories
4. Use synthetic paths: git:commit:<sha> with line 0, git:pr:<number> with line 0
5. 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 directory if it doesn't exist:

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

- Invoke the humanize-beagle skill to apply fixes
- Invoke the humanize-beagle skill with --dry-run to preview changes first
- Review the JSON report at `.beagle/ai-writing-review.json`

10. Verification

Before completing, all of the following must pass (objective checks):

1. JSON file exists and parses: .beagle/ai-writing-review.json is present or you exited at Gate 1 with no scan (then no JSON is required).
2. JSON validity: If the file exists, python3 -c "import json; json.load(open('.beagle/ai-writing-review.json'))" exits 0.
3. Subagent success: If you dispatched subagents, each returned without tool/runtime failure (a failed dispatch = do not write final JSON as if complete).
4. Git HEAD captured: When JSON exists, git_head matches git rev-parse HEAD (non-empty string).
5. No double-flagging: If .beagle/llm-artifacts-review.json exists, no finding duplicates its file:line + overlapping type for the skip rules in §4.

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

If any check 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 read this skill's pattern catalog and load review-verification-protocol first
• If the agent supports subagents, parallelize across artifact groups when >= 4 items to scan; otherwise scan sequentially
• 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

Gates (sequenced pass conditions)

Advance only when each pass condition is satisfied using artifacts (paths, exit codes, parseable output)—not an internal “I checked” claim.

1. Arguments → scope

   • Pass: You can list the concrete paths (or git:commit:<sha> / git:pr:<n>) you will scan. If that set is empty, emit the “No files to scan…” message and do not create .beagle/ai-writing-review.json.

2. Scope → execution

   • Pass: Each of Prose, Code docs, and Git (when in scope) has either completed subagent output or equivalent inline work with the same structured fields per finding.

3. Consolidation → write

   • Pass: Duplicates (same file:line and type) removed; when .beagle/llm-artifacts-review.json exists, overlaps with it skipped per §4; git_head equals the output of git rev-parse HEAD (non-empty).

4. JSON → summary

   • Pass: python3 -c "import json; json.load(open('.beagle/ai-writing-review.json'))" exits 0.

5. Finding → verification protocol

   • Pass: For each reported issue, you can cite the surrounding paragraph or function you used so the flag is evidence-backed (see review-verification-protocol).

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

Category	Reference	Key Signals
Content	references/content-patterns.md	Promotional language, vague authority, formulaic structure, synthetic openers
Vocabulary	references/vocabulary-patterns.md	AI word tiers, copula avoidance, rhetorical devices, synonym cycling, commit inflation
Formatting	references/formatting-patterns.md	Boldface overuse, emoji decoration, heading restatement
Communication	references/communication-patterns.md	Chat leaks, cutoff disclaimers, sycophantic tone, apologetic errors
Filler	references/filler-patterns.md	Filler phrases, excessive hedging, generic conclusions
Code Docs	references/code-docs-patterns.md	Tautological docstrings, narrating obvious code, "This noun verbs", exhaustive enumeration

Scope

Scan these artifact types:

Artifact	File Patterns	Notes
Markdown docs	*.md	READMEs, guides, changelogs
Docstrings	*.py, *.ts, *.js, *.go, *.swift, *.rs, *.java, *.kt, *.rb, *.ex	Language-specific docstring formats
Code comments	Same as docstrings	Inline and block comments
Commit messages	git log output	Use synthetic path git:commit:<sha>
PR descriptions	GitHub PR body	Use synthetic path 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:

Pattern	Why It's Valid
"Ensure" in security docs	Standard term for security requirements
"Comprehensive" in test coverage discussion	Accurate technical descriptor
Formal tone in API reference docs	Expected register for reference material
"Leverage" in financial/business domain code	Domain-specific meaning, not AI filler
Bold formatting in CLI help text	Standard convention
Structured intro paragraphs in RFCs/ADRs	Expected format for these document types
"This module provides" in Python __init__.py	Idiomatic Python module docstring
Rhetorical questions in blog posts	Appropriate for informal content

Integration

With 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 llm-artifacts-detection

Code-level patterns (tautological docstrings, obvious comments) overlap with llm-artifacts-detection's style criteria. When both skills are loaded:

• 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.