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anderskev

Humanize Beagle

by Kevin Anderson · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install humanize-beagle
Description
Rewrite AI-generated developer text to sound human — fix inflated language, filler, tautological docs, and robotic tone. Use after review-ai-writing identifi...
README (SKILL.md)

Humanize

Apply fixes from a previous review-ai-writing run with automatic safe/risky classification.

Usage

/beagle-docs:humanize-beagle [--dry-run] [--all] [--category \x3Cname>]

Flags:

  • --dry-run - Show what would be fixed without changing files
  • --all - Fix entire codebase (runs review with --all first)
  • --category \x3Cname> - Only fix specific category: content|vocabulary|formatting|communication|filler|code_docs

Instructions

Hard gates

Advance past destructive or evidence-bound steps only when each PASS is true (commands and artifacts—not “I checked mentally”):

  1. G1 — Safe to edit filesPASS: git status --porcelain is empty, or git stash push -u -m "beagle-docs: pre-humanize backup" exits 0.
  2. G2 — Review input is real JSON with expected shapePASS: .beagle/ai-writing-review.json exists and the file parses as JSON with a git_head key and a findings value that is an array (possibly empty). Use the jq -e command in step 3, or the same checks with json.load in Python. If this fails, stop with a parse/validation error—do not apply fixes.
  3. G3 — References before rewritesPASS: For each finding you will edit, the references/*.md files required by step 4 for that category/type are read in this session before you change text.
  4. G4 — Per-file validationPASS: Every modified file passes the step 8 check for its type; otherwise run git checkout -- "$file" for that file and do not list it as OK in the summary.
  5. G5 — Delete review file only on full successPASS: Run rm .beagle/ai-writing-review.json only when G4 holds for all files you are keeping unchanged from validation failures (aligns with step 10).

1. Parse Arguments

Extract flags from $ARGUMENTS:

  • --dry-run - Preview mode only
  • --all - Full codebase scan
  • --category \x3Cname> - Filter to specific category

2. Pre-flight Safety Checks

# Check for uncommitted changes
git status --porcelain

If working directory is dirty, warn:

Warning: You have uncommitted changes. Creating a git stash before proceeding.
Run `git stash pop` to restore if needed.

Create stash if dirty:

git stash push -u -m "beagle-docs: pre-humanize backup"

G1 PASS: Either the working tree was already clean, or the stash command exited 0.

3. Load Review Results

Check for existing review file:

cat .beagle/ai-writing-review.json 2>/dev/null

If file missing:

  • If --all flag: Run /beagle-docs:review-ai-writing --all first
  • Otherwise: Fail with: "No review results found. Run /beagle-docs:review-ai-writing first."

If file exists, validate JSON and freshness (G2):

# Required shape: parseable JSON with git_head and findings array (may be empty)
jq -e 'has("git_head") and ((.findings // []) | type == "array")' .beagle/ai-writing-review.json >/dev/null 2>&1 \
  || { echo "Invalid or incompatible ai-writing-review.json"; exit 1; }

# Get stored git HEAD from JSON
stored_head=$(jq -r '.git_head' .beagle/ai-writing-review.json)
current_head=$(git rev-parse HEAD)

if [ "$stored_head" != "$current_head" ]; then
  echo "Warning: Review was run at commit $stored_head, but HEAD is now $current_head"
fi

If stale, prompt: "Review results are stale. Re-run review? (y/n)"

4. Load Reference Material

Read the appropriate reference files based on the findings being fixed:

  • Read references/vocabulary-swaps.md when applying ai_vocabulary_high or ai_vocabulary_low fixes
  • Read references/fix-strategies.md for strategy details and before/after examples for any category
  • Read references/developer-voice.md for tone/register guidance when rewriting prose

Only load what you need — if fixing only vocabulary, skip the voice guide.

5. Filter Findings

If --category is set, filter findings to that category only.

Partition remaining findings by fix_safety:

Safe Fixes (auto-apply):

  • chat_leak - Delete conversational artifacts
  • cutoff_disclaimer - Delete knowledge cutoff references
  • filler_phrase - Delete filler phrases
  • heading_restatement - Delete restating first sentence
  • emoji_decoration - Remove emoji from technical text
  • boldface_overuse - Remove excessive bold formatting
  • ai_vocabulary_high - Swap high-signal AI words
  • narrating_obvious - Delete obvious code comments
  • synthetic_opener - Delete "In today's..." openers
  • sycophantic_tone - Delete or neutralize praise
  • vague_authority - Delete unattributed claims
  • excessive_hedging - Remove qualifiers
  • generic_conclusion - Delete summary padding
  • copula_avoidance - Use "is/are" naturally
  • rhetorical_device - Delete rhetorical questions
  • em_dash_overuse - Replace formulaic em dashes with commas, parentheses, or colons
  • thematic_break - Remove horizontal rules before headings
  • title_case_heading - Convert AI title-case headings to sentence case
  • curly_quotes - Normalize curly quotes/apostrophes to straight
  • negative_parallelism - Delete "Not just X, but also Y" filler constructions
  • challenges_and_prospects - Delete "Despite its... faces challenges..." formulaic wrappers

Needs Review Fixes (require confirmation):

  • promotional_language - Rewrite with specifics
  • formulaic_structure - Restructure sections
  • synonym_cycling - Pick consistent term
  • commit_inflation - Rewrite commit scope
  • tautological_docstring - Rewrite or delete docstring
  • exhaustive_enumeration - Trim parameter docs
  • this_noun_verbs - Rewrite docstring voice
  • ai_vocabulary_low - Reduce cluster density
  • apologetic_error - Rewrite error message
  • rule_of_three - Simplify three-item lists used as filler comprehensiveness
  • inline_header_list - Restructure boldfaced inline-header vertical lists
  • unnecessary_table - Convert small tables to prose
  • regression_to_mean - Restore specific facts replaced by vague praise

6. Apply Safe Fixes

If --dry-run:

## Safe Fixes (would apply automatically)

| # | File | Line | Type | Action |
|---|------|------|------|--------|
| 1 | README.md | 3 | synthetic_opener | Delete "In today's rapidly evolving..." |
| 2 | src/auth.py | 15 | narrating_obvious | Delete "# Check if user exists" |
| 3 | README.md | 42 | ai_vocabulary_high | Replace "utilize" with "use" |
...

Otherwise, apply fixes grouped by file to minimize file I/O:

  1. Sort findings by file, then by line number (descending, to avoid offset drift)
  2. For each file, apply all safe fixes in reverse line order
  3. For git artifacts (git:commit:*, git:pr:*), skip — these can't be auto-fixed. Report them for manual attention.

7. Handle Needs Review Fixes

If --dry-run, list them:

## Needs Review Fixes (would prompt interactively)

| # | File | Line | Type | Original | Suggested |
|---|------|------|------|----------|-----------|
| 4 | README.md | 8 | promotional_language | "powerful, enterprise-grade solution" | "authentication library" |
...

Otherwise, for each fix, prompt interactively:

[README.md:8] Promotional language: "powerful, enterprise-grade solution"
Suggested: "authentication library"
(y)es / (n)o / (e)dit / (s)kip all:

Track user choices:

  • y - Apply this fix as suggested
  • n - Skip this fix
  • e - User provides custom replacement
  • s - Skip all remaining interactive fixes

8. Validate Results

For each modified markdown file, verify basic validity:

# Check for broken markdown (unclosed code blocks, broken links)
# Simple check: matching ``` pairs
grep -c '```' "$file" | awk '{print ($1 % 2 == 0) ? "OK" : "WARNING: odd number of code fences"}'

For modified source files, check syntax is still valid:

Python:

python3 -c "import ast; ast.parse(open('$file').read())"

TypeScript/JavaScript:

npx -y acorn --ecma2020 "$file" > /dev/null 2>&1

If validation fails for any file, revert that file:

git checkout -- "$file"
echo "Reverted $file due to validation failure"

9. Report Results

## Humanize Summary

### Applied Fixes
- [x] README.md:3 - Deleted synthetic opener
- [x] README.md:42 - Replaced "utilize" with "use"
- [x] src/auth.py:15 - Deleted obvious comment

### Interactive Fixes
- [x] README.md:8 - Rewrote promotional language (user approved)
- [ ] docs/guide.md:22 - Skipped by user

### Skipped (Git Artifacts)
- [ ] git:commit:abc1234 - Chat leak in commit message (amend manually)

### Validation
- README.md: OK
- src/auth.py: OK

### Diff Summary
git diff --stat

10. Cleanup

On successful completion (all validations pass):

rm .beagle/ai-writing-review.json

If any validation fails, keep the file and report:

Review file preserved at .beagle/ai-writing-review.json
Fix issues and re-run, or restore with: git stash pop

Core Principles

  1. Delete first, rewrite second. Most AI patterns are padding. Removing them improves the text.
  2. Use simple words. Replace "utilize" with "use", "facilitate" with "help", "implement" with "add".
  3. Keep sentences short. Break compound sentences. One idea per sentence.
  4. Preserve meaning. Never change what the text says, only how it says it.
  5. Match the register. Commit messages are terse. READMEs are conversational. API docs are precise. Read references/developer-voice.md for the full register guide.
  6. Don't overcorrect. A slightly formal sentence is fine. Only fix patterns that read as obviously AI-generated.
  7. Understand regression to the mean. LLMs produce the most statistically likely output. Specific, unusual facts get replaced with generic, positive descriptions. When humanizing, restore specificity — replace vague praise with concrete details.
  8. Score density, not individual words. AI vocabulary words co-occur. One or two may be coincidental; a cluster of 3+ is a strong AI tell.

Example

# Preview all fixes without applying
/beagle-docs:humanize-beagle --dry-run

# Fix only vocabulary issues
/beagle-docs:humanize-beagle --category vocabulary

# Full codebase scan and fix
/beagle-docs:humanize-beagle --all

# Preview filler fixes only
/beagle-docs:humanize-beagle --category filler --dry-run

Rules

  • Always load reference material before applying fixes (step 4); satisfy G3 per finding
  • Never modify files without a clean working tree or a successful stash (G1)
  • Apply safe fixes in reverse line order to avoid offset drift
  • Never auto-fix git artifacts (commits, PRs) — report them for manual action
  • Validate every modified file before considering it done (G4)
  • Revert files that fail validation
  • Do not present the step 9 summary as “complete” until step 8 validation has passed for every file you are keeping
  • Remove .beagle/ai-writing-review.json only after full success (G5); if validation failed partway, keep the file and follow step 10
Usage Guidance
This skill appears to do what it says, but before installing or running it: 1) Confirm git and jq are available in the execution environment (SKILL.md uses both but they aren't declared). 2) Back up your working tree or run in a disposable branch—the skill stashes, edits files, and may delete .beagle/ai-writing-review.json on success. 3) Prefer running with --dry-run first and review the referenced review JSON (.beagle/ai-writing-review.json) and references/*.md to ensure edits will be appropriate. 4) Be aware it depends on the review-ai-writing skill/output; verify that dependency's behavior and outputs. If any of these assumptions (git/jq present, correct review file shape) are false, the skill may fail or behave unexpectedly.
Capability Analysis
Type: OpenClaw Skill Name: humanize-beagle Version: 1.0.1 The humanize-beagle skill is a developer utility designed to refine AI-generated text by applying stylistic fixes and removing robotic patterns. It demonstrates strong safety practices, including mandatory git status checks (G1), JSON schema validation (G2), and post-modification syntax validation for Python and JavaScript/TypeScript files (G4). The logic is transparently focused on local file manipulation and lacks any indicators of data exfiltration, persistence, or malicious execution.
Capability Tags
crypto
Capability Assessment
Purpose & Capability
Name/description (rewrite AI-generated developer text) aligns with the instructions and included references. The declared dependencies include review-ai-writing and docs-style, which make sense. However, the SKILL.md assumes availability of git and jq and a .beagle/ai-writing-review.json file but the skill metadata did not declare any required binaries or runtime tools; that metadata omission is inconsistent.
Instruction Scope
Instructions stay within the stated purpose: they validate a prior review JSON, load local reference files, partition findings by safety, and make edits. There are clear hard gates (git state, JSON validation, per-file validation) limiting destructive action. The skill does instruct file edits, git stash/checkout, and deletion of the review file on success—expected for this kind of automation but potentially impactful.
Install Mechanism
Instruction-only skill with no install spec or remote downloads. No code is written to disk by an installer. This is the lowest-risk install mechanism.
Credentials
The skill requests no environment variables or credentials (good). But it implicitly requires command-line tools (git, jq) and depends on the presence and contents of .beagle/ai-writing-review.json and the local references/*.md files; these runtime assumptions are not documented in the manifest and should be clarified.
Persistence & Privilege
always:false and disable-model-invocation:true reduce autonomous risk. The skill performs local edits and may remove the review file on success, but it does not request persistent presence or modify other skills' configs.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install humanize-beagle
  3. After installation, invoke the skill by name or use /humanize-beagle
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Added "hard gates" section with explicit PASS criteria for file edits, review input validation, reference loading, per-file validation, and safe cleanup. - Clarified JSON validation for input: checks for git_head and findings array using jq or Python, and halts on parse/validation errors (G2). - Required reference files must be read before any rewrites are applied (G3). - Updated cleanup: the review file is only deleted if all validations for modified files pass (G5). - Numerous details elaborated in pre-flight, review file, and validation steps to ensure safer, more predictable operation.
v1.0.0
Initial release of humanize-beagle – rewrites AI-generated developer text to sound natural. - Applies fixes identified by review-ai-writing, auto-classifying as safe or needing review. - Supports selective or full codebase runs, with dry-run previews. - Handles specific categories such as filler, vocabulary, and formatting with targeted reference materials. - Safe issues are auto-fixed; riskier cases prompt interactive approval or edits. - Integrates git safety checks, validation steps, and reverts on failure. - Provides clear, itemized summary of actions and cleans up review files on success.
Metadata
Slug humanize-beagle
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 2
Frequently Asked Questions

What is Humanize Beagle?

Rewrite AI-generated developer text to sound human — fix inflated language, filler, tautological docs, and robotic tone. Use after review-ai-writing identifi... It is an AI Agent Skill for Claude Code / OpenClaw, with 88 downloads so far.

How do I install Humanize Beagle?

Run "/install humanize-beagle" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Humanize Beagle free?

Yes, Humanize Beagle is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Humanize Beagle support?

Humanize Beagle is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Humanize Beagle?

It is built and maintained by Kevin Anderson (@anderskev); the current version is v1.0.1.

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