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Humanize Ai Writing

作者 Kevin Anderson · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install humanize-ai-writing
功能描述
Rewrite AI-generated developer text to sound human — fix inflated language, filler, tautological docs, and robotic tone. Use after review-ai-writing identifi...
使用说明 (SKILL.md)

Humanize

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

Usage

/beagle-docs:humanize-ai-writing [--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

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"

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 freshness:

# 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-ai-writing --dry-run

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

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

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

Rules

  • Always load reference material before applying fixes (step 4)
  • Never modify files without a stash or clean working directory
  • 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
  • Revert files that fail validation
  • Write JSON report before displaying summary
  • Clean up JSON report only on full success
安全使用建议
This skill appears to do what it says: load results from a prior review and rewrite developer-facing prose. Before using it: (1) Ensure git and jq (used in the instructions) are available in your environment — the skill metadata doesn't list them. (2) Run it on a clean branch or commit, or back up uncommitted changes — the skill will auto-create a git stash (it warns, but will push a stash by default). (3) Be prepared to review the interactive prompts for 'needs review' fixes; safe fixes are applied automatically. (4) Confirm you have the upstream 'review-ai-writing' results (.beagle/ai-writing-review.json) or run with --all so the skill can generate them. If you need extra caution, run with --dry-run to see exactly what changes would be made before applying them.
功能分析
Type: OpenClaw Skill Name: humanize-ai-writing Version: 1.0.0 The bundle provides a utility for rewriting AI-generated documentation and code comments to improve readability. It implements defensive practices like checking for uncommitted git changes, using `git stash` for backups, and performing syntax validation (via `python3 -c` and `npx acorn`) on modified files in SKILL.md. No indicators of malicious intent, data exfiltration, or prompt injection were found; the logic is entirely focused on text processing and codebase maintenance.
能力标签
crypto
能力评估
Purpose & Capability
The name/description, declared dependencies (docs-style, review-ai-writing) and the runtime instructions align: the skill expects to consume review-ai-writing results and rewrite files. However, the SKILL.md uses git and jq commands yet the registry metadata lists no required binaries — the skill should declare git and jq (or equivalent) as required tooling.
Instruction Scope
Instructions stay within scope (load review results, read reference docs, apply text fixes). They do perform repository operations: check git status, create a git stash automatically if the working tree is dirty, edit files in-place for 'safe' fixes, and interactively prompt for 'needs review' fixes. There are no network/external endpoints or hidden exfiltration steps in the provided text. Automatic stashing and in-place file edits are legitimate for this task but could be surprising if you have uncommitted work.
Install Mechanism
Instruction-only skill with no install spec or code files — lowest installation risk. Nothing will be written to disk by an installer step beyond what the agent's normal execution does (editing repo files).
Credentials
No environment variables, credentials, or config paths are requested. The requested accesses (reading .beagle/ai-writing-review.json and local repo files) are proportional to the stated purpose.
Persistence & Privilege
always:false and disable-model-invocation:true — the skill will not be force-included or autonomously invoked. It does modify the repository it runs in (stashing and editing files), but it does not request system-wide privileges or change other skills' configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install humanize-ai-writing
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /humanize-ai-writing 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of the "humanize-ai-writing" skill. - Automatically rewrites AI-generated developer docs to remove robotic tone, inflated language, filler, and tautologies. - Integrates with output from "review-ai-writing" to identify and correct problematic writing patterns. - Offers safe/risky fix classification with auto-apply for safe fixes and interactive prompts for riskier changes. - Supports filtering fixes by category and dry run mode for previewing changes. - Performs safety checks, validation, and reports a concise summary of applied and skipped changes.
元数据
Slug humanize-ai-writing
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 0
历史版本数 1
常见问题

Humanize Ai Writing 是什么?

Rewrite AI-generated developer text to sound human — fix inflated language, filler, tautological docs, and robotic tone. Use after review-ai-writing identifi... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 105 次。

如何安装 Humanize Ai Writing?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install humanize-ai-writing」即可一键安装,无需额外配置。

Humanize Ai Writing 是免费的吗?

是的,Humanize Ai Writing 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Humanize Ai Writing 支持哪些平台?

Humanize Ai Writing 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Humanize Ai Writing?

由 Kevin Anderson(@anderskev)开发并维护,当前版本 v1.0.0。

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