Release v1.8.0: Add transcript-fixer skill

## New Skill: transcript-fixer v1.0.0

Correct speech-to-text (ASR/STT) transcription errors through dictionary-based rules and AI-powered corrections with automatic pattern learning.

**Features:**
- Two-stage correction pipeline (dictionary + AI)
- Automatic pattern detection and learning
- Domain-specific dictionaries (general, embodied_ai, finance, medical)
- SQLite-based correction repository
- Team collaboration with import/export
- GLM API integration for AI corrections
- Cost optimization through dictionary promotion

**Use cases:**
- Correcting meeting notes, lecture recordings, or interview transcripts
- Fixing Chinese/English homophone errors and technical terminology
- Building domain-specific correction dictionaries
- Improving transcript accuracy through iterative learning

**Documentation:**
- Complete workflow guides in references/
- SQL query templates
- Troubleshooting guide
- Team collaboration patterns
- API setup instructions

**Marketplace updates:**
- Updated marketplace to v1.8.0
- Added transcript-fixer plugin (category: productivity)
- Updated README.md with skill description and use cases
- Updated CLAUDE.md with skill listing and counts

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

transcript-fixer/scripts/utils/diff_formats/inline_format.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+"""
+Inline diff format generator
+
+SINGLE RESPONSIBILITY: Generate inline diff with change markers
+"""
+
+from __future__ import annotations
+
+import difflib
+
+from .text_splitter import split_into_words
+
+
+def generate_inline_diff(original: str, fixed: str) -> str:
+    """
+    Generate inline diff marking deletions and additions
+
+    Format:
+        - Normal words: unchanged
+        - Deletions: [-word-]
+        - Additions: [+word+]
+
+    Args:
+        original: Original text
+        fixed: Fixed text
+
+    Returns:
+        Inline diff string with markers
+    """
+    original_words = split_into_words(original)
+    fixed_words = split_into_words(fixed)
+
+    diff = difflib.ndiff(original_words, fixed_words)
+
+    result = []
+    result.append("=" * 80)
+    result.append("行内词语级别对比 (- 删除, + 添加, ? 修改标记)")
+    result.append("=" * 80)
+    result.append("")
+
+    current_line = []
+    for item in diff:
+        marker = item[0]
+        word = item[2:]
+
+        if marker == ' ':
+            current_line.append(word)
+        elif marker == '-':
+            current_line.append(f"[-{word}-]")
+        elif marker == '+':
+            current_line.append(f"[+{word}+]")
+        elif marker == '?':
+            # Skip change marker lines
+            continue
+
+        # Wrap at 80 characters
+        if len(''.join(current_line)) > 80:
+            result.append(''.join(current_line))
+            current_line = []
+
+    if current_line:
+        result.append(''.join(current_line))
+
+    return '\n'.join(result)