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/change_extractor.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""
+Change extraction and summarization
+
+SINGLE RESPONSIBILITY: Extract and summarize changes between text versions
+"""
+
+from __future__ import annotations
+
+import difflib
+
+from .text_splitter import split_into_words
+
+
+def extract_changes(original: str, fixed: str) -> list[dict]:
+    """
+    Extract all changes and return change list
+
+    Args:
+        original: Original text
+        fixed: Fixed text
+
+    Returns:
+        List of change dictionaries with type, context, and content
+    """
+    original_words = split_into_words(original)
+    fixed_words = split_into_words(fixed)
+
+    diff = difflib.SequenceMatcher(None, original_words, fixed_words)
+    changes = []
+
+    for tag, i1, i2, j1, j2 in diff.get_opcodes():
+        if tag == 'replace':
+            original_text = ''.join(original_words[i1:i2])
+            fixed_text = ''.join(fixed_words[j1:j2])
+            changes.append({
+                'type': 'replace',
+                'original': original_text,
+                'fixed': fixed_text,
+                'context_before': ''.join(original_words[max(0, i1-5):i1]),
+                'context_after': ''.join(original_words[i2:min(len(original_words), i2+5)])
+            })
+        elif tag == 'delete':
+            original_text = ''.join(original_words[i1:i2])
+            changes.append({
+                'type': 'delete',
+                'original': original_text,
+                'fixed': '',
+                'context_before': ''.join(original_words[max(0, i1-5):i1]),
+                'context_after': ''.join(original_words[i2:min(len(original_words), i2+5)])
+            })
+        elif tag == 'insert':
+            fixed_text = ''.join(fixed_words[j1:j2])
+            changes.append({
+                'type': 'insert',
+                'original': '',
+                'fixed': fixed_text,
+                'context_before': ''.join(fixed_words[max(0, j1-5):j1]) if j1 > 0 else '',
+                'context_after': ''.join(fixed_words[j2:min(len(fixed_words), j2+5)])
+            })
+
+    return changes
+
+
+def generate_change_summary(changes: list[dict]) -> str:
+    """
+    Generate change summary
+
+    Args:
+        changes: List of change dictionaries
+
+    Returns:
+        Formatted summary string
+    """
+    result = []
+    result.append("=" * 80)
+    result.append(f"修改摘要 (共 {len(changes)} 处修改)")
+    result.append("=" * 80)
+    result.append("")
+
+    for i, change in enumerate(changes, 1):
+        change_type = {
+            'replace': '替换',
+            'delete': '删除',
+            'insert': '添加'
+        }[change['type']]
+
+        result.append(f"[{i}] {change_type}")
+
+        if change['original']:
+            result.append(f"  原文: {change['original']}")
+        if change['fixed']:
+            result.append(f"  修复: {change['fixed']}")
+
+        # Show context
+        context = change['context_before'] + "【修改处】" + change['context_after']
+        if context.strip():
+            result.append(f"  上下文: ...{context}...")
+
+        result.append("")
+
+    return '\n'.join(result)