diff --git a/.claude-plugin/marketplace.json b/.claude-plugin/marketplace.json
index 609421b..f5fe668 100644
--- a/.claude-plugin/marketplace.json
+++ b/.claude-plugin/marketplace.json
@@ -291,7 +291,7 @@
       "description": "Corrects speech-to-text (ASR/STT) transcription errors using dictionary rules and native Claude AI corrections (no API key needed by default). Supports intelligent paragraph breaks, filler word reduction, interactive review, Chinese domain names, and iterative dictionary building. Use when users mention transcript correction, ASR errors, speech-to-text mistakes, homophone errors, or working with transcription files",
       "source": "./",
       "strict": false,
-      "version": "1.3.1",
+      "version": "1.4.0",
       "category": "productivity",
       "keywords": [
         "transcription",
@@ -971,7 +971,7 @@
             "hooks": [
               {
                 "type": "command",
-                "command": "${CLAUDE_PLUGIN_ROOT}/scripts/post_edit_validate.sh"
+                "command": "${CLAUDE_PLUGIN_ROOT}/marketplace-dev/scripts/post_edit_validate.sh"
               }
             ]
           },
@@ -980,7 +980,7 @@
             "hooks": [
               {
                 "type": "command",
-                "command": "${CLAUDE_PLUGIN_ROOT}/scripts/post_edit_sync_check.sh"
+                "command": "${CLAUDE_PLUGIN_ROOT}/marketplace-dev/scripts/post_edit_sync_check.sh"
               }
             ]
           }
@@ -988,4 +988,4 @@
       }
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/transcript-fixer/SKILL.md b/transcript-fixer/SKILL.md
index b03a5ab..55e4043 100644
--- a/transcript-fixer/SKILL.md
+++ b/transcript-fixer/SKILL.md
@@ -17,15 +17,21 @@ All scripts use PEP 723 inline metadata — `uv run` auto-installs dependencies.
 # First time: Initialize database
 uv run scripts/fix_transcription.py --init
 
-# Phase 1: Dictionary corrections (instant, free)
+# Single file
 uv run scripts/fix_transcription.py --input meeting.md --stage 1
+
+# Batch: multiple files in parallel (use shell loop)
+for f in /path/to/*.txt; do
+  uv run scripts/fix_transcription.py --input "$f" --stage 1
+done
 ```
 
 After Stage 1, Claude reads the output and fixes remaining ASR errors natively (no API key needed):
-1. Read Stage 1 output in ~200-line chunks using the Read tool
-2. Identify ASR errors — homophones, garbled terms, broken sentences
-3. Present corrections in a table for user review before applying
-4. Save stable patterns to dictionary for future reuse
+1. Read all Stage 1 outputs — read **entire** transcript before proposing corrections (later context disambiguates earlier errors)
+2. Identify ASR errors — compile all corrections across files
+3. Apply fixes with sed in batch, verify each with diff
+4. Finalize: rename `_stage1.md` → `.md`, delete original `.txt`
+5. Save stable patterns to dictionary for future reuse
 
 See `references/example_session.md` for a concrete input/output walkthrough.
 
@@ -51,6 +57,25 @@ Two-phase pipeline with persistent learning:
 
 **After fixing, always save reusable corrections to dictionary.** This is the skill's core value — see `references/iteration_workflow.md` for the complete checklist.
 
+### Dictionary Addition After Fixing
+
+After native AI correction, review all applied fixes and decide which to save. Use this decision matrix:
+
+| Pattern type | Example | Action |
+|-------------|---------|--------|
+| Non-word → correct term | 克劳锐→Claude, cloucode→Claude Code | ✅ Add (zero false positive risk) |
+| Rare word → correct term | 潜彩→前采, 维星→韦青 | ✅ Add (verify it's not a real word first) |
+| Person/company name ASR error | 宋天航→宋天生, 策马攀山→策马看山 | ✅ Add (stable, unique) |
+| Common word → context word | 争→蒸, 钱财→前采, 报纸→标品 | ❌ Skip (high false positive risk) |
+| Real brand → different brand | Xcode→Claude Code, Clover→Claude | ❌ Skip (real words in other contexts) |
+
+Batch add multiple corrections in one session:
+```bash
+uv run scripts/fix_transcription.py --add "错误1" "正确1" --domain tech
+uv run scripts/fix_transcription.py --add "错误2" "正确2" --domain business
+# Chain with && for efficiency
+```
+
 ## False Positive Prevention
 
 Adding wrong dictionary rules silently corrupts future transcripts. **Read `references/false_positive_guide.md` before adding any correction rule**, especially for short words (≤2 chars) or common Chinese words that appear correctly in normal text.
@@ -59,21 +84,46 @@ Adding wrong dictionary rules silently corrupts future transcripts. **Read `refe
 
 When running inside Claude Code, use Claude's own language understanding for Phase 2:
 
-1. Run Stage 1 (dictionary): `--input file.md --stage 1`
+1. Run Stage 1 (dictionary) on all files (parallel if multiple)
 2. Verify Stage 1 — diff original vs output. If dictionary introduced false positives, work from the **original** file
-3. Read the full text in ~200-line chunks. Read the entire transcript before proposing corrections — later context often disambiguates earlier errors
-4. Identify ASR errors:
-   - Product/tool names: "close code" → "Claude Code", "get Hub" → "GitHub"
-   - Technical terms: "Web coding" → "Vibe Coding", "happy pass" → "happy path"
-   - Homophone errors: "上海文" → "上下文", "分值" → "分支"
-   - English ASR garbling: "Pre top" → "prototype", "rapper" → "repo"
-   - Broken sentences: "很大程。路上" → "很大程度上"
-5. Present corrections in high/medium confidence tables with line numbers
-6. Apply with sed on a copy, verify with diff, replace original
-7. Generate word diff: `uv run scripts/generate_word_diff.py original.md corrected.md diff.html`
-8. Save stable patterns to dictionary
+3. Read **all** Stage 1 outputs fully before proposing any corrections — later context often disambiguates earlier errors. For large files (>10k tokens), read in chunks but finish the entire file before identifying errors
+4. Identify ASR errors per file — classify by confidence:
+   - **High confidence** (apply directly): non-words, obvious garbling, product name variants
+   - **Medium confidence** (present for review): context-dependent homophones, person names
+5. Apply fixes efficiently:
+   - **Global replacements** (unique non-words like "克劳锐"→"Claude"): use `sed -i ''` with `-e` flags, multiple patterns in one command
+   - **Context-dependent** (common words like "争"→"蒸" only in distillation context): use sed with longer context phrases for uniqueness, or Edit tool
+6. Verify with diff: `diff original.txt corrected_stage1.md`
+7. Finalize files: rename `*_stage1.md` → `*.md`, delete original `.txt`
+8. Save stable patterns to dictionary (see "Dictionary Addition" below)
 9. Remove false positives if Stage 1 had any
 
+### Common ASR Error Patterns
+
+AI product names are frequently garbled. These patterns recur across transcripts:
+
+| Correct term | Common ASR variants |
+|-------------|-------------------|
+| Claude | cloud, Clou, calloc, 克劳锐, Clover, color |
+| Claude Code | cloud code, Xcode, call code, cloucode, cloudcode, color code |
+| Claude Agent SDK | cloud agent SDK |
+| Opus | Opaas |
+| Vibe Coding | web coding, Web coding |
+| GitHub | get Hub, Git Hub |
+| prototype | Pre top |
+
+Person names and company names also produce consistent ASR errors across sessions — always add confirmed name corrections to the dictionary.
+
+### Efficient Batch Fix Strategy
+
+When fixing multiple files (e.g., 5 transcripts from one day):
+
+1. **Stage 1 in parallel**: run all files through dictionary at once
+2. **Read all files first**: build a mental model of speakers, topics, and recurring terms before fixing anything
+3. **Compile a global correction list**: many errors repeat across files from the same session (same speakers, same topics)
+4. **Apply global corrections first** (sed with multiple `-e` flags), then per-file context-dependent fixes
+5. **Verify all diffs**, finalize all files, then do one dictionary addition pass
+
 ### Enhanced Capabilities (Native Mode Only)
 
 - **Intelligent paragraph breaks**: Add `\n\n` at logical topic transitions