Update transcript-fixer guidance and hook paths

.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 @@
       }
     }
   ]
-}
+}

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
+1. Read all Stage 1 outputs — read **entire** transcript before proposing corrections (later context disambiguates earlier errors)
-2. Identify ASR errors — homophones, garbled terms, broken sentences
+2. Identify ASR errors — compile all corrections across files
-3. Present corrections in a table for user review before applying
+3. Apply fixes with sed in batch, verify each with diff
-4. Save stable patterns to dictionary for future reuse
+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
+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:
+4. Identify ASR errors per file — classify by confidence:
-   - Product/tool names: "close code" → "Claude Code", "get Hub" → "GitHub"
+   - **High confidence** (apply directly): non-words, obvious garbling, product name variants
-   - Technical terms: "Web coding" → "Vibe Coding", "happy pass" → "happy path"
+   - **Medium confidence** (present for review): context-dependent homophones, person names
-   - Homophone errors: "上海文" → "上下文", "分值" → "分支"
+5. Apply fixes efficiently:
-   - English ASR garbling: "Pre top" → "prototype", "rapper" → "repo"
+   - **Global replacements** (unique non-words like "克劳锐"→"Claude"): use `sed -i ''` with `-e` flags, multiple patterns in one command
-   - Broken sentences: "很大程。路上" → "很大程度上"
+   - **Context-dependent** (common words like "争"→"蒸" only in distillation context): use sed with longer context phrases for uniqueness, or Edit tool
-5. Present corrections in high/medium confidence tables with line numbers
+6. Verify with diff: `diff original.txt corrected_stage1.md`
-6. Apply with sed on a copy, verify with diff, replace original
+7. Finalize files: rename `*_stage1.md` → `*.md`, delete original `.txt`
-7. Generate word diff: `uv run scripts/generate_word_diff.py original.md corrected.md diff.html`
+8. Save stable patterns to dictionary (see "Dictionary Addition" below)
-8. Save stable patterns to dictionary
 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