[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M70H1HZFCCSBZ9BYBFD3GRNF":3,"skill-same:01M70H1HZFCCSBZ9BYBFD3GRNF":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M70H1HZFCCSBZ9BYBFD3GRNF","regex-vs-llm-structured-text",null,"Decision framework for parsing structured text (quizzes, forms, invoices, receipts, tables) with a hybrid regex-first pipeline — regex extraction handles 95%+ cheaply, a confidence scorer flags low-confidence items, and an LLM validator fixes only the edge cases. Use when choosing between regex and LLM for text extraction, building a cheap document parser, or optimizing extraction cost and accuracy.","mdnaimul22\u002Fhuman-skills","mdnaimul22","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F64520358?v=4","01M70FH95MH2H796Q01TYSFZ03","Naimul","01M70FH96798CQR4YHA13F17GA",3,"MIT","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Faffaan-m_ECC\u002Fregex-vs-llm-structured-text\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"text","regex vs llm structured text","structured","regex","when","framework","document parser","hybrid","fixes","validator","invoices","tables","forms","flags","only","parser","decision","receipts","parsing","pipeline","2026-11-10 10:12:30",false,"---\nname: regex-vs-llm-structured-text\ndescription: Decision framework for parsing structured text (quizzes, forms, invoices, receipts, tables) with a hybrid regex-first pipeline — regex extraction handles 95%+ cheaply, a confidence scorer flags low-confidence items, and an LLM validator fixes only the edge cases. Use when choosing between regex and LLM for text extraction, building a cheap document parser, or optimizing extraction cost and accuracy.\nmetadata:\n  origin: ECC\n---\n\n# Regex vs LLM for Structured Text Parsing\n\nA practical decision framework for parsing structured text (quizzes, forms, invoices, documents). The key insight: regex handles 95-98% of cases cheaply and deterministically. Reserve expensive LLM calls for the remaining edge cases.\n\n## When to Activate\n\n- Parsing structured text with repeating patterns (questions, forms, tables)\n- Deciding between regex and LLM for text extraction\n- Building hybrid pipelines that combine both approaches\n- Optimizing cost\u002Faccuracy tradeoffs in text processing\n\n## Decision Framework\n\n```\nIs the text format consistent and repeating?\n├── Yes (>90% follows a pattern) → Start with Regex\n│   ├── Regex handles 95%+ → Done, no LLM needed\n│   └── Regex handles \u003C95% → Add LLM for edge cases only\n└── No (free-form, highly variable) → Use LLM directly\n```\n\n## Architecture Pattern\n\n```\nSource Text\n    │\n    ▼\n[Regex Parser] ─── Extracts structure (95-98% accuracy)\n    │\n    ▼\n[Text Cleaner] ─── Removes noise (markers, page numbers, artifacts)\n    │\n    ▼\n[Confidence Scorer] ─── Flags low-confidence extractions\n    │\n    ├── High confidence (≥0.95) → Direct output\n    │\n    └── Low confidence (\u003C0.95) → [LLM Validator] → Output\n```\n\n## Implementation\n\n### 1. Regex Parser (Handles the Majority)\n\n```python\nimport re\nfrom dataclasses import dataclass\n\n@dataclass(frozen=True)\nclass ParsedItem:\n    id: str\n    text: str\n    choices: tuple[str, ...]\n    answer: str\n    confidence: float = 1.0\n\ndef parse_structured_text(content: str) -> list[ParsedItem]:\n    \"\"\"Parse structured text using regex patterns.\"\"\"\n    pattern = re.compile(\n        r\"(?P\u003Cid>\\d+)\\.\\s*(?P\u003Ctext>.+?)\\n\"\n        r\"(?P\u003Cchoices>(?:[A-D]\\..+?\\n)+)\"\n        r\"Answer:\\s*(?P\u003Canswer>[A-D])\",\n        re.MULTILINE | re.DOTALL,\n    )\n    items = []\n    for match in pattern.finditer(content):\n        choices = tuple(\n            c.strip() for c in re.findall(r\"[A-D]\\.\\s*(.+)\", match.group(\"choices\"))\n        )\n        items.append(ParsedItem(\n            id=match.group(\"id\"),\n            text=match.group(\"text\").strip(),\n            choices=choices,\n            answer=match.group(\"answer\"),\n        ))\n    return items\n```\n\n### 2. Confidence Scoring\n\nFlag items that may need LLM review:\n\n```python\n@dataclass(frozen=True)\nclass ConfidenceFlag:\n    item_id: str\n    score: float\n    reasons: tuple[str, ...]\n\ndef score_confidence(item: ParsedItem) -> ConfidenceFlag:\n    \"\"\"Score extraction confidence and flag issues.\"\"\"\n    reasons = []\n    score = 1.0\n\n    if len(item.choices) \u003C 3:\n        reasons.append(\"few_choices\")\n        score -= 0.3\n\n    if not item.answer:\n        reasons.append(\"missing_answer\")\n        score -= 0.5\n\n    if len(item.text) \u003C 10:\n        reasons.append(\"short_text\")\n        score -= 0.2\n\n    return ConfidenceFlag(\n        item_id=item.id,\n        score=max(0.0, score),\n        reasons=tuple(reasons),\n    )\n\ndef identify_low_confidence(\n    items: list[ParsedItem],\n    threshold: float = 0.95,\n) -> list[ConfidenceFlag]:\n    \"\"\"Return items below confidence threshold.\"\"\"\n    flags = [score_confidence(item) for item in items]\n    return [f for f in flags if f.score \u003C threshold]\n```\n\n### 3. LLM Validator (Edge Cases Only)\n\n```python\ndef validate_with_llm(\n    item: ParsedItem,\n    original_text: str,\n    client,\n) -> ParsedItem:\n    \"\"\"Use LLM to fix low-confidence extractions.\"\"\"\n    response = client.messages.create(\n        model=\"claude-haiku-4-5-20251001\",  # Cheapest model for validation\n        max_tokens=500,\n        messages=[{\n            \"role\": \"user\",\n            \"content\": (\n                f\"Extract the question, choices, and answer from this text.\\n\\n\"\n                f\"Text: {original_text}\\n\\n\"\n                f\"Current extraction: {item}\\n\\n\"\n                f\"Return corrected JSON if needed, or 'CORRECT' if accurate.\"\n            ),\n        }],\n    )\n    # Parse LLM response and return corrected item...\n    return corrected_item\n```\n\n### 4. Hybrid Pipeline\n\n```python\ndef process_document(\n    content: str,\n    *,\n    llm_client=None,\n    confidence_threshold: float = 0.95,\n) -> list[ParsedItem]:\n    \"\"\"Full pipeline: regex -> confidence check -> LLM for edge cases.\"\"\"\n    # Step 1: Regex extraction (handles 95-98%)\n    items = parse_structured_text(content)\n\n    # Step 2: Confidence scoring\n    low_confidence = identify_low_confidence(items, confidence_threshold)\n\n    if not low_confidence or llm_client is None:\n        return items\n\n    # Step 3: LLM validation (only for flagged items)\n    low_conf_ids = {f.item_id for f in low_confidence}\n    result = []\n    for item in items:\n        if item.id in low_conf_ids:\n            result.append(validate_with_llm(item, content, llm_client))\n        else:\n            result.append(item)\n\n    return result\n```\n\n## Real-World Metrics\n\nFrom a production quiz parsing pipeline (410 items):\n\n| Metric | Value |\n|--------|-------|\n| Regex success rate | 98.0% |\n| Low confidence items | 8 (2.0%) |\n| LLM calls needed | ~5 |\n| Cost savings vs all-LLM | ~95% |\n| Test coverage | 93% |\n\n## Best Practices\n\n- **Start with regex** — even imperfect regex gives you a baseline to improve\n- **Use confidence scoring** to programmatically identify what needs LLM help\n- **Use the cheapest LLM** for validation (Haiku-class models are sufficient)\n- **Never mutate** parsed items — return new instances from cleaning\u002Fvalidation steps\n- **TDD works well** for parsers — write tests for known patterns first, then edge cases\n- **Log metrics** (regex success rate, LLM call count) to track pipeline health\n\n## Anti-Patterns to Avoid\n\n- Sending all text to an LLM when regex handles 95%+ of cases (expensive and slow)\n- Using regex for free-form, highly variable text (LLM is better here)\n- Skipping confidence scoring and hoping regex \"just works\"\n- Mutating parsed objects during cleaning\u002Fvalidation steps\n- Not testing edge cases (malformed input, missing fields, encoding issues)\n\n## When to Use\n\n- Quiz\u002Fexam question parsing\n- Form data extraction\n- Invoice\u002Freceipt processing\n- Document structure parsing (headers, sections, tables)\n- Any structured text with repeating patterns where cost matters\n","# Regex vs LLM for Structured Text Parsing\n\nA practical decision framework for parsing structured text (quizzes, forms, invoices, documents). The key insight: regex handles 95-98% of cases cheaply and deterministically. Reserve expensive LLM calls for the remaining edge cases.\n\n## When to Activate\n\n- Parsing structured text with repeating patterns (questions, forms, tables)\n- Deciding between regex and LLM for text extraction\n- Building hybrid pipelines that combine both approaches\n- Optimizing cost\u002Faccuracy tradeoffs in text processing\n\n## Decision Framework\n\n```\nIs the text format consistent and repeating?\n├── Yes (>90% follows a pattern) → Start with Regex\n│   ├── Regex handles 95%+ → Done, no LLM needed\n│   └── Regex handles \u003C95% → Add LLM for edge cases only\n└── No (free-form, highly variable) → Use LLM directly\n```\n\n## Architecture Pattern\n\n```\nSource Text\n    │\n    ▼\n[Regex Parser] ─── Extracts structure (95-98% accuracy)\n    │\n    ▼\n[Text Cleaner] ─── Removes noise (markers, page numbers, artifacts)\n    │\n    ▼\n[Confidence Scorer] ─── Flags low-confidence extractions\n    │\n    ├── High confidence (≥0.95) → Direct output\n    │\n    └── Low confidence (\u003C0.95) → [LLM Validator] → Output\n```\n\n## Implementation\n\n### 1. Regex Parser (Handles the Majority)\n\n```python\nimport re\nfrom dataclasses import dataclass\n\n@dataclass(frozen=True)\nclass ParsedItem:\n    id: str\n    text: str\n    choices: tuple[str, ...]\n    answer: str\n    confidence: float = 1.0\n\ndef parse_structured_text(content: str) -> list[ParsedItem]:\n    \"\"\"Parse structured text using regex patterns.\"\"\"\n    pattern = re.compile(\n        r\"(?P\u003Cid>\\d+)\\.\\s*(?P\u003Ctext>.+?)\\n\"\n        r\"(?P\u003Cchoices>(?:[A-D]\\..+?\\n)+)\"\n        r\"Answer:\\s*(?P\u003Canswer>[A-D])\",\n        re.MULTILINE | re.DOTALL,\n    )\n    items = []\n    for match in pattern.finditer(content):\n        choices = tuple(\n            c.strip() for c in re.findall(r\"[A-D]\\.\\s*(.+)\", match.group(\"choices\"))\n        )\n        items.append(ParsedItem(\n            id=match.group(\"id\"),\n            text=match.group(\"text\").strip(),\n            choices=choices,\n            answer=match.group(\"answer\"),\n        ))\n    return items\n```\n\n### 2. Confidence Scoring\n\nFlag items that may need LLM review:\n\n```python\n@dataclass(frozen=True)\nclass ConfidenceFlag:\n    item_id: str\n    score: float\n    reasons: tuple[str, ...]\n\ndef score_confidence(item: ParsedItem) -> ConfidenceFlag:\n    \"\"\"Score extraction confidence and flag issues.\"\"\"\n    reasons = []\n    score = 1.0\n\n    if len(item.choices) \u003C 3:\n        reasons.append(\"few_choices\")\n        score -= 0.3\n\n    if not item.answer:\n        reasons.append(\"missing_answer\")\n        score -= 0.5\n\n    if len(item.text) \u003C 10:\n        reasons.append(\"short_text\")\n        score -= 0.2\n\n    return ConfidenceFlag(\n        item_id=item.id,\n        score=max(0.0, score),\n        reasons=tuple(reasons),\n    )\n\ndef identify_low_confidence(\n    items: list[ParsedItem],\n    threshold: float = 0.95,\n) -> list[ConfidenceFlag]:\n    \"\"\"Return items below confidence threshold.\"\"\"\n    flags = [score_confidence(item) for item in items]\n    return [f for f in flags if f.score \u003C threshold]\n```\n\n### 3. LLM Validator (Edge Cases Only)\n\n```python\ndef validate_with_llm(\n    item: ParsedItem,\n    original_text: str,\n    client,\n) -> ParsedItem:\n    \"\"\"Use LLM to fix low-confidence extractions.\"\"\"\n    response = client.messages.create(\n        model=\"claude-haiku-4-5-20251001\",  # Cheapest model for validation\n        max_tokens=500,\n        messages=[{\n            \"role\": \"user\",\n            \"content\": (\n                f\"Extract the question, choices, and answer from this text.\\n\\n\"\n                f\"Text: {original_text}\\n\\n\"\n                f\"Current extraction: {item}\\n\\n\"\n                f\"Return corrected JSON if needed, or 'CORRECT' if accurate.\"\n            ),\n        }],\n    )\n    # Parse LLM response and return corrected item...\n    return corrected_item\n```\n\n### 4. Hybrid Pipeline\n\n```python\ndef process_document(\n    content: str,\n    *,\n    llm_client=None,\n    confidence_threshold: float = 0.95,\n) -> list[ParsedItem]:\n    \"\"\"Full pipeline: regex -> confidence check -> LLM for edge cases.\"\"\"\n    # Step 1: Regex extraction (handles 95-98%)\n    items = parse_structured_text(content)\n\n    # Step 2: Confidence scoring\n    low_confidence = identify_low_confidence(items, confidence_threshold)\n\n    if not low_confidence or llm_client is None:\n        return items\n\n    # Step 3: LLM validation (only for flagged items)\n    low_conf_ids = {f.item_id for f in low_confidence}\n    result = []\n    for item in items:\n        if item.id in low_conf_ids:\n            result.append(validate_with_llm(item, content, llm_client))\n        else:\n            result.append(item)\n\n    return result\n```\n\n## Real-World Metrics\n\nFrom a production quiz parsing pipeline (410 items):\n\n| Metric | Value |\n|--------|-------|\n| Regex success rate | 98.0% |\n| Low confidence items | 8 (2.0%) |\n| LLM calls needed | ~5 |\n| Cost savings vs all-LLM | ~95% |\n| Test coverage | 93% |\n\n## Best Practices\n\n- **Start with regex** — even imperfect regex gives you a baseline to improve\n- **Use confidence scoring** to programmatically identify what needs LLM help\n- **Use the cheapest LLM** for validation (Haiku-class models are sufficient)\n- **Never mutate** parsed items — return new instances from cleaning\u002Fvalidation steps\n- **TDD works well** for parsers — write tests for known patterns first, then edge cases\n- **Log metrics** (regex success rate, LLM call count) to track pipeline health\n\n## Anti-Patterns to Avoid\n\n- Sending all text to an LLM when regex handles 95%+ of cases (expensive and slow)\n- Using regex for free-form, highly variable text (LLM is better here)\n- Skipping confidence scoring and hoping regex \"just works\"\n- Mutating parsed objects during cleaning\u002Fvalidation steps\n- Not testing edge cases (malformed input, missing fields, encoding issues)\n\n## When to Use\n\n- Quiz\u002Fexam question parsing\n- Form data extraction\n- Invoice\u002Freceipt processing\n- Document structure parsing (headers, sections, tables)\n- Any structured text with repeating patterns where cost matters\n",{"name":5,"metadata":44,"description":7},{"origin":45},"ECC","main","74715deacdc69119d739f38916f8ee4f0f979ebf","skills\u002Fstorage\u002Faffaan-m_ECC\u002Fregex-vs-llm-structured-text",0,"https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills","https:\u002F\u002Fgithub.com\u002Fmdnaimul22","AI Agents Workflows Automation | Specializing in Python, FastAPI, MCP Agents & Custom ERP.","2026-11-11 21:16:57",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add mdnaimul22\u002Fhuman-skills --skill regex-vs-llm-structured-text",{"tool":60,"command":61},"bunx","bunx skills add mdnaimul22\u002Fhuman-skills --skill regex-vs-llm-structured-text",{"tool":63,"command":64},"pnpm","pnpm dlx skills add mdnaimul22\u002Fhuman-skills --skill regex-vs-llm-structured-text","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Faffaan-m_ECC\u002Fregex-vs-llm-structured-text\u002FSKILL.md",true,[],[69,77,108,130,149,164],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":76},"01M72JT3YP1SQHD0MANGDXQ4RT","higgsfield-websites","Build, edit, and deploy full-stack websites, apps and games via the Higgsfield CLI (`higgsfield website …`). Each is a React 19 + TanStack Start SSR app in one Cloudflare Worker (D1\u002FR2\u002FKV\u002FDO\u002FContainers). THREE product types, picked via `--type` on create: `website` (standalone, no Higgsfield integration — references\u002Fwebsite-flow.md), `app` (Sign in with Higgsfield + fnf SDK, Quanta — references\u002Fapp-flow.md), `game` (realtime multiplayer rooms — references\u002Fgame-flow.md). Routes to the right flow; each carries its own rules and deploy\u002Fpublish gates.\nUse when: \"build me a website\", \"make a landing page\", \"create a web app\", \"build a SaaS dashboard \u002F portfolio\", \"make me a game\", \"deploy this site\", \"publish\". Also owns GAME ART: \"make a spritesheet\", \"tileable texture\", \"animate a 3D character\", game music\u002FSFX — see the game-* references.\nNOT for: single image\u002Fvideo\u002Faudio generation (higgsfield-generate), product photos (higgsfield-product-photoshoot), marketplace cards (higgsfield-marketplace-cards).","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fhiggsfield-ai_skills\u002Fhiggsfield-websites\u002FSKILL.md",[],[],"2026-11-10 22:39:42",{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":99,"publishedAt":39},"01M70JPK8HAXXA85P7Q8NKZZYG","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fwriting-skills\u002FSKILL.md",[83,88,93,96],{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",{"categoryNo":94,"categoryName":95,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":97,"categoryName":98,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000008","测试与质量",[100,101,102,103,104,105,106,23,107],"writing","writing skills","editing","deployment","use","Existing","new","work",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":119,"publishedAt":39},"01M70JP4PHBH1CQVVDWZ94W159","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fwriting-plans\u002FSKILL.md",[114,115,116],{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},{"categoryNo":117,"categoryName":118,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000030","开发工程师",[100,120,121,122,23,123,124,104,125,126,127,128,129],"writing plans","plans","you","code","for","requirements","task","multi","spec","multi-step task",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":136,"publishedAt":39},"01M70JNTYVGXFZT8PASZ844TH8","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fverification-before-completion\u002FSKILL.md",[],[137,138,139,140,141,142,104,143,144,145,23,146,107,147,148],"verification","verification before completion","complete","about","output","assertions","prs","evidence","claim","claims","commands","running",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":156,"publishedAt":39},"01M70JNGVSQDESWN0Q5BPJ875V","using-superpowers","Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fusing-superpowers\u002FSKILL.md",[155],{"categoryNo":94,"categoryName":95,"taxonomyType":86,"taxonomyTypeDesc":87},[157,158,159,160,104,23,161,162,163],"using superpowers","superpowers","conversation","how to","how","find","questions",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":170,"publishedAt":39},"01M70JN57CE588NPYPYTWH1DWG","using-git-worktrees","Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fusing-git-worktrees\u002FSKILL.md",[],[171,172,173,174,175,176,23,107,177,178,179,180,181,182,183,121,104,184,185],"using git worktrees","Using git","git","worktrees","git worktrees","implementation","git worktree","worktree","native","Tools","isolation","fallback","feature","via","workspace"]