[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KVZC94REQZ852EJ99ZXATK":3,"skill-same:01M6KVZC94REQZ852EJ99ZXATK":75},{"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":31,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6KVZC94REQZ852EJ99ZXATK","grok-workflows",null,"Prompt templates and workflows for using xAI GrokBot with Nuvel — code, design, research, and automated pipelines.","Folken2\u002Fnuvel","Folken2","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F175213316?v=4","01M6KVPA04RKNPCC04H2G96FAS","Albert Folch","01M6KVPA43VNTA4PXSEB1TCD81",2,"MIT","https:\u002F\u002Fgithub.com\u002FFolken2\u002Fnuvel\u002Fblob\u002Fmain\u002Fplugins\u002Fgrokbot-plugin\u002Fskills\u002Fgrok-workflows\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"workflows","grok","grok workflows","design","research","prompt templates","code","templates","xai","prompt","grokbot","pipelines","with","for","nuvel","2026-11-10 10:12:30",false,"---\nname: grok-workflows\ndescription: Prompt templates and workflows for using xAI GrokBot with Nuvel — code, design, research, and automated pipelines.\n---\n\n# GrokBot Workflows for Nuvel\n\n## Trigger Conditions\n- Code generation with OrgMemory context\n- Architecture research and design\n- Automated code review in CI\u002FCD pipelines\n- DeepResearch tasks with web context\n- Quick prototyping with image analysis for diagrams\n\n## Prerequisites\n- GrokBot configured for Nuvel (`grok-setup` skill)\n- Grok API key (`XAI_API_KEY`) set for automated workflows\n- Nuvel OrgMemory accessible for context gathering\n- Helper scripts from `grok-setup` available\n\n## Workflow Templates\n\n### Workflow 1: OrgMemory-Grounded Code Generation\n\n**When to use:** Implementing features with documented patterns\n\n**Step 1: Prepare Context File**\n```bash\ncat > .grok\u002Forgmemory-context.md \u003C\u003C 'EOF'\n## OrgMemory Context\n\n### Architecture Decisions\n[Paste relevant ADR from Nuvel]\n\n### Coding Standards\n[Paste relevant standards from Nuvel]\n\n### Reference Implementation\n[Describe or paste existing similar code pattern]\nEOF\n```\n\n**Step 2: Use the API Client**\n```python\nfrom grok_client import ask_grok\n\nprompt = \"\"\"\n## Implementation Request\n\nRead the OrgMemory context from .grok\u002Forgmemory-context.md for our standards.\n\n### Task\nImplement [feature description] following our documented patterns.\n\n### Requirements\n- Use the [pattern] from our ADRs\n- Include proper error handling per our standards\n- Write unit tests\n- Add type hints throughout\n\n### Deliverables\n1. Implementation code\n2. Unit tests\n3. Notes for OrgMemory update\n\"\"\"\n\nresult = ask_grok(system_prompt=NUVEL_SYSTEM_PROMPT, user_message=prompt)\nprint(result)\n```\n\n**Step 3: For Web Grok Users**\n\nPaste the combined context + task prompt into the Grok web interface.\n\n---\n\n### Workflow 2: DeepResearch Architecture Analysis\n\n**When to use:** Researching technology choices before recording ADRs\n\n**Step 1: Define Research Scope**\n```bash\ncat > .grok\u002Fresearch-prompt.md \u003C\u003C 'EOF'\n## Architecture Research Task\n\n### Question\nWhat is the best approach for [technical decision] given our constraints?\n\n### Current OrgMemory Context\n**System Requirements:**\n- Scale: [current\u002Ffuture load]\n- Latency: [requirements]\n- Team expertise: [languages\u002Fpatterns team knows]\n\n**Existing Decisions:**\n- ADR-010: We use [current approach] because [reason]\n- ADR-024: All new services must [constraint]\n\n### Research Deliverables\n1. Comparison of 2-3 approaches with trade-offs\n2. Recommendation aligned with existing ADRs\n3. New ADR draft for the chosen approach\n4. Migration complexity estimate if replacing existing system\nEOF\n```\n\n**Step 2: Run Research**\n```python\n# Use DeepSearch mode for web research\nresponse = client.chat.completions.create(\n    model=\"grok-3-deepsearch\",\n    messages=[{\n        \"role\": \"user\",\n        \"content\": open(\".grok\u002Fresearch-prompt.md\").read()\n    }],\n)\nprint(response.choices[0].message.content)\n```\n\n---\n\n### Workflow 3: CI\u002FCD Automated Code Review\n\n**When to use:** Automated first-pass review in CI pipelines\n\n**Step 1: Create Review Script**\n\nSave as `scripts\u002Fgrok-review.py`:\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Automated code review using Grok with Nuvel standards.\"\"\"\nimport os\nimport sys\nimport subprocess\nfrom grok_client import ask_grok\n\ndef get_diff():\n    \"\"\"Get the git diff for the current PR.\"\"\"\n    base = os.environ.get(\"GITHUB_BASE_REF\", \"main\")\n    return subprocess.check_output(\n        [\"git\", \"diff\", f\"origin\u002F{base}...HEAD\"],\n        text=True\n    )\n\ndef get_orgmemory_standards():\n    \"\"\"Fetch relevant standards from OrgMemory context file.\"\"\"\n    standards_file = \".grok\u002Forgmemory-standards.md\"\n    if os.path.exists(standards_file):\n        with open(standards_file) as f:\n            return f.read()\n    return \"No specific standards file found.\"\n\ndef main():\n    diff = get_diff()\n    standards = get_orgmemory_standards()\n    \n    if not diff.strip():\n        print(\"No changes to review.\")\n        sys.exit(0)\n    \n    prompt = f\"\"\"\n## Automated Code Review\n\n### OrgMemory Standards\n{standards}\n\n### Changes to Review\n```diff\n{diff}\n```\n\n### Review Instructions\nCheck the diff against our OrgMemory standards. Focus on:\n1. Standards violations\n2. Security issues\n3. Obvious bugs\n4. Missing error handling\n\n### Output Format\nSTATUS: [PASS|WARN|FAIL]\nFINDINGS:\n- [severity] [file:line] [description]\nSUGGESTIONS:\n- [optional improvement]\n\"\"\"\n    \n    review = ask_grok(NUVEL_SYSTEM_PROMPT, prompt)\n    \n    # Parse status for CI exit code\n    if \"STATUS: FAIL\" in review:\n        print(review)\n        sys.exit(1)\n    elif \"STATUS: WARN\" in review:\n        print(review)\n        sys.exit(0)  # Warning doesn't block\n    else:\n        print(review)\n        sys.exit(0)\n\nif __name__ == \"__main__\":\n    main()\n```\n\n**Step 2: Add to CI Config**\n\nGitHub Actions example:\n```yaml\n# .github\u002Fworkflows\u002Fgrok-review.yml\nname: Grok Code Review\non: [pull_request]\n\njobs:\n  review:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions\u002Fcheckout@v4\n        with:\n          fetch-depth: 0\n      - uses: actions\u002Fsetup-python@v5\n        with:\n          python-version: '3.11'\n      - run: pip install openai\n      - name: Run Grok Review\n        env:\n          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}\n        run: python scripts\u002Fgrok-review.py\n```\n\n---\n\n### Workflow 4: Image-Based Architecture Review\n\n**When to use:** Reviewing architecture diagrams, ERDs, or flowcharts\n\n**Step 1: Export Diagram**\n\nExport your architecture diagram as PNG from your diagramming tool.\n\n**Step 2: Review with Grok**\n\n```python\nimport base64\n\nwith open(\"architecture.png\", \"rb\") as f:\n    image_b64 = base64.b64encode(f.read()).decode()\n\nresponse = client.chat.completions.create(\n    model=\"grok-3\",\n    messages=[{\n        \"role\": \"user\",\n        \"content\": [\n            {\n                \"type\": \"text\",\n                \"text\": \"\"\"Review this architecture diagram against our standards:\n\n### OrgMemory ADRs\n- ADR-012: Services communicate via async messaging, not direct HTTP\n- ADR-018: All databases are accessed through a data service layer\n- ADR-023: Authentication is centralized via Auth Gateway\n\n### Review Questions\n1. Does this diagram violate any of our ADRs?\n2. Are there missing components based on our standards?\n3. What improvements would you suggest?\n4. Flag any single points of failure.\n\nOutput as structured review with specific ADR references.\"\"\"\n            },\n            {\n                \"type\": \"image_url\",\n                \"image_url\": {\"url\": f\"data:image\u002Fpng;base64,{image_b64}\"}\n            }\n        ]\n    }],\n)\nprint(response.choices[0].message.content)\n```\n\n---\n\n## Quick Prompt Templates\n\n### Bug Investigation\n```\nBug: [description]. \nCheck against past incidents in OrgMemory: [paste relevant entries].\nComponent docs: [paste from OrgMemory].\nFind root cause and fix with test.\n```\n\n### API Design\n```\nDesign REST API for [resource].\nFollow API conventions from OrgMemory: [paste standards].\nInclude: endpoints, request\u002Fresponse schemas, error codes, auth requirements.\n```\n\n### Test Generation\n```\nGenerate tests for [function\u002Fcomponent].\nFollow testing patterns from OrgMemory: [paste conventions].\nCover: happy path, edge cases, error conditions, boundary values.\n```\n\n## Pitfalls\n- **DeepSearch cost**: DeepSearch mode may consume more tokens. Use only for research tasks that genuinely need web access.\n- **Image size limits**: Grok has limits on image dimensions and file sizes for analysis. Resize large diagrams before uploading.\n- **CI rate limits**: Automated reviews in CI may hit API rate limits on busy repos. Implement caching or batching for high-PR-volume projects.\n- **Context freshness**: Grok's knowledge cutoff may not include recent framework updates. Supplement with web search or OrgMemory context for recent changes.\n\n## Verification\n1. Generated code compiles and passes tests\n2. DeepSearch results are current and relevant\n3. CI review pipeline runs without errors\n4. Image-based reviews catch ADR violations\n5. All outputs flag OrgMemory update needs where applicable","# GrokBot Workflows for Nuvel\n\n## Trigger Conditions\n- Code generation with OrgMemory context\n- Architecture research and design\n- Automated code review in CI\u002FCD pipelines\n- DeepResearch tasks with web context\n- Quick prototyping with image analysis for diagrams\n\n## Prerequisites\n- GrokBot configured for Nuvel (`grok-setup` skill)\n- Grok API key (`XAI_API_KEY`) set for automated workflows\n- Nuvel OrgMemory accessible for context gathering\n- Helper scripts from `grok-setup` available\n\n## Workflow Templates\n\n### Workflow 1: OrgMemory-Grounded Code Generation\n\n**When to use:** Implementing features with documented patterns\n\n**Step 1: Prepare Context File**\n```bash\ncat > .grok\u002Forgmemory-context.md \u003C\u003C 'EOF'\n## OrgMemory Context\n\n### Architecture Decisions\n[Paste relevant ADR from Nuvel]\n\n### Coding Standards\n[Paste relevant standards from Nuvel]\n\n### Reference Implementation\n[Describe or paste existing similar code pattern]\nEOF\n```\n\n**Step 2: Use the API Client**\n```python\nfrom grok_client import ask_grok\n\nprompt = \"\"\"\n## Implementation Request\n\nRead the OrgMemory context from .grok\u002Forgmemory-context.md for our standards.\n\n### Task\nImplement [feature description] following our documented patterns.\n\n### Requirements\n- Use the [pattern] from our ADRs\n- Include proper error handling per our standards\n- Write unit tests\n- Add type hints throughout\n\n### Deliverables\n1. Implementation code\n2. Unit tests\n3. Notes for OrgMemory update\n\"\"\"\n\nresult = ask_grok(system_prompt=NUVEL_SYSTEM_PROMPT, user_message=prompt)\nprint(result)\n```\n\n**Step 3: For Web Grok Users**\n\nPaste the combined context + task prompt into the Grok web interface.\n\n---\n\n### Workflow 2: DeepResearch Architecture Analysis\n\n**When to use:** Researching technology choices before recording ADRs\n\n**Step 1: Define Research Scope**\n```bash\ncat > .grok\u002Fresearch-prompt.md \u003C\u003C 'EOF'\n## Architecture Research Task\n\n### Question\nWhat is the best approach for [technical decision] given our constraints?\n\n### Current OrgMemory Context\n**System Requirements:**\n- Scale: [current\u002Ffuture load]\n- Latency: [requirements]\n- Team expertise: [languages\u002Fpatterns team knows]\n\n**Existing Decisions:**\n- ADR-010: We use [current approach] because [reason]\n- ADR-024: All new services must [constraint]\n\n### Research Deliverables\n1. Comparison of 2-3 approaches with trade-offs\n2. Recommendation aligned with existing ADRs\n3. New ADR draft for the chosen approach\n4. Migration complexity estimate if replacing existing system\nEOF\n```\n\n**Step 2: Run Research**\n```python\n# Use DeepSearch mode for web research\nresponse = client.chat.completions.create(\n    model=\"grok-3-deepsearch\",\n    messages=[{\n        \"role\": \"user\",\n        \"content\": open(\".grok\u002Fresearch-prompt.md\").read()\n    }],\n)\nprint(response.choices[0].message.content)\n```\n\n---\n\n### Workflow 3: CI\u002FCD Automated Code Review\n\n**When to use:** Automated first-pass review in CI pipelines\n\n**Step 1: Create Review Script**\n\nSave as `scripts\u002Fgrok-review.py`:\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Automated code review using Grok with Nuvel standards.\"\"\"\nimport os\nimport sys\nimport subprocess\nfrom grok_client import ask_grok\n\ndef get_diff():\n    \"\"\"Get the git diff for the current PR.\"\"\"\n    base = os.environ.get(\"GITHUB_BASE_REF\", \"main\")\n    return subprocess.check_output(\n        [\"git\", \"diff\", f\"origin\u002F{base}...HEAD\"],\n        text=True\n    )\n\ndef get_orgmemory_standards():\n    \"\"\"Fetch relevant standards from OrgMemory context file.\"\"\"\n    standards_file = \".grok\u002Forgmemory-standards.md\"\n    if os.path.exists(standards_file):\n        with open(standards_file) as f:\n            return f.read()\n    return \"No specific standards file found.\"\n\ndef main():\n    diff = get_diff()\n    standards = get_orgmemory_standards()\n    \n    if not diff.strip():\n        print(\"No changes to review.\")\n        sys.exit(0)\n    \n    prompt = f\"\"\"\n## Automated Code Review\n\n### OrgMemory Standards\n{standards}\n\n### Changes to Review\n```diff\n{diff}\n```\n\n### Review Instructions\nCheck the diff against our OrgMemory standards. Focus on:\n1. Standards violations\n2. Security issues\n3. Obvious bugs\n4. Missing error handling\n\n### Output Format\nSTATUS: [PASS|WARN|FAIL]\nFINDINGS:\n- [severity] [file:line] [description]\nSUGGESTIONS:\n- [optional improvement]\n\"\"\"\n    \n    review = ask_grok(NUVEL_SYSTEM_PROMPT, prompt)\n    \n    # Parse status for CI exit code\n    if \"STATUS: FAIL\" in review:\n        print(review)\n        sys.exit(1)\n    elif \"STATUS: WARN\" in review:\n        print(review)\n        sys.exit(0)  # Warning doesn't block\n    else:\n        print(review)\n        sys.exit(0)\n\nif __name__ == \"__main__\":\n    main()\n```\n\n**Step 2: Add to CI Config**\n\nGitHub Actions example:\n```yaml\n# .github\u002Fworkflows\u002Fgrok-review.yml\nname: Grok Code Review\non: [pull_request]\n\njobs:\n  review:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions\u002Fcheckout@v4\n        with:\n          fetch-depth: 0\n      - uses: actions\u002Fsetup-python@v5\n        with:\n          python-version: '3.11'\n      - run: pip install openai\n      - name: Run Grok Review\n        env:\n          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}\n        run: python scripts\u002Fgrok-review.py\n```\n\n---\n\n### Workflow 4: Image-Based Architecture Review\n\n**When to use:** Reviewing architecture diagrams, ERDs, or flowcharts\n\n**Step 1: Export Diagram**\n\nExport your architecture diagram as PNG from your diagramming tool.\n\n**Step 2: Review with Grok**\n\n```python\nimport base64\n\nwith open(\"architecture.png\", \"rb\") as f:\n    image_b64 = base64.b64encode(f.read()).decode()\n\nresponse = client.chat.completions.create(\n    model=\"grok-3\",\n    messages=[{\n        \"role\": \"user\",\n        \"content\": [\n            {\n                \"type\": \"text\",\n                \"text\": \"\"\"Review this architecture diagram against our standards:\n\n### OrgMemory ADRs\n- ADR-012: Services communicate via async messaging, not direct HTTP\n- ADR-018: All databases are accessed through a data service layer\n- ADR-023: Authentication is centralized via Auth Gateway\n\n### Review Questions\n1. Does this diagram violate any of our ADRs?\n2. Are there missing components based on our standards?\n3. What improvements would you suggest?\n4. Flag any single points of failure.\n\nOutput as structured review with specific ADR references.\"\"\"\n            },\n            {\n                \"type\": \"image_url\",\n                \"image_url\": {\"url\": f\"data:image\u002Fpng;base64,{image_b64}\"}\n            }\n        ]\n    }],\n)\nprint(response.choices[0].message.content)\n```\n\n---\n\n## Quick Prompt Templates\n\n### Bug Investigation\n```\nBug: [description]. \nCheck against past incidents in OrgMemory: [paste relevant entries].\nComponent docs: [paste from OrgMemory].\nFind root cause and fix with test.\n```\n\n### API Design\n```\nDesign REST API for [resource].\nFollow API conventions from OrgMemory: [paste standards].\nInclude: endpoints, request\u002Fresponse schemas, error codes, auth requirements.\n```\n\n### Test Generation\n```\nGenerate tests for [function\u002Fcomponent].\nFollow testing patterns from OrgMemory: [paste conventions].\nCover: happy path, edge cases, error conditions, boundary values.\n```\n\n## Pitfalls\n- **DeepSearch cost**: DeepSearch mode may consume more tokens. Use only for research tasks that genuinely need web access.\n- **Image size limits**: Grok has limits on image dimensions and file sizes for analysis. Resize large diagrams before uploading.\n- **CI rate limits**: Automated reviews in CI may hit API rate limits on busy repos. Implement caching or batching for high-PR-volume projects.\n- **Context freshness**: Grok's knowledge cutoff may not include recent framework updates. Supplement with web search or OrgMemory context for recent changes.\n\n## Verification\n1. Generated code compiles and passes tests\n2. DeepSearch results are current and relevant\n3. CI review pipeline runs without errors\n4. Image-based reviews catch ADR violations\n5. 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