[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CXPT3EXD6W2AH0VABN080P":3,"skill-same:01M6CXPT3EXD6W2AH0VABN080P":273},{"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":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":67,"commitSha":68,"skillDirectory":69,"forksCount":70,"repositoryHtmlUrl":71,"repositoryHomepageUrl":72,"ownerHtmlUrl":73,"ownerBlogUrl":74,"ownerDescription":75,"updatedAt":76,"install":77,"resources":90},"01M6CXPT3EXD6W2AH0VABN080P","kaggle",null,"Unified Kaggle skill. Use when the user mentions kaggle, kaggle.com, Kaggle competitions, datasets, models, notebooks, GPUs, TPUs, badges, or anything Kaggle-related. Handles account setup, competition reports, dataset\u002Fmodel downloads, notebook execution, competition submissions, badge collection, and general Kaggle questions.","eric861129\u002FSKILLS_All-in-one","eric861129","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F74950944?v=4","01M6CXHDYEYRPFGGV4EFXCSYCF","ChiYu-Huang","01M6CXHE2J5CN1EBTY0FKV3ZKW",52,"MIT","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FData & Analysis\u002Fkaggle\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[5,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"model","token","competition","notebook","general","reports","use","read","python3","MCP","Bash","api","collection","execution","Tools","downloads","browser","packages","python","2026-11-10 10:12:30",false,"---\nname: kaggle\ndescription: \"Unified Kaggle skill. Use when the user mentions kaggle, kaggle.com, Kaggle competitions, datasets, models, notebooks, GPUs, TPUs, badges, or anything Kaggle-related. Handles account setup, competition reports, dataset\u002Fmodel downloads, notebook execution, competition submissions, badge collection, and general Kaggle questions.\"\nlicense: MIT\ncompatibility: \"Python 3.9+, pip packages kagglehub, kaggle, requests, python-dotenv. Optional: playwright for browser badges. Playwright MCP tools for competition reports.\"\nhomepage: https:\u002F\u002Fgithub.com\u002Fshepsci\u002Fkaggle-skill\nmetadata: {\"author\": \"shepsci\", \"version\": \"1.0.1\", \"primaryEnv\": \"KAGGLE_KEY\", \"openclaw\": {\"requires\": {\"bins\": [\"python3\", \"pip3\"], \"env\": [\"KAGGLE_USERNAME\", \"KAGGLE_KEY\", \"KAGGLE_API_TOKEN\"]}}}\nallowed-tools: Bash Read WebFetch\n---\n\n# Kaggle — Unified Skill\n\nComplete Kaggle integration for any LLM or agentic coding system (Claude Code,\ngemini-cli, Cursor, etc.): account setup, competition reports, dataset\u002Fmodel\ndownloads, notebook execution, competition submissions, badge collection, and\ngeneral Kaggle questions. Four integrated modules working together.\n\n> **Overlap guard:** For hackathon grading evaluation and alignment analysis,\n> use the **kaggle-hackathon-grading** skill instead.\n\n**Network requirements:** outbound HTTPS to `api.kaggle.com`, `www.kaggle.com`,\nand `storage.googleapis.com`.\n\n## Modules\n\n| Module | Purpose |\n|--------|---------|\n| **registration** | Account creation, API key generation, credential storage |\n| **comp-report** | Competition landscape reports with Playwright scraping |\n| **kllm** | Core Kaggle interaction (kagglehub, CLI, MCP, UI) |\n| **badge-collector** | Systematic badge earning across 5 phases |\n\n## Credential Setup\n\n**Always run the credential checker first:**\n\n```bash\npython3 skills\u002Fkaggle\u002Fshared\u002Fcheck_all_credentials.py\n```\n\nThree credential types are needed for full compatibility:\n\n| Variable | Format | Purpose |\n|----------|--------|---------|\n| `KAGGLE_USERNAME` | Kaggle handle | Identity for all tools |\n| `KAGGLE_KEY` | 32-char hex | Legacy key (CLI, kagglehub, most MCP) |\n| `KAGGLE_API_TOKEN` | `KGAT_`-prefixed | Scoped token (some MCP endpoints) |\n\nIf any are missing, follow the registration walkthrough:\n`Read modules\u002Fregistration\u002FREADME.md` for the full step-by-step guide.\n\n**Security:** Never echo, log, or commit actual credential values.\n\n## Module: Registration\n\nWalks users through creating a Kaggle account and generating all three API\ncredentials. Saves to `.env` and `~\u002F.kaggle\u002Fkaggle.json`.\n\nKey commands:\n```bash\npython3 skills\u002Fkaggle\u002Fmodules\u002Fregistration\u002Fscripts\u002Fcheck_registration.py\nbash skills\u002Fkaggle\u002Fmodules\u002Fregistration\u002Fscripts\u002Fsetup_env.sh\n```\n\n`Read modules\u002Fregistration\u002FREADME.md` for the complete walkthrough.\n\n## Module: Competition Reports\n\nGenerates comprehensive landscape reports of recent Kaggle competition activity.\nUses Python API for metadata + Playwright MCP tools for SPA content.\n\n6-step workflow:\n1. Verify credentials\n2. Gather competition list across all categories\n3. Get structured details per competition (files, leaderboard, kernels)\n4. Scrape problem statements, evaluation metrics, writeups via Playwright\n5. Compose markdown report with Methods & Insights analysis\n6. Present inline\n\n```bash\npython3 skills\u002Fkaggle\u002Fmodules\u002Fcomp-report\u002Fscripts\u002Flist_competitions.py --lookback-days 30 --output json\npython3 skills\u002Fkaggle\u002Fmodules\u002Fcomp-report\u002Fscripts\u002Fcompetition_details.py --slug SLUG\n```\n\n`Read modules\u002Fcomp-report\u002FREADME.md` for full details including hackathon handling.\n\n## Module: Kaggle Interaction (kllm)\n\nFour methods to interact with kaggle.com:\n\n| Method | Best For |\n|--------|----------|\n| **kagglehub** | Quick dataset\u002Fmodel download in Python |\n| **kaggle-cli** | Full workflow scripting |\n| **MCP Server** | AI agent integration |\n| **Kaggle UI** | Account setup, verification |\n\nCapability matrix:\n\n| Task | kagglehub | kaggle-cli | MCP | UI |\n|------|-----------|------------|-----|-----|\n| Download dataset | `dataset_download()` | `datasets download` | Yes | Yes |\n| Download model | `model_download()` | `models instances versions download` | Yes | Yes |\n| Execute notebook | — | `kernels push\u002Fstatus\u002Foutput` | Yes | Yes |\n| Submit to competition | — | `competitions submit` | Yes | Yes |\n| Publish dataset | `dataset_upload()` | `datasets create` | Yes | Yes |\n| Publish model | `model_upload()` | `models create` | Yes | Yes |\n\n**Known issues:**\n- `dataset_load()` broken in kagglehub v0.4.3 — use `dataset_download()` + `pd.read_csv()`\n- `competitions download` has no `--unzip` in CLI >= 1.8\n- Competition-linked datasets return 403 — use standalone copies\n\n`Read modules\u002Fkllm\u002FREADME.md` for full details and all task workflows.\n\n## Module: Badge Collector\n\nSystematically earns ~38 automatable Kaggle badges across 5 phases:\n\n| Phase | Name | Badges | Time |\n|-------|------|--------|------|\n| 1 | Instant API | ~16 | 5-10 min |\n| 2 | Competition | ~7 | 10-15 min |\n| 3 | Pipeline | ~3 | 15-30 min |\n| 4 | Browser | ~8 | 5-10 min |\n| 5 | Streaks | ~4 | Setup only |\n\n```bash\npython3 skills\u002Fkaggle\u002Fmodules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py --dry-run\npython3 skills\u002Fkaggle\u002Fmodules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py --phase 1\npython3 skills\u002Fkaggle\u002Fmodules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py --status\n```\n\n`Read modules\u002Fbadge-collector\u002FREADME.md` for full details.\n\n## Orchestration Workflow\n\nThis skill is primarily a **reference** — use the modules and scripts as needed\nbased on the user's request. When explicitly asked to run the **full Kaggle\nworkflow**, follow these steps:\n\n### Step 1: Check Credentials\n\n```bash\npython3 skills\u002Fkaggle\u002Fshared\u002Fcheck_all_credentials.py\n```\n\nIf any credentials are missing, walk through the registration module. **Never\necho or log actual credential values.**\n\n### Step 2: Generate Competition Landscape Report\n\nRun the comp-report workflow: list competitions, get details, scrape with\nPlaywright, compose report. Output inline.\n\n### Step 3: Summarize Kaggle Interaction Methods\n\nPresent a concise summary of the four ways to interact with Kaggle (kagglehub,\nkaggle-cli, MCP Server, UI) with the capability matrix from the kllm module.\n\n### Step 4: Present Interactive Menu\n\nAsk the user what they'd like to do next:\n\n- **Earn Kaggle badges** — Run the badge collector (5 phases, ~38 automatable badges)\n- **Explore recent competitions** — Dive deeper into specific competitions from the report\n- **Enter a Kaggle competition** — Register, download data, build a submission, submit\n- **Download a Kaggle dataset** — Search for and download any public dataset\n- **Download a Kaggle model** — Download pre-trained models (LLMs, CV, etc.)\n- **Run a notebook on Kaggle** — Push and execute a notebook on KKB with free GPU\u002FTPU\n- **Publish to Kaggle** — Upload a dataset, model, or notebook\n- **Learn about Kaggle progression** — Tiers, medals, how to rank up\n- **Something else** — Free-form Kaggle help\n\n### Step 5: Execute and Continue\n\nHandle the user's choice using the appropriate module, then loop back to offer\nmore options.\n\n## Security\n\n- **Never** commit `.env`, `kaggle.json`, or any credential files\n- **Never** echo or log actual credential values in terminal output\n- The `.gitignore` excludes `.env`, `kaggle.json`, and related files\n- Set file permissions: `chmod 600 .env ~\u002F.kaggle\u002Fkaggle.json`\n- If credentials are accidentally exposed, rotate them immediately at\n  [https:\u002F\u002Fwww.kaggle.com\u002Fsettings](https:\u002F\u002Fwww.kaggle.com\u002Fsettings)\n\n## Scope of Operations\n\nThis skill performs both read-only and write operations on kaggle.com.\n\n**Read-only operations** (no account side-effects):\n- List\u002Fsearch competitions, datasets, models, notebooks\n- Download datasets, models, competition data\n- View leaderboards, competition details, badge progress\n- Generate competition landscape reports\n\n**Write operations** (create or modify resources on your account):\n- Create\u002Fpublish datasets, notebooks, models (always private by default)\n- Submit predictions to competitions\n- Push and execute notebooks on Kaggle Kernel Backend (KKB)\n- Earn badges through API activity (profile-visible)\n\n**Phase 5 (Streaks)** generates a local shell script for daily execution but\ndoes **not** auto-install cron jobs or launchd plists. Users must manually\nconfigure scheduling if desired.\n\n## Scripts Index\n\n**Shared:**\n- `shared\u002Fcheck_all_credentials.py` — Unified credential checker (all 3 types)\n\n**Registration:**\n- `modules\u002Fregistration\u002Fscripts\u002Fcheck_registration.py` — Check all 3 credentials\n- `modules\u002Fregistration\u002Fscripts\u002Fsetup_env.sh` — Auto-configure credentials from env\u002Fdotenv\n\n**Competition Reports:**\n- `modules\u002Fcomp-report\u002Fscripts\u002Futils.py` — Credential check, API init, rate limiting\n- `modules\u002Fcomp-report\u002Fscripts\u002Flist_competitions.py` — Fetch competitions across categories\n- `modules\u002Fcomp-report\u002Fscripts\u002Fcompetition_details.py` — Files, leaderboard, kernels per competition\n\n**Kaggle Interaction (kllm):**\n- `modules\u002Fkllm\u002Fscripts\u002Fsetup_env.sh` — Auto-configure credentials (with .env loading)\n- `modules\u002Fkllm\u002Fscripts\u002Fcheck_credentials.py` — Verify and auto-map credentials\n- `modules\u002Fkllm\u002Fscripts\u002Fnetwork_check.sh` — Check Kaggle API reachability\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_download.sh` — Download datasets\u002Fmodels via CLI\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_execute.sh` — Execute notebook on KKB\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_competition.sh` — Competition workflow (list\u002Fdownload\u002Fsubmit)\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_publish.sh` — Publish datasets\u002Fnotebooks\u002Fmodels\n- `modules\u002Fkllm\u002Fscripts\u002Fpoll_kernel.sh` — Poll kernel status and download output\n- `modules\u002Fkllm\u002Fscripts\u002Fkagglehub_download.py` — Download via kagglehub\n- `modules\u002Fkllm\u002Fscripts\u002Fkagglehub_publish.py` — Publish via kagglehub\n\n**Badge Collector:**\n- `modules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py` — Main entry point\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fbadge_registry.py` — 59 badge definitions\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fbadge_tracker.py` — Progress persistence\n- `modules\u002Fbadge-collector\u002Fscripts\u002Futils.py` — Shared utilities\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_1_instant_api.py` — Instant API badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_2_competition.py` — Competition badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_3_pipeline.py` — Pipeline badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_4_browser.py` — Browser badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_5_streaks.py` — Streak automation\n\n## References Index\n\n- `modules\u002Fregistration\u002Freferences\u002Fkaggle-setup.md` — Full credential setup guide with troubleshooting\n- `modules\u002Fcomp-report\u002Freferences\u002Fcompetition-categories.md` — Competition types and API mapping\n- `modules\u002Fkllm\u002Freferences\u002Fkaggle-knowledge.md` — Comprehensive Kaggle platform knowledge\n- `modules\u002Fkllm\u002Freferences\u002Fkagglehub-reference.md` — Full kagglehub Python API reference\n- `modules\u002Fkllm\u002Freferences\u002Fcli-reference.md` — Complete kaggle-cli command reference\n- `modules\u002Fkllm\u002Freferences\u002Fmcp-reference.md` — Kaggle MCP server reference\n- `modules\u002Fbadge-collector\u002Freferences\u002Fbadge-catalog.md` — Complete 59-badge catalog\n","# Kaggle — Unified Skill\n\nComplete Kaggle integration for any LLM or agentic coding system (Claude Code,\ngemini-cli, Cursor, etc.): account setup, competition reports, dataset\u002Fmodel\ndownloads, notebook execution, competition submissions, badge collection, and\ngeneral Kaggle questions. Four integrated modules working together.\n\n> **Overlap guard:** For hackathon grading evaluation and alignment analysis,\n> use the **kaggle-hackathon-grading** skill instead.\n\n**Network requirements:** outbound HTTPS to `api.kaggle.com`, `www.kaggle.com`,\nand `storage.googleapis.com`.\n\n## Modules\n\n| Module | Purpose |\n|--------|---------|\n| **registration** | Account creation, API key generation, credential storage |\n| **comp-report** | Competition landscape reports with Playwright scraping |\n| **kllm** | Core Kaggle interaction (kagglehub, CLI, MCP, UI) |\n| **badge-collector** | Systematic badge earning across 5 phases |\n\n## Credential Setup\n\n**Always run the credential checker first:**\n\n```bash\npython3 skills\u002Fkaggle\u002Fshared\u002Fcheck_all_credentials.py\n```\n\nThree credential types are needed for full compatibility:\n\n| Variable | Format | Purpose |\n|----------|--------|---------|\n| `KAGGLE_USERNAME` | Kaggle handle | Identity for all tools |\n| `KAGGLE_KEY` | 32-char hex | Legacy key (CLI, kagglehub, most MCP) |\n| `KAGGLE_API_TOKEN` | `KGAT_`-prefixed | Scoped token (some MCP endpoints) |\n\nIf any are missing, follow the registration walkthrough:\n`Read modules\u002Fregistration\u002FREADME.md` for the full step-by-step guide.\n\n**Security:** Never echo, log, or commit actual credential values.\n\n## Module: Registration\n\nWalks users through creating a Kaggle account and generating all three API\ncredentials. Saves to `.env` and `~\u002F.kaggle\u002Fkaggle.json`.\n\nKey commands:\n```bash\npython3 skills\u002Fkaggle\u002Fmodules\u002Fregistration\u002Fscripts\u002Fcheck_registration.py\nbash skills\u002Fkaggle\u002Fmodules\u002Fregistration\u002Fscripts\u002Fsetup_env.sh\n```\n\n`Read modules\u002Fregistration\u002FREADME.md` for the complete walkthrough.\n\n## Module: Competition Reports\n\nGenerates comprehensive landscape reports of recent Kaggle competition activity.\nUses Python API for metadata + Playwright MCP tools for SPA content.\n\n6-step workflow:\n1. Verify credentials\n2. Gather competition list across all categories\n3. Get structured details per competition (files, leaderboard, kernels)\n4. Scrape problem statements, evaluation metrics, writeups via Playwright\n5. Compose markdown report with Methods & Insights analysis\n6. Present inline\n\n```bash\npython3 skills\u002Fkaggle\u002Fmodules\u002Fcomp-report\u002Fscripts\u002Flist_competitions.py --lookback-days 30 --output json\npython3 skills\u002Fkaggle\u002Fmodules\u002Fcomp-report\u002Fscripts\u002Fcompetition_details.py --slug SLUG\n```\n\n`Read modules\u002Fcomp-report\u002FREADME.md` for full details including hackathon handling.\n\n## Module: Kaggle Interaction (kllm)\n\nFour methods to interact with kaggle.com:\n\n| Method | Best For |\n|--------|----------|\n| **kagglehub** | Quick dataset\u002Fmodel download in Python |\n| **kaggle-cli** | Full workflow scripting |\n| **MCP Server** | AI agent integration |\n| **Kaggle UI** | Account setup, verification |\n\nCapability matrix:\n\n| Task | kagglehub | kaggle-cli | MCP | UI |\n|------|-----------|------------|-----|-----|\n| Download dataset | `dataset_download()` | `datasets download` | Yes | Yes |\n| Download model | `model_download()` | `models instances versions download` | Yes | Yes |\n| Execute notebook | — | `kernels push\u002Fstatus\u002Foutput` | Yes | Yes |\n| Submit to competition | — | `competitions submit` | Yes | Yes |\n| Publish dataset | `dataset_upload()` | `datasets create` | Yes | Yes |\n| Publish model | `model_upload()` | `models create` | Yes | Yes |\n\n**Known issues:**\n- `dataset_load()` broken in kagglehub v0.4.3 — use `dataset_download()` + `pd.read_csv()`\n- `competitions download` has no `--unzip` in CLI >= 1.8\n- Competition-linked datasets return 403 — use standalone copies\n\n`Read modules\u002Fkllm\u002FREADME.md` for full details and all task workflows.\n\n## Module: Badge Collector\n\nSystematically earns ~38 automatable Kaggle badges across 5 phases:\n\n| Phase | Name | Badges | Time |\n|-------|------|--------|------|\n| 1 | Instant API | ~16 | 5-10 min |\n| 2 | Competition | ~7 | 10-15 min |\n| 3 | Pipeline | ~3 | 15-30 min |\n| 4 | Browser | ~8 | 5-10 min |\n| 5 | Streaks | ~4 | Setup only |\n\n```bash\npython3 skills\u002Fkaggle\u002Fmodules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py --dry-run\npython3 skills\u002Fkaggle\u002Fmodules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py --phase 1\npython3 skills\u002Fkaggle\u002Fmodules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py --status\n```\n\n`Read modules\u002Fbadge-collector\u002FREADME.md` for full details.\n\n## Orchestration Workflow\n\nThis skill is primarily a **reference** — use the modules and scripts as needed\nbased on the user's request. When explicitly asked to run the **full Kaggle\nworkflow**, follow these steps:\n\n### Step 1: Check Credentials\n\n```bash\npython3 skills\u002Fkaggle\u002Fshared\u002Fcheck_all_credentials.py\n```\n\nIf any credentials are missing, walk through the registration module. **Never\necho or log actual credential values.**\n\n### Step 2: Generate Competition Landscape Report\n\nRun the comp-report workflow: list competitions, get details, scrape with\nPlaywright, compose report. Output inline.\n\n### Step 3: Summarize Kaggle Interaction Methods\n\nPresent a concise summary of the four ways to interact with Kaggle (kagglehub,\nkaggle-cli, MCP Server, UI) with the capability matrix from the kllm module.\n\n### Step 4: Present Interactive Menu\n\nAsk the user what they'd like to do next:\n\n- **Earn Kaggle badges** — Run the badge collector (5 phases, ~38 automatable badges)\n- **Explore recent competitions** — Dive deeper into specific competitions from the report\n- **Enter a Kaggle competition** — Register, download data, build a submission, submit\n- **Download a Kaggle dataset** — Search for and download any public dataset\n- **Download a Kaggle model** — Download pre-trained models (LLMs, CV, etc.)\n- **Run a notebook on Kaggle** — Push and execute a notebook on KKB with free GPU\u002FTPU\n- **Publish to Kaggle** — Upload a dataset, model, or notebook\n- **Learn about Kaggle progression** — Tiers, medals, how to rank up\n- **Something else** — Free-form Kaggle help\n\n### Step 5: Execute and Continue\n\nHandle the user's choice using the appropriate module, then loop back to offer\nmore options.\n\n## Security\n\n- **Never** commit `.env`, `kaggle.json`, or any credential files\n- **Never** echo or log actual credential values in terminal output\n- The `.gitignore` excludes `.env`, `kaggle.json`, and related files\n- Set file permissions: `chmod 600 .env ~\u002F.kaggle\u002Fkaggle.json`\n- If credentials are accidentally exposed, rotate them immediately at\n  [https:\u002F\u002Fwww.kaggle.com\u002Fsettings](https:\u002F\u002Fwww.kaggle.com\u002Fsettings)\n\n## Scope of Operations\n\nThis skill performs both read-only and write operations on kaggle.com.\n\n**Read-only operations** (no account side-effects):\n- List\u002Fsearch competitions, datasets, models, notebooks\n- Download datasets, models, competition data\n- View leaderboards, competition details, badge progress\n- Generate competition landscape reports\n\n**Write operations** (create or modify resources on your account):\n- Create\u002Fpublish datasets, notebooks, models (always private by default)\n- Submit predictions to competitions\n- Push and execute notebooks on Kaggle Kernel Backend (KKB)\n- Earn badges through API activity (profile-visible)\n\n**Phase 5 (Streaks)** generates a local shell script for daily execution but\ndoes **not** auto-install cron jobs or launchd plists. Users must manually\nconfigure scheduling if desired.\n\n## Scripts Index\n\n**Shared:**\n- `shared\u002Fcheck_all_credentials.py` — Unified credential checker (all 3 types)\n\n**Registration:**\n- `modules\u002Fregistration\u002Fscripts\u002Fcheck_registration.py` — Check all 3 credentials\n- `modules\u002Fregistration\u002Fscripts\u002Fsetup_env.sh` — Auto-configure credentials from env\u002Fdotenv\n\n**Competition Reports:**\n- `modules\u002Fcomp-report\u002Fscripts\u002Futils.py` — Credential check, API init, rate limiting\n- `modules\u002Fcomp-report\u002Fscripts\u002Flist_competitions.py` — Fetch competitions across categories\n- `modules\u002Fcomp-report\u002Fscripts\u002Fcompetition_details.py` — Files, leaderboard, kernels per competition\n\n**Kaggle Interaction (kllm):**\n- `modules\u002Fkllm\u002Fscripts\u002Fsetup_env.sh` — Auto-configure credentials (with .env loading)\n- `modules\u002Fkllm\u002Fscripts\u002Fcheck_credentials.py` — Verify and auto-map credentials\n- `modules\u002Fkllm\u002Fscripts\u002Fnetwork_check.sh` — Check Kaggle API reachability\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_download.sh` — Download datasets\u002Fmodels via CLI\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_execute.sh` — Execute notebook on KKB\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_competition.sh` — Competition workflow (list\u002Fdownload\u002Fsubmit)\n- `modules\u002Fkllm\u002Fscripts\u002Fcli_publish.sh` — Publish datasets\u002Fnotebooks\u002Fmodels\n- `modules\u002Fkllm\u002Fscripts\u002Fpoll_kernel.sh` — Poll kernel status and download output\n- `modules\u002Fkllm\u002Fscripts\u002Fkagglehub_download.py` — Download via kagglehub\n- `modules\u002Fkllm\u002Fscripts\u002Fkagglehub_publish.py` — Publish via kagglehub\n\n**Badge Collector:**\n- `modules\u002Fbadge-collector\u002Fscripts\u002Forchestrator.py` — Main entry point\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fbadge_registry.py` — 59 badge definitions\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fbadge_tracker.py` — Progress persistence\n- `modules\u002Fbadge-collector\u002Fscripts\u002Futils.py` — Shared utilities\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_1_instant_api.py` — Instant API badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_2_competition.py` — Competition badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_3_pipeline.py` — Pipeline badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_4_browser.py` — Browser badges\n- `modules\u002Fbadge-collector\u002Fscripts\u002Fphase_5_streaks.py` — Streak automation\n\n## References Index\n\n- `modules\u002Fregistration\u002Freferences\u002Fkaggle-setup.md` — Full credential setup guide with troubleshooting\n- `modules\u002Fcomp-report\u002Freferences\u002Fcompetition-categories.md` — Competition types and API mapping\n- `modules\u002Fkllm\u002Freferences\u002Fkaggle-knowledge.md` — Comprehensive Kaggle platform knowledge\n- `modules\u002Fkllm\u002Freferences\u002Fkagglehub-reference.md` — Full kagglehub Python API reference\n- `modules\u002Fkllm\u002Freferences\u002Fcli-reference.md` — Complete kaggle-cli command reference\n- `modules\u002Fkllm\u002Freferences\u002Fmcp-reference.md` — Kaggle MCP server reference\n- `modules\u002Fbadge-collector\u002Freferences\u002Fbadge-catalog.md` — Complete 59-badge catalog\n",{"name":5,"license":15,"homepage":53,"metadata":54,"description":7,"allowed-tools":65,"compatibility":66},"https:\u002F\u002Fgithub.com\u002Fshepsci\u002Fkaggle-skill",{"author":55,"version":56,"openclaw":57,"primaryEnv":61},"shepsci","1.0.1",{"requires":58},{"env":59,"bins":63},[60,61,62],"KAGGLE_USERNAME","KAGGLE_KEY","KAGGLE_API_TOKEN",[37,64],"pip3","Bash Read WebFetch","Python 3.9+, pip packages kagglehub, kaggle, requests, python-dotenv. 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Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fwhat-if-oracle\u002FSKILL.md",[280],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[282,283,284,285,286,287,288,289,290,291,292,293,294,36,295,296,297,298,299,300],"oracle","what if oracle","go","run","planning","ux","futures","risk","analysis","explore","decision","branch","question","write","idea","exploration","options","stress-test","strategies",{"skillNo":302,"name":303,"nameZh":6,"description":304,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":305,"categories":306,"keywords":317,"publishedAt":48},"01M6CZ1QHGJE91A95MSN5EK5Q9","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fvenue-templates\u002FSKILL.md",[307,310,313,314],{"categoryNo":308,"categoryName":309,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":311,"categoryName":312,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":315,"categoryName":316,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[318,319,320,321,322,323,324,325,39,326,36,295,327,328,329,330],"templates","venue templates","research","guidelines","NeurIPS","ICML","LaTeX","science","access","edit","proposals","Academic","requirements",{"skillNo":332,"name":333,"nameZh":6,"description":334,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":335,"categories":336,"keywords":339,"publishedAt":48},"01M6CZ1KBQD2E5RTFB3BGVX7CD","the-fool","Use when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fthe-fool\u002FSKILL.md",[337,338],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[340,341,284,342,343,285,344,345,346,347,348,349,350,351,328,352,353,354,355,356],"the fool","review","workflow","architecture","code","audit","forge","devil","team","designer","reviewer","challenge","stress test","evidence","expert","poke","ideas",{"skillNo":358,"name":359,"nameZh":6,"description":360,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":361,"categories":362,"keywords":366,"publishedAt":48},"01M6CZ1ES826S7P1X3333E40TA","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fscientific-writing\u002FSKILL.md",[363,364,365],{"categoryNo":308,"categoryName":309,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":315,"categoryName":316,"taxonomyType":26,"taxonomyTypeDesc":27},[367,368,321,369,370,371,372,373,39,374,36,295,327,35,375,376,377,378,379,380],"writing","scientific writing","core","reporting","deep research","Citations","Outlines","tool","never","structure","deep","full","stage","prisma",{"skillNo":382,"name":383,"nameZh":6,"description":384,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":385,"categories":386,"keywords":388,"publishedAt":48},"01M6CZ1B2JGJTB9FRD0TW4RXXE","scientific-brainstorming","Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fscientific-brainstorming\u002FSKILL.md",[387],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[389,390,320,391,286,392,393,394,35,395,396,397,297,398,399,379,400],"brainstorming","scientific brainstorming","sessions","data","hypothesis","open","ideation","creative","do","connections","research ideation","generation",{"skillNo":402,"name":403,"nameZh":6,"description":404,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":405,"categories":406,"keywords":413,"publishedAt":48},"01M6CZ16JG8N7A2ZX04BGE8J2Y","research-lookup","Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fresearch-lookup\u002FSKILL.md",[407,410,411,412],{"categoryNo":408,"categoryName":409,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":311,"categoryName":312,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[320,414,415,416,392,417,40,39,418,419,36,295,327,35,420,421,422,423,424,329],"lookup","research lookup","search","perplexity","parallel","openrouter","backend","up","look","queries","chat"]