[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CPJ3TGVFQ2BHTKFD2S58VB":3,"skill-same:01M7CPJ3TGVFQ2BHTKFD2S58VB":107},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":19,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":6,"ownerHtmlUrl":31,"ownerBlogUrl":6,"ownerDescription":32,"updatedAt":33,"install":34,"resources":47},"01M7CPJ3TGVFQ2BHTKFD2S58VB","sora",null,"Use when the user asks to generate, remix, poll, list, download, or delete Sora videos via OpenAI’s video API using the bundled CLI (`scripts\u002Fsora.py`), including requests like “generate AI video,” “Sora,” “video remix,” “download video\u002Fthumbnail\u002Fspritesheet,” and batch video generation; requires `OPENAI_API_KEY` and Sora API access.","Carchaloneo22\u002FClaude-Code-template","Carchaloneo22","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F159488757?v=4","01M7CK5MQXAFZE3W7SAP69KNYR","Carlos Chaparro","01M7CK5MRFE26CDPEQDN5RSQPK",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fvideo\u002Fsora\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fvideo\u002Fsora\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: \"sora\"\ndescription: \"Use when the user asks to generate, remix, poll, list, download, or delete Sora videos via OpenAI\\u2019s video API using the bundled CLI (`scripts\u002Fsora.py`), including requests like \\u201cgenerate AI video,\\u201d \\u201cSora,\\u201d \\u201cvideo remix,\\u201d \\u201cdownload video\u002Fthumbnail\u002Fspritesheet,\\u201d and batch video generation; requires `OPENAI_API_KEY` and Sora API access.\"\nauthor: openai\n---\n\n\n# Sora Video Generation Skill\n\nCreates or manages short video clips for the current project (product demos, marketing spots, cinematic shots, UI mocks). Defaults to `sora-2` and a structured prompt augmentation workflow, and prefers the bundled CLI for deterministic runs. Note: `$sora` is a skill tag in prompts, not a shell command.\n\n## When to use\n- Generate a new video clip from a prompt\n- Remix an existing video by ID\n- Poll status, list jobs, or download assets (video\u002Fthumbnail\u002Fspritesheet)\n- Batch runs (many prompts or variants)\n\n## Decision tree (create vs remix vs status\u002Fdownload vs batch)\n- If the user has a **video id** and wants a change → **remix**\n- If the user has a **video id** and wants status or assets → **status\u002Fpoll\u002Fdownload**\n- If the user needs many prompts\u002Fassets → **create-batch**\n- If the user asks for two versions with a small change (same shot, different subject\u002Fdetail) → **create** the base, then **remix** for the variation\n- Otherwise → **create** (or **create-and-poll** if they need a ready asset in one step)\n\n## Workflow\n1. Decide intent: create vs remix vs status\u002Fdownload vs batch.\n2. Collect inputs: prompt, model, size, seconds, and any input reference image.\n3. If batch: write a temporary JSONL under tmp\u002F (one job per line), run once, then delete the JSONL.\n4. Prefer CLI augmentation flags (`--use-case`, `--scene`, `--camera`, etc.) instead of pre-writing a structured prompt. If you already produced a structured prompt file, pass `--no-augment` to avoid double-wrapping.\n5. Run the bundled CLI (`scripts\u002Fsora.py`) with sensible defaults (see references\u002Fcli.md). For long prompts, prefer `--prompt-file` to avoid shell-escaping issues; pair with `--no-augment` if the prompt is already structured.\n6. For async jobs, poll until complete (or use create-and-poll).\n7. Download assets (video\u002Fthumbnail\u002Fspritesheet) and save locally.\n8. Remove intermediate files created during invocation (for example `prompt.txt`, `remix_job.json`, temp JSONL). If the sandbox blocks `rm`, skip cleanup or truncate the files without surfacing an error.\n9. Iterate with a single targeted change per prompt.\n\n## Authentication\n- `OPENAI_API_KEY` must be set for live API calls.\n\nIf the key is missing, give the user these steps:\n1. Create an API key in the OpenAI platform UI: https:\u002F\u002Fplatform.openai.com\u002Fapi-keys\n2. Set `OPENAI_API_KEY` as an environment variable in their system.\n3. Offer to guide them through setting the environment variable for their OS\u002Fshell if needed.\n- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.\n\n## Defaults & rules\n- Default model: `sora-2` (use `sora-2-pro` for higher fidelity).\n- Default size: `1280x720`.\n- Default seconds: `4` (allowed: \"4\", \"8\", \"12\" as strings).\n- Always set size and seconds via API params; prose will not change them.\n- Use the OpenAI Python SDK (`openai` package); do not use raw HTTP.\n- Require `OPENAI_API_KEY` before any live API call.\n- If uv cache permissions fail, set `UV_CACHE_DIR=\u002Ftmp\u002Fuv-cache`.\n- Input reference images must be jpg\u002Fpng\u002Fwebp and should match target size.\n- Download URLs expire after about 1 hour; copy assets to your own storage.\n- Prefer the bundled CLI and **never modify** `scripts\u002Fsora.py` unless the user asks.\n- Sora can generate audio; if a user requests voiceover\u002Faudio, specify it explicitly in the `Audio:` and `Dialogue:` lines and keep it short.\n\n## API limitations\n- Models are limited to `sora-2` and `sora-2-pro`.\n- API access to Sora models requires an organization-verified account.\n- Duration is limited to 4\u002F8\u002F12 seconds and must be set via the `seconds` parameter.\n- The API expects `seconds` as a string enum (\"4\", \"8\", \"12\").\n- Output sizes are limited by model (see `references\u002Fvideo-api.md` for the supported sizes).\n- Video creation is async; you must poll for completion before downloading.\n- Rate limits apply by usage tier (do not list specific limits).\n- Content restrictions are enforced by the API (see Guardrails below).\n\n## Guardrails (must enforce)\n- Only content suitable for audiences under 18.\n- No copyrighted characters or copyrighted music.\n- No real people (including public figures).\n- Input images with human faces are rejected.\n\n## Prompt augmentation\nReformat prompts into a structured, production-oriented spec. Only make implicit details explicit; do not invent new creative requirements.\n\nTemplate (include only relevant lines):\n```\nUse case: \u003Cwhere the clip will be used>\nPrimary request: \u003Cuser's main prompt>\nScene\u002Fbackground: \u003Clocation, time of day, atmosphere>\nSubject: \u003Cmain subject>\nAction: \u003Csingle clear action>\nCamera: \u003Cshot type, angle, motion>\nLighting\u002Fmood: \u003Clighting + mood>\nColor palette: \u003C3-5 color anchors>\nStyle\u002Fformat: \u003Cfilm\u002Fanimation\u002Fformat cues>\nTiming\u002Fbeats: \u003Ccounts or beats>\nAudio: \u003Cambient cue \u002F music \u002F voiceover if requested>\nText (verbatim): \"\u003Cexact text>\"\nDialogue:\n\u003Cdialogue>\n- Speaker: \"Short line.\"\n\u003C\u002Fdialogue>\nConstraints: \u003Cmust keep\u002Fmust avoid>\nAvoid: \u003Cnegative constraints>\n```\n\nAugmentation rules:\n- Keep it short; add only details the user already implied or provided elsewhere.\n- For remixes, explicitly list invariants (\"same shot, change only X\").\n- If any critical detail is missing and blocks success, ask a question; otherwise proceed.\n- If you pass a structured prompt file to the CLI, add `--no-augment` to avoid the tool re-wrapping it.\n\n## Examples\n\n### Generation example (single shot)\n```\nUse case: product teaser\nPrimary request: a close-up of a matte black camera on a pedestal\nAction: slow 30-degree orbit over 4 seconds\nCamera: 85mm, shallow depth of field, gentle handheld drift\nLighting\u002Fmood: soft key light, subtle rim, premium studio feel\nConstraints: no logos, no text\n```\n\n### Remix example (invariants)\n```\nPrimary request: same shot and framing, switch palette to teal\u002Fsand\u002Frust with warmer backlight\nConstraints: keep the subject and camera move unchanged\n```\n\n## Prompting best practices (short list)\n- One main action + one camera move per shot.\n- Use counts or beats for timing (\"two steps, pause, turn\").\n- Keep text short and the camera locked-off for UI or on-screen text.\n- Add a brief avoid line when artifacts appear (flicker, jitter, fast motion).\n- Shorter prompts are more creative; longer prompts are more controlled.\n- Put dialogue in a dedicated block; keep lines short for 4-8s clips.\n- State invariants explicitly for remixes (same shot, same camera move).\n- Iterate with single-change follow-ups to preserve continuity.\n\n## Guidance by asset type\nUse these modules when the request is for a specific artifact. They provide targeted templates and defaults.\n- Cinematic shots: `references\u002Fcinematic-shots.md`\n- Social ads: `references\u002Fsocial-ads.md`\n\n## CLI + environment notes\n- CLI commands + examples: `references\u002Fcli.md`\n- API parameter quick reference: `references\u002Fvideo-api.md`\n- Prompting guidance: `references\u002Fprompting.md`\n- Sample prompts: `references\u002Fsample-prompts.md`\n- Troubleshooting: `references\u002Ftroubleshooting.md`\n- Network\u002Fsandbox tips: `references\u002Fcodex-network.md`\n\n## Reference map\n- **`references\u002Fcli.md`**: how to run create\u002Fpoll\u002Fremix\u002Fdownload\u002Fbatch via `scripts\u002Fsora.py`.\n- **`references\u002Fvideo-api.md`**: API-level knobs (models, sizes, duration, variants, status).\n- **`references\u002Fprompting.md`**: prompt structure and iteration guidance.\n- **`references\u002Fsample-prompts.md`**: copy\u002Fpaste prompt recipes (examples only; no extra theory).\n- **`references\u002Fcinematic-shots.md`**: templates for filmic shots.\n- **`references\u002Fsocial-ads.md`**: templates for short social ad beats.\n- **`references\u002Ftroubleshooting.md`**: common errors and fixes.\n- **`references\u002Fcodex-network.md`**: network\u002Fapproval troubleshooting.\n","# Sora Video Generation Skill\n\nCreates or manages short video clips for the current project (product demos, marketing spots, cinematic shots, UI mocks). Defaults to `sora-2` and a structured prompt augmentation workflow, and prefers the bundled CLI for deterministic runs. Note: `$sora` is a skill tag in prompts, not a shell command.\n\n## When to use\n- Generate a new video clip from a prompt\n- Remix an existing video by ID\n- Poll status, list jobs, or download assets (video\u002Fthumbnail\u002Fspritesheet)\n- Batch runs (many prompts or variants)\n\n## Decision tree (create vs remix vs status\u002Fdownload vs batch)\n- If the user has a **video id** and wants a change → **remix**\n- If the user has a **video id** and wants status or assets → **status\u002Fpoll\u002Fdownload**\n- If the user needs many prompts\u002Fassets → **create-batch**\n- If the user asks for two versions with a small change (same shot, different subject\u002Fdetail) → **create** the base, then **remix** for the variation\n- Otherwise → **create** (or **create-and-poll** if they need a ready asset in one step)\n\n## Workflow\n1. Decide intent: create vs remix vs status\u002Fdownload vs batch.\n2. Collect inputs: prompt, model, size, seconds, and any input reference image.\n3. If batch: write a temporary JSONL under tmp\u002F (one job per line), run once, then delete the JSONL.\n4. Prefer CLI augmentation flags (`--use-case`, `--scene`, `--camera`, etc.) instead of pre-writing a structured prompt. If you already produced a structured prompt file, pass `--no-augment` to avoid double-wrapping.\n5. Run the bundled CLI (`scripts\u002Fsora.py`) with sensible defaults (see references\u002Fcli.md). For long prompts, prefer `--prompt-file` to avoid shell-escaping issues; pair with `--no-augment` if the prompt is already structured.\n6. For async jobs, poll until complete (or use create-and-poll).\n7. Download assets (video\u002Fthumbnail\u002Fspritesheet) and save locally.\n8. Remove intermediate files created during invocation (for example `prompt.txt`, `remix_job.json`, temp JSONL). If the sandbox blocks `rm`, skip cleanup or truncate the files without surfacing an error.\n9. Iterate with a single targeted change per prompt.\n\n## Authentication\n- `OPENAI_API_KEY` must be set for live API calls.\n\nIf the key is missing, give the user these steps:\n1. Create an API key in the OpenAI platform UI: https:\u002F\u002Fplatform.openai.com\u002Fapi-keys\n2. Set `OPENAI_API_KEY` as an environment variable in their system.\n3. Offer to guide them through setting the environment variable for their OS\u002Fshell if needed.\n- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.\n\n## Defaults & rules\n- Default model: `sora-2` (use `sora-2-pro` for higher fidelity).\n- Default size: `1280x720`.\n- Default seconds: `4` (allowed: \"4\", \"8\", \"12\" as strings).\n- Always set size and seconds via API params; prose will not change them.\n- Use the OpenAI Python SDK (`openai` package); do not use raw HTTP.\n- Require `OPENAI_API_KEY` before any live API call.\n- If uv cache permissions fail, set `UV_CACHE_DIR=\u002Ftmp\u002Fuv-cache`.\n- Input reference images must be jpg\u002Fpng\u002Fwebp and should match target size.\n- Download URLs expire after about 1 hour; copy assets to your own storage.\n- Prefer the bundled CLI and **never modify** `scripts\u002Fsora.py` unless the user asks.\n- Sora can generate audio; if a user requests voiceover\u002Faudio, specify it explicitly in the `Audio:` and `Dialogue:` lines and keep it short.\n\n## API limitations\n- Models are limited to `sora-2` and `sora-2-pro`.\n- API access to Sora models requires an organization-verified account.\n- Duration is limited to 4\u002F8\u002F12 seconds and must be set via the `seconds` parameter.\n- The API expects `seconds` as a string enum (\"4\", \"8\", \"12\").\n- Output sizes are limited by model (see `references\u002Fvideo-api.md` for the supported sizes).\n- Video creation is async; you must poll for completion before downloading.\n- Rate limits apply by usage tier (do not list specific limits).\n- Content restrictions are enforced by the API (see Guardrails below).\n\n## Guardrails (must enforce)\n- Only content suitable for audiences under 18.\n- No copyrighted characters or copyrighted music.\n- No real people (including public figures).\n- Input images with human faces are rejected.\n\n## Prompt augmentation\nReformat prompts into a structured, production-oriented spec. Only make implicit details explicit; do not invent new creative requirements.\n\nTemplate (include only relevant lines):\n```\nUse case: \u003Cwhere the clip will be used>\nPrimary request: \u003Cuser's main prompt>\nScene\u002Fbackground: \u003Clocation, time of day, atmosphere>\nSubject: \u003Cmain subject>\nAction: \u003Csingle clear action>\nCamera: \u003Cshot type, angle, motion>\nLighting\u002Fmood: \u003Clighting + mood>\nColor palette: \u003C3-5 color anchors>\nStyle\u002Fformat: \u003Cfilm\u002Fanimation\u002Fformat cues>\nTiming\u002Fbeats: \u003Ccounts or beats>\nAudio: \u003Cambient cue \u002F music \u002F voiceover if requested>\nText (verbatim): \"\u003Cexact text>\"\nDialogue:\n\u003Cdialogue>\n- Speaker: \"Short line.\"\n\u003C\u002Fdialogue>\nConstraints: \u003Cmust keep\u002Fmust avoid>\nAvoid: \u003Cnegative constraints>\n```\n\nAugmentation rules:\n- Keep it short; add only details the user already implied or provided elsewhere.\n- For remixes, explicitly list invariants (\"same shot, change only X\").\n- If any critical detail is missing and blocks success, ask a question; otherwise proceed.\n- If you pass a structured prompt file to the CLI, add `--no-augment` to avoid the tool re-wrapping it.\n\n## Examples\n\n### Generation example (single shot)\n```\nUse case: product teaser\nPrimary request: a close-up of a matte black camera on a pedestal\nAction: slow 30-degree orbit over 4 seconds\nCamera: 85mm, shallow depth of field, gentle handheld drift\nLighting\u002Fmood: soft key light, subtle rim, premium studio feel\nConstraints: no logos, no text\n```\n\n### Remix example (invariants)\n```\nPrimary request: same shot and framing, switch palette to teal\u002Fsand\u002Frust with warmer backlight\nConstraints: keep the subject and camera move unchanged\n```\n\n## Prompting best practices (short list)\n- One main action + one camera move per shot.\n- Use counts or beats for timing (\"two steps, pause, turn\").\n- Keep text short and the camera locked-off for UI or on-screen text.\n- Add a brief avoid line when artifacts appear (flicker, jitter, fast motion).\n- Shorter prompts are more creative; longer prompts are more controlled.\n- Put dialogue in a dedicated block; keep lines short for 4-8s clips.\n- State invariants explicitly for remixes (same shot, same camera move).\n- Iterate with single-change follow-ups to preserve continuity.\n\n## Guidance by asset type\nUse these modules when the request is for a specific artifact. They provide targeted templates and defaults.\n- Cinematic shots: `references\u002Fcinematic-shots.md`\n- Social ads: `references\u002Fsocial-ads.md`\n\n## CLI + environment notes\n- CLI commands + examples: `references\u002Fcli.md`\n- API parameter quick reference: `references\u002Fvideo-api.md`\n- Prompting guidance: `references\u002Fprompting.md`\n- Sample prompts: `references\u002Fsample-prompts.md`\n- Troubleshooting: `references\u002Ftroubleshooting.md`\n- Network\u002Fsandbox tips: `references\u002Fcodex-network.md`\n\n## Reference map\n- **`references\u002Fcli.md`**: how to run create\u002Fpoll\u002Fremix\u002Fdownload\u002Fbatch via `scripts\u002Fsora.py`.\n- **`references\u002Fvideo-api.md`**: API-level knobs (models, sizes, duration, variants, status).\n- **`references\u002Fprompting.md`**: prompt structure and iteration guidance.\n- **`references\u002Fsample-prompts.md`**: copy\u002Fpaste prompt recipes (examples only; no extra theory).\n- **`references\u002Fcinematic-shots.md`**: templates for filmic shots.\n- **`references\u002Fsocial-ads.md`**: templates for short social ad beats.\n- **`references\u002Ftroubleshooting.md`**: common errors and fixes.\n- **`references\u002Fcodex-network.md`**: network\u002Fapproval troubleshooting.\n",{"name":5,"author":25,"description":7},"openai","add-x-twitter-scraper-skill","7bc347b2f7dc2dde2761a965585925e0d0171748","cli-tool\u002Fcomponents\u002Fskills\u002Fvideo\u002Fsora",0,"https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template","https:\u002F\u002Fgithub.com\u002FCarchaloneo22","Ingeniero Informático | Ingeniero en Desarrollo de Software | Consultor TIC en Gestión Publica | Docente Análisis y Desarrollo de Software. 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automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","MIT","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[],[],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":120,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":20},"01M7CPQDR2HMKAYHAAY99EA4B2","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[],{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"publishedAt":20},"01M7CPQ7K5MAQSXEB027HZFM2G","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[],[],{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":20},"01M7CPQ34F18JHMEM8NHQS1N7K","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[],{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":20},"01M7CPPZSXXDB1SAWT5N63ACZT","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[],[],{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":20},"01M7CPPTQSBVWSK0R10FJNMP0F","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002FCarchaloneo22\u002FClaude-Code-template\u002Fblob\u002Fadd-x-twitter-scraper-skill\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[],[]]