[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78AX024G7S080T1JFFY9CWR":3,"skill-same:01M78AX024G7S080T1JFFY9CWR":74},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":14,"repositoryHtmlUrl":31,"repositoryHomepageUrl":6,"ownerHtmlUrl":32,"ownerBlogUrl":33,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M78AX024G7S080T1JFFY9CWR","vss-ask-video",null,"Use this skill to ask the VSS agent's video_understanding tool a fresh visual question about a recorded clip. Not for prior tool output, search hits, or metadata-answerable questions.","Solizardking\u002Ftrain2earn","Solizardking","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F108096810?v=4","01M786KDB10AYV042N0F9BD5B5","Clawd","01M786KDC5C3NPE4W44C5641EZ",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: vss-ask-video\ndescription: Use this skill to ask the VSS agent's video_understanding tool a fresh visual question about a recorded clip. Not for prior tool output, search hits, or metadata-answerable questions.\nlicense: Apache-2.0\nmetadata:\n  version: \"3.2.0\"\n  github-url: \"https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization\"\n  tags: \"nvidia blueprint operational\"\n---\n\n# Video QnA using VLM through VSS Agent\n\nUse this skill when you need details about the video which requires VLM to look at the video frames — for example the agent has **no** usable prior answer and needs a **fresh look at the pixels** for a specific clip.\n\n---\n\n## When to Use\n\n- The user asks **what happens in the video**, what **objects \u002F people \u002F actions** appear, **colors**, **timing**, **safety**, or other **visual facts** that require watching the clip.\n- The user asks for **details** that **cannot be answered** from existing messages, summaries, Elasticsearch\u002FMCP results, or filenames alone—you need **model inference on the video**.\n- Follow-up questions about **content details** after a coarse summary or after report generation.\n\nDo **not** use this skill when a **database \u002F MCP \u002F prior tool output** already answers the question, unless the user explicitly wants **verification** against the video.\n\n---\n\n## Deployment prerequisite\n\nThis skill requires a VSS profile that serves the `video_understanding` tool — typically **base** (recommended) or **lvs**. Before any request:\n\n1. Probe the VSS agent:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:8000\u002Fdocs\" >\u002Fdev\u002Fnull\n   ```\n\n2. **If the probe fails**, ask the user:\n   > *\"No VSS profile is running on `$HOST_IP`. Shall I deploy `base` (recommended for per-clip VLM QnA) using the `\u002Fvss-deploy-profile` skill? If you prefer `lvs`, say so.\"*\n\n   - If yes → hand off to `\u002Fvss-deploy-profile -p base` (or `-p lvs` if the user prefers). Return here once it succeeds.\n   - If no → stop.\n\n3. If the probe passes, proceed.\n\n---\n\n## Sensor prerequisite\n\n**You MUST list VST sensors before any `\u002Fgenerate` call.** This is required even when the user names the sensor explicitly, even when the user asserts the video is already uploaded, and even when a previous turn appeared to use the same video. Do not skip this step.\n\n1. List sensors:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fsensor\u002Flist\" | jq '.[].name'\n   ```\n\n2. Compare the returned `name` values against the user-supplied `\u003Csensor-id>` (or **filename stem**, e.g. `warehouse_safety_0001`).\n\n3. **If a matching sensor is present** → proceed to the Agent workflow below.\n\n4. **If no matching sensor is present** — upload the video first, then re-list to confirm the new sensor appears:\n   ```bash\n   # filename: must not contain whitespace\n   # timestamp: ISO 8601 UTC — default 2025-01-01T00:00:00.000Z if user did not specify\n   curl -s -X PUT \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fstorage\u002Ffile\u002F\u003Cfilename>?timestamp=\u003Ctimestamp>\" \\\n     -H \"Content-Type: application\u002Foctet-stream\" \\\n     -H \"Content-Length: \u003Cfile_size_in_bytes>\" \\\n     --upload-file \u002Fpath\u002Fto\u002F\u003Cfilename> | jq .\n   ```\n   See `\u002Fvss-manage-video-io-storage` for full upload semantics (v1 vs v2, conflict handling, delete flow). In interactive runs, confirm with the user before uploading. **Never** issue an unconditional PUT without first running the sensor-list check above — that is exactly the failure mode this prerequisite exists to prevent.\n\n---\n\n## Agent workflow\n\nThe Sensor prerequisite above must have already confirmed (or made) the sensor exist on VST. Then:\n\n1. **Clip** — Identify **sensor id**, **filename**, or **URL** for one video segment. If ambiguous, ask the user.\n2. Call vss agent with the sensor id and ask for it to call video_understanding tool to answer the user's question.\n3. Return the vss agent's answer back to the user.\n\n\n## Query VSS agent (`\u002Fgenerate`)\n\n```bash\n# Set from deployment (compose \u002F .env \u002F host where vss-agent listens)\nexport VSS_AGENT_BASE_URL=\"http:\u002F\u002Flocalhost:8000\"\n\ncurl -s -X POST \"${VSS_AGENT_BASE_URL}\u002Fgenerate\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"input_message\": \"Call video_understanding tool to answer the following question about \u003Csensor-id>: \u003Cuser query>\"}' | jq .\n```\n\n### Response contract and extraction\n\n`\u002Fgenerate` returns a JSON object with the assistant output in `value`, for example:\n\n```json\n{\"value\":\"\u003Cagent-think>\u003Cagent-think-step ...>...\u003C\u002Fagent-think-step>\u003C\u002Fagent-think>\\n\\n\u003Cfinal answer>\\n\\n\"}\n```\n\nThere is no separate clean-answer field. The consumable answer is the text in `.value` after removing any `\u003Cagent-think>...\u003C\u002Fagent-think>` block.\n\nRequired handling for this skill (and any downstream caller):\n\n1. Read `.value` from the JSON response.\n2. Strip `\u003Cagent-think>...\u003C\u002Fagent-think>` sections wherever they appear.\n3. Return only the remaining final-answer text to the user.\n\nExample extraction:\n\n```bash\ncurl -s -X POST \"${VSS_AGENT_BASE_URL}\u002Fgenerate\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"input_message\":\"Call video_understanding tool to answer the following question about \u003Csensor-id>: \u003Cuser query>\"}' \\\n| jq -r '.value' \\\n| python3 -c 'import re,sys; t=sys.stdin.read(); t=re.sub(r\"\u003Cagent-think>.*?\u003C\u002Fagent-think>\\s*\", \"\", t, flags=re.S); print(t.strip())'\n```\n\n---\n\n## Cross-Reference\n\n- **vss-manage-video-io-storage** — VST storage\u002Freplay URLs so **`VIDEO_URL`** is valid for the VLM.\n- **vss-generate-video-report** — timestamped **reports** via **Mode A (direct VLM)** or **Mode B (video-analytics incidents)**; this skill is **VSS-agent `\u002Fgenerate`** for ad-hoc **video Q&A**.\n","# Video QnA using VLM through VSS Agent\n\nUse this skill when you need details about the video which requires VLM to look at the video frames — for example the agent has **no** usable prior answer and needs a **fresh look at the pixels** for a specific clip.\n\n---\n\n## When to Use\n\n- The user asks **what happens in the video**, what **objects \u002F people \u002F actions** appear, **colors**, **timing**, **safety**, or other **visual facts** that require watching the clip.\n- The user asks for **details** that **cannot be answered** from existing messages, summaries, Elasticsearch\u002FMCP results, or filenames alone—you need **model inference on the video**.\n- Follow-up questions about **content details** after a coarse summary or after report generation.\n\nDo **not** use this skill when a **database \u002F MCP \u002F prior tool output** already answers the question, unless the user explicitly wants **verification** against the video.\n\n---\n\n## Deployment prerequisite\n\nThis skill requires a VSS profile that serves the `video_understanding` tool — typically **base** (recommended) or **lvs**. Before any request:\n\n1. Probe the VSS agent:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:8000\u002Fdocs\" >\u002Fdev\u002Fnull\n   ```\n\n2. **If the probe fails**, ask the user:\n   > *\"No VSS profile is running on `$HOST_IP`. Shall I deploy `base` (recommended for per-clip VLM QnA) using the `\u002Fvss-deploy-profile` skill? If you prefer `lvs`, say so.\"*\n\n   - If yes → hand off to `\u002Fvss-deploy-profile -p base` (or `-p lvs` if the user prefers). Return here once it succeeds.\n   - If no → stop.\n\n3. If the probe passes, proceed.\n\n---\n\n## Sensor prerequisite\n\n**You MUST list VST sensors before any `\u002Fgenerate` call.** This is required even when the user names the sensor explicitly, even when the user asserts the video is already uploaded, and even when a previous turn appeared to use the same video. Do not skip this step.\n\n1. List sensors:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fsensor\u002Flist\" | jq '.[].name'\n   ```\n\n2. Compare the returned `name` values against the user-supplied `\u003Csensor-id>` (or **filename stem**, e.g. `warehouse_safety_0001`).\n\n3. **If a matching sensor is present** → proceed to the Agent workflow below.\n\n4. **If no matching sensor is present** — upload the video first, then re-list to confirm the new sensor appears:\n   ```bash\n   # filename: must not contain whitespace\n   # timestamp: ISO 8601 UTC — default 2025-01-01T00:00:00.000Z if user did not specify\n   curl -s -X PUT \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fstorage\u002Ffile\u002F\u003Cfilename>?timestamp=\u003Ctimestamp>\" \\\n     -H \"Content-Type: application\u002Foctet-stream\" \\\n     -H \"Content-Length: \u003Cfile_size_in_bytes>\" \\\n     --upload-file \u002Fpath\u002Fto\u002F\u003Cfilename> | jq .\n   ```\n   See `\u002Fvss-manage-video-io-storage` for full upload semantics (v1 vs v2, conflict handling, delete flow). In interactive runs, confirm with the user before uploading. **Never** issue an unconditional PUT without first running the sensor-list check above — that is exactly the failure mode this prerequisite exists to prevent.\n\n---\n\n## Agent workflow\n\nThe Sensor prerequisite above must have already confirmed (or made) the sensor exist on VST. Then:\n\n1. **Clip** — Identify **sensor id**, **filename**, or **URL** for one video segment. If ambiguous, ask the user.\n2. Call vss agent with the sensor id and ask for it to call video_understanding tool to answer the user's question.\n3. Return the vss agent's answer back to the user.\n\n\n## Query VSS agent (`\u002Fgenerate`)\n\n```bash\n# Set from deployment (compose \u002F .env \u002F host where vss-agent listens)\nexport VSS_AGENT_BASE_URL=\"http:\u002F\u002Flocalhost:8000\"\n\ncurl -s -X POST \"${VSS_AGENT_BASE_URL}\u002Fgenerate\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"input_message\": \"Call video_understanding tool to answer the following question about \u003Csensor-id>: \u003Cuser query>\"}' | jq .\n```\n\n### Response contract and extraction\n\n`\u002Fgenerate` returns a JSON object with the assistant output in `value`, for example:\n\n```json\n{\"value\":\"\u003Cagent-think>\u003Cagent-think-step ...>...\u003C\u002Fagent-think-step>\u003C\u002Fagent-think>\\n\\n\u003Cfinal answer>\\n\\n\"}\n```\n\nThere is no separate clean-answer field. The consumable answer is the text in `.value` after removing any `\u003Cagent-think>...\u003C\u002Fagent-think>` block.\n\nRequired handling for this skill (and any downstream caller):\n\n1. Read `.value` from the JSON response.\n2. Strip `\u003Cagent-think>...\u003C\u002Fagent-think>` sections wherever they appear.\n3. Return only the remaining final-answer text to the user.\n\nExample extraction:\n\n```bash\ncurl -s -X POST \"${VSS_AGENT_BASE_URL}\u002Fgenerate\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"input_message\":\"Call video_understanding tool to answer the following question about \u003Csensor-id>: \u003Cuser query>\"}' \\\n| jq -r '.value' \\\n| python3 -c 'import re,sys; t=sys.stdin.read(); t=re.sub(r\"\u003Cagent-think>.*?\u003C\u002Fagent-think>\\s*\", \"\", t, flags=re.S); print(t.strip())'\n```\n\n---\n\n## Cross-Reference\n\n- **vss-manage-video-io-storage** — VST storage\u002Freplay URLs so **`VIDEO_URL`** is valid for the VLM.\n- **vss-generate-video-report** — timestamped **reports** via **Mode A (direct VLM)** or **Mode B (video-analytics incidents)**; this skill is **VSS-agent `\u002Fgenerate`** for ad-hoc **video Q&A**.\n",{"name":5,"license":15,"metadata":24,"description":7},{"tags":25,"version":26,"github-url":27},"nvidia blueprint operational","3.2.0","https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization","main","86cc27ffd16af6cb85948f1155578e6f3f61cc4c","skillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn","https:\u002F\u002Fgithub.com\u002FSolizardking","https:\u002F\u002Fmusebook.trade","Got a mile high and never came down","2026-10-08 11:52:53",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add Solizardking\u002Ftrain2earn --skill vss-ask-video",{"tool":42,"command":43},"bunx","bunx skills add Solizardking\u002Ftrain2earn --skill vss-ask-video",{"tool":45,"command":46},"pnpm","pnpm dlx skills add Solizardking\u002Ftrain2earn --skill vss-ask-video","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video\u002FSKILL.md",true,[50,56,60,64,68],{"path":51,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":54,"sourceHtmlUrl":55},"BENCHMARK.md","REFERENCE","参考文档",4182,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video\u002FBENCHMARK.md",{"path":57,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":58,"sourceHtmlUrl":59},"evals\u002Fbase_profile_video_understanding.json",3960,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video\u002Fevals\u002Fbase_profile_video_understanding.json",{"path":61,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":62,"sourceHtmlUrl":63},"evals\u002Fevals.json",422,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video\u002Fevals\u002Fevals.json",{"path":65,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":66,"sourceHtmlUrl":67},"skill-card.md",3863,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video\u002Fskill-card.md",{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"skill.oms.sig","OTHER","其他",4841,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fvss-ask-video\u002Fskill.oms.sig",[75,83,91,99,106,114],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M78B94RSCAKMH1QS79VE6DG9","testing","For testing with Light Protocol programs and clients on localnet, devnet, and mainnet validation.","MIT","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Ftesting\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":87,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M78B81BKY8RX12HETPG8YKWK","qedgen","Formally verify programs by writing Lean 4 proofs. Trigger this skill whenever the user wants to formally verify code, generate Lean 4 proofs, prove properties about algorithms or smart contracts, verify invariants, convert program logic into formal specifications, or anything involving Lean 4 and formal verification. Also trigger when the user mentions \"qedgen\", \"lean proof\", \"formal proof\", \"verify my code\", \"prove correctness\", \"formal verification\", or wants mathematical guarantees about their implementation.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-formal-verification\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-formal-verification\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":95,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":19},"01M78B7Q7C5K54E01DN83A60KG","solana-dev","Use when user asks to \"build a Solana dapp\", \"write an Anchor program\", \"create a token\", \"debug Solana errors\", \"set up wallet connection\", \"test my Solana program\", \"deploy to devnet\", or \"explain Solana concepts\" (rent, accounts, PDAs, CPIs, etc.). Also use for quick on-chain lookups via public RPC + curl — \"what's the balance of \u003Cwallet>\", \"look up transaction \u003Csig>\", \"token balance for \u003Caccount>\", \"check this address on mainnet\u002Fdevnet\". End-to-end Solana development playbook covering wallet connection, Anchor\u002FPinocchio programs, Codama client generation, LiteSVM\u002FMollusk\u002FSurfpool testing, security checklists, and JSON-RPC curl lookups against public clusters. Integrates with the Solana MCP server for live documentation search. Prefers framework-kit (@solana\u002Fclient + @solana\u002Freact-hooks) for UI, wallet-standard-first connection (incl. ConnectorKit), @solana\u002Fkit for client\u002FRPC code, and @solana\u002Fweb3-compat for legacy boundaries.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-dev\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-dev\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":19},"01M78B7HXD6N6GWKSVRQ8F6Q31","solana-common-errors","Diagnose and fix common Solana development errors — GLIBC mismatches, Anchor version conflicts, cargo build-sbf \u002F platform-tools failures, LiteSVM issues, edition2024 crate pins, RPC airdrops, and Anchor 0.29→0.32 migrations. Use when builds fail, Anchor CLI won't install, or CI breaks on Debian\u002FUbuntu.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-common-errors\u002FSKILL.md",[],[],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":110,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":19},"01M78B6T7YSYWQ0N4DP40QDXEZ","skill-creator","Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fskill-creator\u002Flicense.txt","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fskill-creator\u002FSKILL.md",[],[],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":19},"01M78B1VFT9X5AND7AE1HZPZGF","phantom-wallet-mcp","Execute wallet operations through the Phantom MCP server. Use when the user wants to interact with their Phantom wallet directly — get addresses, transfer SOL or SPL tokens, buy\u002Fswap tokens, sign transactions, and sign messages across Solana, Ethereum, Bitcoin, and Sui. Requires the @phantom\u002Fmcp-server to be configured as an MCP server.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fphantom-wallet-mcp\u002FSKILL.md",[],[]]