[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78AB2WN35M5MNFJRWAMPPWW":3,"skill-same:01M78AB2WN35M5MNFJRWAMPPWW":71},{"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":25,"commitSha":26,"skillDirectory":27,"forksCount":14,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":30,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M78AB2WN35M5MNFJRWAMPPWW","nemo-mbridge-perf-hierarchical-context-parallel",null,"Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.","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\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: nemo-mbridge-perf-hierarchical-context-parallel\ndescription: Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.\nlicense: Apache-2.0\nwhen_to_use: Scaling context parallelism beyond KV heads, or investigating a commit that changed CP config and caused OOM or a regression; 'hierarchical_context_parallel_sizes', 'a2a+p2p', 'hierarchical CP', 'CP beyond KV heads', 'multi-level CP'.\n---\n\n# Hierarchical Context Parallel Skill\n\nThis skill covers hierarchical context parallelism: nested context-parallel process\ngroups used by `cp_comm_type=\"a2a+p2p\"` and configured with\n`hierarchical_context_parallel_sizes`.\n\nFor what hierarchical CP is, when to use it, and the decision tree\n(`a2a+p2p` vs pure `a2a` vs `p2p`), see:\n\n- @docs\u002Ftraining\u002Fhierarchical-context-parallel.md\n- @skills\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fcard.yaml\n\n## Enablement\n\nMinimal Bridge override:\n\n```python\ncfg.model.context_parallel_size = 4\ncfg.model.cp_comm_type = \"a2a+p2p\"\ncfg.model.hierarchical_context_parallel_sizes = [2, 2]\ncfg.dist.use_decentralized_pg = False\n```\n\nRequired constraints:\n\n- `prod(hierarchical_context_parallel_sizes) == context_parallel_size`\n- `seq_length % (2 * context_parallel_size) == 0`\n- Transformer Engine `>= 1.12.0`\n\n## Code Anchors\n\nUpstream config and validation:\n\n```45:54:3rdparty\u002FMegatron-LM\u002Fmegatron\u002Fcore\u002Fmodel_parallel_config.py\ncontext_parallel_size: int = 1\n\"\"\"Splits network input along sequence dimension across GPU ranks.\"\"\"\n\nhierarchical_context_parallel_sizes: Optional[list[int]] = None\n\"\"\"Degrees of the hierarchical context parallelism. Users should provide a list to specify \n   the sizes for different levels. Taking the a2a+p2p cp comm type as example, it contains\n   groups of two levels, so the first value of the list indicates the group size of the a2a\n   communication type, and the second value indicates the group size of the p2p communication\n   type.\n\"\"\"\n```\n\n```428:433:3rdparty\u002FMegatron-LM\u002Fmegatron\u002Ftraining\u002Farguments.py\nif args.hierarchical_context_parallel_sizes:\n    from numpy import prod\n    assert args.context_parallel_size == prod(args.hierarchical_context_parallel_sizes)\nif \"a2a+p2p\" in args.cp_comm_type:\n    assert args.hierarchical_context_parallel_sizes is not None, \\\n    \"--hierarchical-context-parallel-sizes must be set when a2a+p2p is used in cp comm\"\n```\n\nBridge MPU path:\n\n```613:648:src\u002Fmegatron\u002Fbridge\u002Ftraining\u002Finitialize.py\nparallel_state.initialize_model_parallel(\n    ...\n    context_parallel_size=model_config.context_parallel_size,\n    hierarchical_context_parallel_sizes=model_config.hierarchical_context_parallel_sizes,\n    ...\n)\n...\nreturn ProcessGroupCollection.use_mpu_process_groups()\n```\n\nBridge decentralized-PG path:\n\n```503:524:src\u002Fmegatron\u002Fbridge\u002Ftraining\u002Finitialize.py\npg_collection = ProcessGroupCollection(\n    ...\n    cp=cp_pg,\n    tp_cp=tp_cp_pg,\n    hcp=None,\n    ep=ep_pg,\n    ...\n)\n```\n\n## Implementation Map\n\nThe code anchors above show the config declarations and argument validation.\n\n### Validation (MCore)\n\n`TransformerConfig.__post_init__` enforces that `a2a+p2p` requires HCP sizes and the product matches CP.\n\n### Process group creation\n\n`parallel_state.initialize_model_parallel` creates hierarchical CP sub-groups\nwhen HCP sizes are provided via `create_hierarchical_groups`. Bridge currently\ngets those groups through the MPU-backed `ProcessGroupCollection`.\n\n### TE integration\n\n`TEDotProductAttention` passes the hierarchical groups to Transformer Engine\nwhen `a2a+p2p` is used. Requires **Transformer Engine >= 1.12.0**.\n\n## Pitfalls\n\n1. **Bridge HCP is MPU-only today**: If `use_decentralized_pg=True`, Bridge initializes flat CP groups and leaves HCP unset.\n2. **No checked-in Bridge recipe** currently exercises HCP directly.\n3. **Single-GPU load helpers** clear `hierarchical_context_parallel_sizes`.\n4. **Silent broken training on old stacks**: If you use `a2a+p2p` without setting `hierarchical_context_parallel_sizes`, MCore now asserts. Older versions would silently disable CP communication, so each rank attended only to its local chunk and produced artificially high throughput with broken gradients.\n5. **Product must match**: `prod(hierarchical_context_parallel_sizes)` must exactly equal `context_parallel_size`. A mismatch triggers an assertion.\n6. **Verify in logs**: Look for the process group initialization output. You should see `HIERARCHICAL_CONTEXT_PARALLEL_GROUPS` being created. If you only see `CONTEXT_PARALLEL_GROUP`, HCP is not active.\n\n## Verification\n\nNo dedicated Bridge end-to-end test exists yet for HCP (see @skills\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fcard.yaml\n`follow_up_validation`). Use the existing unit tests and log inspection instead.\n\nRun the decentralized-PG unit test to confirm the flat-CP behavior is preserved:\n\n```bash\nuv run python -m pytest tests\u002Funit_tests\u002Ftraining\u002Ftest_decentralized_pg.py -q\n```\n\nFor a manual smoke check, launch a 4-GPU run with a small recipe and\n`cp_comm_type=a2a+p2p` plus `hierarchical_context_parallel_sizes=[2,2]`:\n\n```bash\nCUDA_VISIBLE_DEVICES=0,1,2,3 uv run python -m torch.distributed.run --nproc_per_node=4 \\\n  scripts\u002Ftraining\u002Frun_recipe.py \\\n  --recipe llama32_1b_pretrain_config \\\n  model.context_parallel_size=4 \\\n  model.cp_comm_type=a2a+p2p \\\n  \"model.hierarchical_context_parallel_sizes=[2,2]\" \\\n  train.train_iters=2\n```\n\nSuccess criteria:\n\n- Logs show `HIERARCHICAL_CONTEXT_PARALLEL_GROUPS` being created\n- Training completes at least one step without error\n- If you only see `CONTEXT_PARALLEL_GROUP`, HCP is not active\n","# Hierarchical Context Parallel Skill\n\nThis skill covers hierarchical context parallelism: nested context-parallel process\ngroups used by `cp_comm_type=\"a2a+p2p\"` and configured with\n`hierarchical_context_parallel_sizes`.\n\nFor what hierarchical CP is, when to use it, and the decision tree\n(`a2a+p2p` vs pure `a2a` vs `p2p`), see:\n\n- @docs\u002Ftraining\u002Fhierarchical-context-parallel.md\n- @skills\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fcard.yaml\n\n## Enablement\n\nMinimal Bridge override:\n\n```python\ncfg.model.context_parallel_size = 4\ncfg.model.cp_comm_type = \"a2a+p2p\"\ncfg.model.hierarchical_context_parallel_sizes = [2, 2]\ncfg.dist.use_decentralized_pg = False\n```\n\nRequired constraints:\n\n- `prod(hierarchical_context_parallel_sizes) == context_parallel_size`\n- `seq_length % (2 * context_parallel_size) == 0`\n- Transformer Engine `>= 1.12.0`\n\n## Code Anchors\n\nUpstream config and validation:\n\n```45:54:3rdparty\u002FMegatron-LM\u002Fmegatron\u002Fcore\u002Fmodel_parallel_config.py\ncontext_parallel_size: int = 1\n\"\"\"Splits network input along sequence dimension across GPU ranks.\"\"\"\n\nhierarchical_context_parallel_sizes: Optional[list[int]] = None\n\"\"\"Degrees of the hierarchical context parallelism. Users should provide a list to specify \n   the sizes for different levels. Taking the a2a+p2p cp comm type as example, it contains\n   groups of two levels, so the first value of the list indicates the group size of the a2a\n   communication type, and the second value indicates the group size of the p2p communication\n   type.\n\"\"\"\n```\n\n```428:433:3rdparty\u002FMegatron-LM\u002Fmegatron\u002Ftraining\u002Farguments.py\nif args.hierarchical_context_parallel_sizes:\n    from numpy import prod\n    assert args.context_parallel_size == prod(args.hierarchical_context_parallel_sizes)\nif \"a2a+p2p\" in args.cp_comm_type:\n    assert args.hierarchical_context_parallel_sizes is not None, \\\n    \"--hierarchical-context-parallel-sizes must be set when a2a+p2p is used in cp comm\"\n```\n\nBridge MPU path:\n\n```613:648:src\u002Fmegatron\u002Fbridge\u002Ftraining\u002Finitialize.py\nparallel_state.initialize_model_parallel(\n    ...\n    context_parallel_size=model_config.context_parallel_size,\n    hierarchical_context_parallel_sizes=model_config.hierarchical_context_parallel_sizes,\n    ...\n)\n...\nreturn ProcessGroupCollection.use_mpu_process_groups()\n```\n\nBridge decentralized-PG path:\n\n```503:524:src\u002Fmegatron\u002Fbridge\u002Ftraining\u002Finitialize.py\npg_collection = ProcessGroupCollection(\n    ...\n    cp=cp_pg,\n    tp_cp=tp_cp_pg,\n    hcp=None,\n    ep=ep_pg,\n    ...\n)\n```\n\n## Implementation Map\n\nThe code anchors above show the config declarations and argument validation.\n\n### Validation (MCore)\n\n`TransformerConfig.__post_init__` enforces that `a2a+p2p` requires HCP sizes and the product matches CP.\n\n### Process group creation\n\n`parallel_state.initialize_model_parallel` creates hierarchical CP sub-groups\nwhen HCP sizes are provided via `create_hierarchical_groups`. Bridge currently\ngets those groups through the MPU-backed `ProcessGroupCollection`.\n\n### TE integration\n\n`TEDotProductAttention` passes the hierarchical groups to Transformer Engine\nwhen `a2a+p2p` is used. Requires **Transformer Engine >= 1.12.0**.\n\n## Pitfalls\n\n1. **Bridge HCP is MPU-only today**: If `use_decentralized_pg=True`, Bridge initializes flat CP groups and leaves HCP unset.\n2. **No checked-in Bridge recipe** currently exercises HCP directly.\n3. **Single-GPU load helpers** clear `hierarchical_context_parallel_sizes`.\n4. **Silent broken training on old stacks**: If you use `a2a+p2p` without setting `hierarchical_context_parallel_sizes`, MCore now asserts. Older versions would silently disable CP communication, so each rank attended only to its local chunk and produced artificially high throughput with broken gradients.\n5. **Product must match**: `prod(hierarchical_context_parallel_sizes)` must exactly equal `context_parallel_size`. A mismatch triggers an assertion.\n6. **Verify in logs**: Look for the process group initialization output. You should see `HIERARCHICAL_CONTEXT_PARALLEL_GROUPS` being created. If you only see `CONTEXT_PARALLEL_GROUP`, HCP is not active.\n\n## Verification\n\nNo dedicated Bridge end-to-end test exists yet for HCP (see @skills\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fcard.yaml\n`follow_up_validation`). Use the existing unit tests and log inspection instead.\n\nRun the decentralized-PG unit test to confirm the flat-CP behavior is preserved:\n\n```bash\nuv run python -m pytest tests\u002Funit_tests\u002Ftraining\u002Ftest_decentralized_pg.py -q\n```\n\nFor a manual smoke check, launch a 4-GPU run with a small recipe and\n`cp_comm_type=a2a+p2p` plus `hierarchical_context_parallel_sizes=[2,2]`:\n\n```bash\nCUDA_VISIBLE_DEVICES=0,1,2,3 uv run python -m torch.distributed.run --nproc_per_node=4 \\\n  scripts\u002Ftraining\u002Frun_recipe.py \\\n  --recipe llama32_1b_pretrain_config \\\n  model.context_parallel_size=4 \\\n  model.cp_comm_type=a2a+p2p \\\n  \"model.hierarchical_context_parallel_sizes=[2,2]\" \\\n  train.train_iters=2\n```\n\nSuccess criteria:\n\n- Logs show `HIERARCHICAL_CONTEXT_PARALLEL_GROUPS` being created\n- Training completes at least one step without error\n- If you only see `CONTEXT_PARALLEL_GROUP`, HCP is not active\n",{"name":5,"license":15,"description":7,"when_to_use":24},"Scaling context parallelism beyond KV heads, or investigating a commit that changed CP config and caused OOM or a regression; 'hierarchical_context_parallel_sizes', 'a2a+p2p', 'hierarchical CP', 'CP beyond KV heads', 'multi-level CP'.","main","86cc27ffd16af6cb85948f1155578e6f3f61cc4c","skillhub\u002Fskills\u002Fnvidia\u002Fnemo-mbridge-perf-hierarchical-context-parallel","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":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add Solizardking\u002Ftrain2earn --skill nemo-mbridge-perf-hierarchical-context-parallel",{"tool":39,"command":40},"bunx","bunx skills add Solizardking\u002Ftrain2earn --skill nemo-mbridge-perf-hierarchical-context-parallel",{"tool":42,"command":43},"pnpm","pnpm dlx skills add Solizardking\u002Ftrain2earn --skill nemo-mbridge-perf-hierarchical-context-parallel","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002FSKILL.md",true,[47,53,57,61,65],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"BENCHMARK.md","REFERENCE","参考文档",4459,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002FBENCHMARK.md",{"path":54,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":55,"sourceHtmlUrl":56},"card.yaml",2800,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fcard.yaml",{"path":58,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":59,"sourceHtmlUrl":60},"evals\u002Fevals.json",1579,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fevals\u002Fevals.json",{"path":62,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":63,"sourceHtmlUrl":64},"skill-card.md",3949,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fskill-card.md",{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"skill.oms.sig","OTHER","其他",4841,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Fnemo-mbridge-perf-hierarchical-context-parallel\u002Fskill.oms.sig",[72,80,88,96,103,111],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"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":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":84,"sourceHtmlUrl":85,"categories":86,"keywords":87,"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":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":92,"sourceHtmlUrl":93,"categories":94,"keywords":95,"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":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"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":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":107,"sourceHtmlUrl":108,"categories":109,"keywords":110,"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":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"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",[],[]]