[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QFVSZXA71P38CP7KWG4ADP":3,"skill-same:01M6QFVSZXA71P38CP7KWG4ADP":87},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":14,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6QFVSZXA71P38CP7KWG4ADP","rwkv-architecture",null,"Covers the RWKV (Receptance Weighted Key Value) architecture, an RNN\u002FTransformer hybrid with O(n) inference and no KV cache, including RWKV-7, its parallel GPT-mode training and sequential RNN-mode inference, state passing, fine-tuning with DeepSpeed, and CUDA kernel setup. Use when generating text token by token with constant memory, processing very long contexts of 100K+ tokens, fine-tuning an RWKV model, comparing RWKV memory and speed against Transformers, or debugging RWKV state handling, loading, or out-of-memory errors. Prefer a standard Transformer when peak accuracy matters more than memory, and Mamba for state-space models.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-rwkv-architecture\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"rwkv architecture","rwkv","architecture","key value","hybrid","value","accuracy","for","mode","Model Architecture","Efficient Inference","tokens","foundation","complexity","use","context","parallel","text","Fine-Tuning","training","2026-11-10 10:12:30",false,"---\nname: rwkv-architecture\ndescription: Covers the RWKV (Receptance Weighted Key Value) architecture, an RNN\u002FTransformer hybrid with O(n) inference and no KV cache, including RWKV-7, its parallel GPT-mode training and sequential RNN-mode inference, state passing, fine-tuning with DeepSpeed, and CUDA kernel setup. Use when generating text token by token with constant memory, processing very long contexts of 100K+ tokens, fine-tuning an RWKV model, comparing RWKV memory and speed against Transformers, or debugging RWKV state handling, loading, or out-of-memory errors. Prefer a standard Transformer when peak accuracy matters more than memory, and Mamba for state-space models.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: ml-training\n  maintainer: Kalaris Labs\n  tags: RWKV, Model Architecture, RNN, Transformer Hybrid, Linear Complexity, Infinite Context, Efficient Inference, Linux Foundation, Alternative Architecture\n  dependencies: rwkv, torch, transformers\n---\n\n# RWKV - Receptance Weighted Key Value\n\n## Quick start\n\nRWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).\n\n**Installation**:\n```bash\n# Install PyTorch\npip install torch --upgrade --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu121\n\n# Install dependencies\npip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade\n\n# Install RWKV\npip install rwkv\n```\n\n**Basic usage** (GPT mode + RNN mode):\n```python\nimport os\nfrom rwkv.model import RWKV\n\nos.environ[\"RWKV_JIT_ON\"] = '1'\nos.environ[\"RWKV_CUDA_ON\"] = '1'  # Use CUDA kernel for speed\n\n# Load model\nmodel = RWKV(\n    model='\u002Fpath\u002Fto\u002FRWKV-4-Pile-1B5-20220903-8040',\n    strategy='cuda fp16'\n)\n\n# GPT mode (parallel processing)\nout, state = model.forward([187, 510, 1563, 310, 247], None)\nprint(out.detach().cpu().numpy())  # Logits\n\n# RNN mode (sequential processing, same result)\nout, state = model.forward([187, 510], None)  # First 2 tokens\nout, state = model.forward([1563], state)      # Next token\nout, state = model.forward([310, 247], state)  # Last tokens\nprint(out.detach().cpu().numpy())  # Same logits as above!\n```\n\n## Common workflows\n\n### Workflow 1: Text generation (streaming)\n\n**Efficient token-by-token generation**:\n```python\nfrom rwkv.model import RWKV\nfrom rwkv.utils import PIPELINE\n\nmodel = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')\npipeline = PIPELINE(model, \"20B_tokenizer.json\")\n\n# Initial prompt\nprompt = \"The future of AI is\"\nstate = None\n\n# Generate token by token\nfor token in prompt:\n    out, state = pipeline.model.forward(pipeline.encode(token), state)\n\n# Continue generation\nfor _ in range(100):\n    out, state = pipeline.model.forward(None, state)\n    token = pipeline.sample_logits(out)\n    print(pipeline.decode(token), end='', flush=True)\n```\n\n**Key advantage**: Constant memory per token (no growing KV cache)\n\n### Workflow 2: Long context processing (infinite context)\n\n**Process million-token sequences**:\n```python\nmodel = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')\n\n# Process very long document\nstate = None\nlong_document = load_document()  # e.g., 1M tokens\n\n# Stream through entire document\nfor chunk in chunks(long_document, chunk_size=1024):\n    out, state = model.forward(chunk, state)\n\n# State now contains information from entire 1M token document\n# Memory usage: O(1) (constant, not O(n)!)\n```\n\n### Workflow 3: Fine-tuning RWKV\n\n**Standard fine-tuning workflow**:\n```python\n# Training script\nimport pytorch_lightning as pl\nfrom rwkv.model import RWKV\nfrom rwkv.trainer import RWKVTrainer\n\n# Configure model\nconfig = {\n    'n_layer': 24,\n    'n_embd': 1024,\n    'vocab_size': 50277,\n    'ctx_len': 1024\n}\n\n# Setup trainer\ntrainer = pl.Trainer(\n    accelerator='gpu',\n    devices=8,\n    precision='bf16',\n    strategy='deepspeed_stage_2',\n    max_epochs=1\n)\n\n# Train\nmodel = RWKV(config)\ntrainer.fit(model, train_dataloader)\n```\n\n### Workflow 4: RWKV vs Transformer comparison\n\n**Memory comparison** (1M token sequence):\n```python\n# Transformer (GPT)\n# Memory: O(n²) for attention\n# KV cache: 1M × hidden_dim × n_layers × 2 (keys + values)\n# Example: 1M × 4096 × 24 × 2 = ~400GB (impractical!)\n\n# RWKV\n# Memory: O(1) per token\n# State: hidden_dim × n_layers = 4096 × 24 = ~400KB\n# 1,000,000× more efficient!\n```\n\n**Speed comparison** (inference):\n```python\n# Transformer: O(n) per token (quadratic overall)\n# First token: 1 computation\n# Second token: 2 computations\n# ...\n# 1000th token: 1000 computations\n\n# RWKV: O(1) per token (linear overall)\n# Every token: 1 computation\n# 1000th token: 1 computation (same as first!)\n```\n\n## When to use vs alternatives\n\n**Use RWKV when**:\n- Need very long context (100K+ tokens)\n- Want constant memory usage\n- Building streaming applications\n- Need RNN efficiency with Transformer performance\n- Memory-constrained deployment\n\n**Key advantages**:\n- **Linear time**: O(n) vs O(n²) for Transformers\n- **No KV cache**: Constant memory per token\n- **Infinite context**: No fixed window limit\n- **Parallelizable training**: Like GPT\n- **Sequential inference**: Like RNN\n\n**Use alternatives instead**:\n- **Transformers**: Need absolute best performance, have compute\n- **Mamba**: Want state-space models\n- **RetNet**: Need retention mechanism\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: Out of memory during training**\n\nUse gradient checkpointing and DeepSpeed:\n```python\ntrainer = pl.Trainer(\n    strategy='deepspeed_stage_3',  # Full ZeRO-3\n    precision='bf16'\n)\n```\n\n**Issue: Slow inference**\n\nEnable CUDA kernel:\n```python\nos.environ[\"RWKV_CUDA_ON\"] = '1'\n```\n\n**Issue: Model not loading**\n\nCheck model path and strategy:\n```python\nmodel = RWKV(\n    model='\u002Fabsolute\u002Fpath\u002Fto\u002Fmodel.pth',\n    strategy='cuda fp16'  # Or 'cpu fp32' for CPU\n)\n```\n\n**Issue: State management in RNN mode**\n\nAlways pass state between forward calls:\n```python\n# WRONG: State lost\nout1, _ = model.forward(tokens1, None)\nout2, _ = model.forward(tokens2, None)  # No context from tokens1!\n\n# CORRECT: State preserved\nout1, state = model.forward(tokens1, None)\nout2, state = model.forward(tokens2, state)  # Has context from tokens1\n```\n\n## Advanced topics\n\n**Time-mixing and channel-mixing**: See [references\u002Farchitecture-details.md](references\u002Farchitecture-details.md) for WKV operation, time-decay mechanism, and receptance gates.\n\n**State management**: See [references\u002Fstate-management.md](references\u002Fstate-management.md) for att_x_prev, att_kv, ffn_x_prev states, and numerical stability considerations.\n\n**RWKV-7 improvements**: See [references\u002Frwkv7.md](references\u002Frwkv7.md) for latest architectural improvements (March 2025) and multimodal capabilities.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA (CUDA 11.6+) or CPU\n- **VRAM** (FP16):\n  - 169M model: 1GB\n  - 430M model: 2GB\n  - 1.5B model: 4GB\n  - 3B model: 8GB\n  - 7B model: 16GB\n  - 14B model: 32GB\n- **Inference**: O(1) memory per token\n- **Training**: Parallelizable like GPT\n\n**Performance** (vs Transformers):\n- **Speed**: Similar training, faster inference\n- **Memory**: 1000× less for long sequences\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (RWKV): https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13048 (May 2023)\n- Paper (RWKV-7): https:\u002F\u002Farxiv.org\u002Fabs\u002F2503.14456 (March 2025)\n- GitHub: https:\u002F\u002Fgithub.com\u002FBlinkDL\u002FRWKV-LM ⭐ 12,000+\n- Docs: https:\u002F\u002Fwiki.rwkv.com\u002F\n- Models: https:\u002F\u002Fhuggingface.co\u002FBlinkDL\n- Linux Foundation AI: Official project\n- Production: Microsoft Windows, Office integration, NeMo support\n\n## Agent operating procedure\n\n1. **Check the environment.** Check GPU type, memory and driver\u002FCUDA versions (`nvidia-smi`), framework versions, and dataset location and size.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| CUDA out-of-memory | Reduce batch size, enable gradient accumulation\u002Fcheckpointing or mixed precision, or shard the model. |\n| Loss is NaN or diverges | Lower the learning rate, check data for invalid values, and enable gradient clipping. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never claim training results without logs; estimate compute cost before launching large jobs.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `mamba-architecture`: Explains how to use Mamba selective state-space models (state-spaces\u002Fmamba package, Mamba-1 with d_state=16 and Mamba-2 with multi-head str…\n- `optimizing-attention-flash`: Enables Flash Attention for transformer models using PyTorch native scaled_dot_product_attention (PyTorch 2.2+) or the flash-attn library,…\n- `speculative-decoding`: Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.\n","# RWKV - Receptance Weighted Key Value\n\n## Quick start\n\nRWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).\n\n**Installation**:\n```bash\n# Install PyTorch\npip install torch --upgrade --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu121\n\n# Install dependencies\npip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade\n\n# Install RWKV\npip install rwkv\n```\n\n**Basic usage** (GPT mode + RNN mode):\n```python\nimport os\nfrom rwkv.model import RWKV\n\nos.environ[\"RWKV_JIT_ON\"] = '1'\nos.environ[\"RWKV_CUDA_ON\"] = '1'  # Use CUDA kernel for speed\n\n# Load model\nmodel = RWKV(\n    model='\u002Fpath\u002Fto\u002FRWKV-4-Pile-1B5-20220903-8040',\n    strategy='cuda fp16'\n)\n\n# GPT mode (parallel processing)\nout, state = model.forward([187, 510, 1563, 310, 247], None)\nprint(out.detach().cpu().numpy())  # Logits\n\n# RNN mode (sequential processing, same result)\nout, state = model.forward([187, 510], None)  # First 2 tokens\nout, state = model.forward([1563], state)      # Next token\nout, state = model.forward([310, 247], state)  # Last tokens\nprint(out.detach().cpu().numpy())  # Same logits as above!\n```\n\n## Common workflows\n\n### Workflow 1: Text generation (streaming)\n\n**Efficient token-by-token generation**:\n```python\nfrom rwkv.model import RWKV\nfrom rwkv.utils import PIPELINE\n\nmodel = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')\npipeline = PIPELINE(model, \"20B_tokenizer.json\")\n\n# Initial prompt\nprompt = \"The future of AI is\"\nstate = None\n\n# Generate token by token\nfor token in prompt:\n    out, state = pipeline.model.forward(pipeline.encode(token), state)\n\n# Continue generation\nfor _ in range(100):\n    out, state = pipeline.model.forward(None, state)\n    token = pipeline.sample_logits(out)\n    print(pipeline.decode(token), end='', flush=True)\n```\n\n**Key advantage**: Constant memory per token (no growing KV cache)\n\n### Workflow 2: Long context processing (infinite context)\n\n**Process million-token sequences**:\n```python\nmodel = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')\n\n# Process very long document\nstate = None\nlong_document = load_document()  # e.g., 1M tokens\n\n# Stream through entire document\nfor chunk in chunks(long_document, chunk_size=1024):\n    out, state = model.forward(chunk, state)\n\n# State now contains information from entire 1M token document\n# Memory usage: O(1) (constant, not O(n)!)\n```\n\n### Workflow 3: Fine-tuning RWKV\n\n**Standard fine-tuning workflow**:\n```python\n# Training script\nimport pytorch_lightning as pl\nfrom rwkv.model import RWKV\nfrom rwkv.trainer import RWKVTrainer\n\n# Configure model\nconfig = {\n    'n_layer': 24,\n    'n_embd': 1024,\n    'vocab_size': 50277,\n    'ctx_len': 1024\n}\n\n# Setup trainer\ntrainer = pl.Trainer(\n    accelerator='gpu',\n    devices=8,\n    precision='bf16',\n    strategy='deepspeed_stage_2',\n    max_epochs=1\n)\n\n# Train\nmodel = RWKV(config)\ntrainer.fit(model, train_dataloader)\n```\n\n### Workflow 4: RWKV vs Transformer comparison\n\n**Memory comparison** (1M token sequence):\n```python\n# Transformer (GPT)\n# Memory: O(n²) for attention\n# KV cache: 1M × hidden_dim × n_layers × 2 (keys + values)\n# Example: 1M × 4096 × 24 × 2 = ~400GB (impractical!)\n\n# RWKV\n# Memory: O(1) per token\n# State: hidden_dim × n_layers = 4096 × 24 = ~400KB\n# 1,000,000× more efficient!\n```\n\n**Speed comparison** (inference):\n```python\n# Transformer: O(n) per token (quadratic overall)\n# First token: 1 computation\n# Second token: 2 computations\n# ...\n# 1000th token: 1000 computations\n\n# RWKV: O(1) per token (linear overall)\n# Every token: 1 computation\n# 1000th token: 1 computation (same as first!)\n```\n\n## When to use vs alternatives\n\n**Use RWKV when**:\n- Need very long context (100K+ tokens)\n- Want constant memory usage\n- Building streaming applications\n- Need RNN efficiency with Transformer performance\n- Memory-constrained deployment\n\n**Key advantages**:\n- **Linear time**: O(n) vs O(n²) for Transformers\n- **No KV cache**: Constant memory per token\n- **Infinite context**: No fixed window limit\n- **Parallelizable training**: Like GPT\n- **Sequential inference**: Like RNN\n\n**Use alternatives instead**:\n- **Transformers**: Need absolute best performance, have compute\n- **Mamba**: Want state-space models\n- **RetNet**: Need retention mechanism\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: Out of memory during training**\n\nUse gradient checkpointing and DeepSpeed:\n```python\ntrainer = pl.Trainer(\n    strategy='deepspeed_stage_3',  # Full ZeRO-3\n    precision='bf16'\n)\n```\n\n**Issue: Slow inference**\n\nEnable CUDA kernel:\n```python\nos.environ[\"RWKV_CUDA_ON\"] = '1'\n```\n\n**Issue: Model not loading**\n\nCheck model path and strategy:\n```python\nmodel = RWKV(\n    model='\u002Fabsolute\u002Fpath\u002Fto\u002Fmodel.pth',\n    strategy='cuda fp16'  # Or 'cpu fp32' for CPU\n)\n```\n\n**Issue: State management in RNN mode**\n\nAlways pass state between forward calls:\n```python\n# WRONG: State lost\nout1, _ = model.forward(tokens1, None)\nout2, _ = model.forward(tokens2, None)  # No context from tokens1!\n\n# CORRECT: State preserved\nout1, state = model.forward(tokens1, None)\nout2, state = model.forward(tokens2, state)  # Has context from tokens1\n```\n\n## Advanced topics\n\n**Time-mixing and channel-mixing**: See [references\u002Farchitecture-details.md](references\u002Farchitecture-details.md) for WKV operation, time-decay mechanism, and receptance gates.\n\n**State management**: See [references\u002Fstate-management.md](references\u002Fstate-management.md) for att_x_prev, att_kv, ffn_x_prev states, and numerical stability considerations.\n\n**RWKV-7 improvements**: See [references\u002Frwkv7.md](references\u002Frwkv7.md) for latest architectural improvements (March 2025) and multimodal capabilities.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA (CUDA 11.6+) or CPU\n- **VRAM** (FP16):\n  - 169M model: 1GB\n  - 430M model: 2GB\n  - 1.5B model: 4GB\n  - 3B model: 8GB\n  - 7B model: 16GB\n  - 14B model: 32GB\n- **Inference**: O(1) memory per token\n- **Training**: Parallelizable like GPT\n\n**Performance** (vs Transformers):\n- **Speed**: Similar training, faster inference\n- **Memory**: 1000× less for long sequences\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (RWKV): https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13048 (May 2023)\n- Paper (RWKV-7): https:\u002F\u002Farxiv.org\u002Fabs\u002F2503.14456 (March 2025)\n- GitHub: https:\u002F\u002Fgithub.com\u002FBlinkDL\u002FRWKV-LM ⭐ 12,000+\n- Docs: https:\u002F\u002Fwiki.rwkv.com\u002F\n- Models: https:\u002F\u002Fhuggingface.co\u002FBlinkDL\n- Linux Foundation AI: Official project\n- Production: Microsoft Windows, Office integration, NeMo support\n\n## Agent operating procedure\n\n1. **Check the environment.** Check GPU type, memory and driver\u002FCUDA versions (`nvidia-smi`), framework versions, and dataset location and size.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| CUDA out-of-memory | Reduce batch size, enable gradient accumulation\u002Fcheckpointing or mixed precision, or shard the model. |\n| Loss is NaN or diverges | Lower the learning rate, check data for invalid values, and enable gradient clipping. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never claim training results without logs; estimate compute cost before launching large jobs.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `mamba-architecture`: Explains how to use Mamba selective state-space models (state-spaces\u002Fmamba package, Mamba-1 with d_state=16 and Mamba-2 with multi-head str…\n- `optimizing-attention-flash`: Enables Flash Attention for transformer models using PyTorch native scaled_dot_product_attention (PyTorch 2.2+) or the flash-attn library,…\n- `speculative-decoding`: Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.\n",{"name":5,"license":15,"metadata":44,"description":7},{"tags":45,"version":46,"category":47,"maintainer":48,"dependencies":49},"RWKV, Model Architecture, RNN, Transformer Hybrid, Linear Complexity, Infinite Context, Efficient Inference, Linux Foundation, Alternative Architecture","1.0.0","ml-training","Kalaris Labs","rwkv, torch, transformers","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-rwkv-architecture","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill rwkv-architecture",{"tool":65,"command":66},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill rwkv-architecture",{"tool":68,"command":69},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill rwkv-architecture","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-rwkv-architecture\u002FSKILL.md",true,[73,79,83],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Farchitecture-details.md","REFERENCE","参考文档",9304,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-rwkv-architecture\u002Freferences\u002Farchitecture-details.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Frwkv7.md",10428,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-rwkv-architecture\u002Freferences\u002Frwkv7.md",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fstate-management.md",9497,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-rwkv-architecture\u002Freferences\u002Fstate-management.md",[88,119,143,170,195,218],{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":99,"publishedAt":39},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[94],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,26,116,117,118],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":39},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[126,127,128,129,104,130,131,132,133,134,135,136,137,138,33,139,140,141,142,26],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","platform","grow","posting","competitive",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":152,"publishedAt":39},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[149],{"categoryNo":150,"categoryName":151,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000023","Skill与智能体开发",[153,137,154,155,156,157,158,159,33,160,161,50,162,163,164,165,166,167,168,169],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":39},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[177,178,179,180,181,182,183,184,185,33,186,111,187,188,189,190,191,192,193,194],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":202,"publishedAt":39},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[201],{"categoryNo":150,"categoryName":151,"taxonomyType":97,"taxonomyTypeDesc":98},[203,204,205,206,207,155,182,208,209,210,211,212,137,34,110,213,214,215,216,217],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","across","fresh","scripts","judge","claude code workflow",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":224,"publishedAt":39},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[225,226,227,228,229,230,231,232,233,211,234,235,236,237,238,239,112,240,241,194],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]