[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-riva-tts":3,"mdc-q9l328-key":34,"related-repo-nvidia-riva-tts":3406,"related-org-nvidia-riva-tts":3471},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"riva-tts","deploy and run Riva TTS models","Use when the user wants to deploy, run, or test a TTS (speech-synthesis) Riva NIM — cloud-hosted (build.nvidia.com) or self-hosted Magpie \u002F voice cloning.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,17,20],{"name":13,"slug":14,"type":15},"Text-to-Speech","text-to-speech","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"AI Infrastructure","ai-infrastructure",{"name":21,"slug":22,"type":15},"Audio","audio",6,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fdigital-health-skills","2026-07-14T05:35:45.262923","Apache-2.0",1,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"NVIDIA Digital Health skills repo for agent-guided healthcare AI workflows, including evaluation, data generation, model adaptation, deployment guidance, and developer best practices.","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fdigital-health-skills\u002Ftree\u002FHEAD\u002Fskills\u002Friva-tts","---\nname: \"riva-tts\"\nlicense: \"Apache-2.0\"\ndescription: \"Use when the user wants to deploy, run, or test a TTS (speech-synthesis) Riva NIM — cloud-hosted (build.nvidia.com) or self-hosted Magpie \u002F voice cloning.\"\nmetadata:\n  author: \"Mayank Jain \u003Cmayjain@nvidia.com>\"\n  team: riva\n  tags:\n    - nvidia\n    - riva\n    - nim\n    - tts\n    - text-to-speech\n    - magpie\n    - voice-synthesis\n    - grpc\n    - http\n    - websocket\n    - cloud\n    - nvcf\n  domain: ml\n  version: \"1.0.0\"\n---\n\n# Riva TTS NIM\n\nRiva TTS NIMs ship in two deployment shapes: cloud-hosted (managed by build.nvidia.com, no local GPU) and self-hosted (your own GPU plus Docker). Pick the one matching the user's constraints below.\n\nIf model choice isn't settled yet, route to `riva-model-selection` first — selection is out of scope for this skill.\n\n> **Agent note:** During multi-step walkthroughs, announce each step before presenting its content (e.g. \"**Step 1\u002F4 — Deploy the Container**\"). This keeps the user oriented in the four-step self-hosted flow.\n\n---\n\n## Purpose\n\nStand up an NVIDIA Riva TTS (text-to-speech) NIM and synthesize speech against it. The skill covers both the build.nvidia.com cloud path and the Docker-based self-hosted path, and walks through offline synthesis, streaming, voice cloning, and Kubernetes (Helm) deployment.\n\n## Workflow\n\nTwo routes:\n- **Cloud (Option A)** — fastest path for testing or no-GPU environments.\n- **Self-hosted (Option B)** — for production deployments where you control the GPU. The self-hosted path runs four sequential steps: deploy the container, verify it's healthy, list available voices, then synthesize.\n\n## Prerequisites\n\n- For self-hosted only: `riva-nim-setup` must be completed first (NVIDIA Container Toolkit installed, `NGC_API_KEY` exported, Docker authenticated to `nvcr.io`).\n- For cloud-hosted: `pip install -U nvidia-riva-client` and a valid `NVIDIA_API_KEY` issued at build.nvidia.com.\n- If the model choice is still open, route to `riva-model-selection` before continuing.\n\n## Instructions\n\nPick the deployment route up-front:\n\n- **Cloud (Option A):** install the Riva Python client, export `NVIDIA_API_KEY`, then invoke `talk.py` against `grpc.nvcf.nvidia.com:443` with `--use-ssl` plus the function ID from the model table just below.\n- **Self-hosted (Option B):** export `CONTAINER_ID` and `NIM_TAGS_SELECTOR` from the TTS support matrix, then walk Steps 1–4 in order (deploy → verify → list voices → synthesize).\n\n\n## Option A — Cloud-Hosted Inference (build.nvidia.com)\n\n**Setup:** `pip install -U nvidia-riva-client` + clone https:\u002F\u002Fgithub.com\u002Fnvidia-riva\u002Fpython-clients and `cd` into it.\n\n**Auth:** Set `NVIDIA_API_KEY` from https:\u002F\u002Fbuild.nvidia.com (different from `NGC_API_KEY`).\n\n**Server:** `grpc.nvcf.nvidia.com:443` — always pass `--use-ssl`.\n\n| Model | Build Page slug | Function ID |\n|-------|----------------|-------------|\n| Magpie TTS Multilingual | `nvidia\u002Fmagpie-tts-multilingual` | `877104f7-e885-42b9-8de8-f6e4c6303969` |\n\nIf a function-id no longer works, fetch the current one from `https:\u002F\u002Fbuild.nvidia.com\u002F\u003Corg>\u002F\u003Cmodel>\u002Fapi`.\n\n**Synthesize speech:**\n\n```bash\npython python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n    --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n    --metadata function-id \"877104f7-e885-42b9-8de8-f6e4c6303969\" \\\n    --metadata authorization \"Bearer $NVIDIA_API_KEY\" \\\n    --language-code en-US \\\n    --text \"Hello from NVIDIA TTS.\" \\\n    --voice \"Magpie-Multilingual.EN-US.Aria\" \\\n    --output audio.wav\n```\n> **Security note:** `$NVIDIA_API_KEY` passed as a command-line argument is\n> visible in process listings and shell history. Prefix the command with a space\n> (`HISTCONTROL=ignorespace`) or store the key in a file with `chmod 600` and\n> reference it at runtime.\n\n\n**List available voices:** add `--list-voices` (drop `--text`, `--voice`, `--output`).\n\n---\n\n## Option B — Self-Hosted NIM Deployment\n\nComplete `riva-nim-setup` first: NVIDIA Container Toolkit, `NGC_API_KEY` exported, Docker logged in to `nvcr.io`. Driver and VRAM minimums: see https:\u002F\u002Fdocs.nvidia.com\u002Fnim\u002Fspeech\u002Flatest\u002Freference\u002Fsupport-matrix\u002Ftts.html. Latency and throughput benchmarks: https:\u002F\u002Fdocs.nvidia.com\u002Fnim\u002Fspeech\u002Flatest\u002Freference\u002Fperformances\u002Ftts\u002Fperformance.html\n\nNot sure which TTS model to pick? See `riva-model-selection`.\n\n## Available Models\n\nCurrent models, voices, languages, and VRAM requirements: https:\u002F\u002Fdocs.nvidia.com\u002Fnim\u002Fspeech\u002Flatest\u002Freference\u002Fsupport-matrix\u002Ftts.html\n\nNote: Magpie TTS Zeroshot and Flow (voice cloning) are **restricted** — apply for access at developer.nvidia.com\u002Friva-tts-zeroshot-models before pulling.\n\n## Step 1 — Deploy the Container\n\nSet variables for your model, then run:\n\n| Model | `CONTAINER_ID` | `NIM_TAGS_SELECTOR` |\n|-------|---------------|---------------------|\n| Magpie TTS Multilingual | `magpie-tts-multilingual` | `name=magpie-tts-multilingual` |\n| Magpie TTS Zeroshot | `magpie-tts-zeroshot` | `name=magpie-tts-zeroshot` |\n| Magpie TTS Flow | `magpie-tts-flow` | `name=magpie-tts-flow` |\n\nFor a specific batch size: `NIM_TAGS_SELECTOR=\"name=magpie-tts-multilingual,batch_size=32\"`\n\n```bash\nexport CONTAINER_ID=\u003Cfrom-table>\nexport NIM_TAGS_SELECTOR=\u003Cfrom-table>\nexport LOCAL_NIM_CACHE=~\u002F.cache\u002Fnim\nmkdir -p $LOCAL_NIM_CACHE && chmod 700 $LOCAL_NIM_CACHE\n\ndocker run -it --rm --name=$CONTAINER_ID \\\n  --runtime=nvidia \\\n  --gpus '\"device=0\"' \\\n  --shm-size=8GB \\\n  -e NGC_API_KEY \\\n  -e NIM_TAGS_SELECTOR \\\n  -e NIM_HTTP_API_PORT=9000 \\\n  -e NIM_GRPC_API_PORT=50051 \\\n  -p 9000:9000 \\\n  -p 50051:50051 \\\n  -v $LOCAL_NIM_CACHE:\u002Fopt\u002Fnim\u002F.cache \\\n  nvcr.io\u002Fnim\u002Fnvidia\u002F$CONTAINER_ID:latest\n```\n\n## Step 2 — Verify Readiness\n\n```bash\ncurl -X GET http:\u002F\u002Flocalhost:9000\u002Fv1\u002Fhealth\u002Fready\n# Expected: {\"status\":\"ready\"}\n```\n\n## Step 3 — List Available Voices\n\nBefore synthesizing, discover available voices on the running NIM.\n\n**gRPC:**\n\n```bash\npython3 python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n  --server 0.0.0.0:50051 \\\n  --list-voices\n```\n\n**HTTP:**\n\n```bash\ncurl -sS http:\u002F\u002Flocalhost:9000\u002Fv1\u002Faudio\u002Flist_voices | python3 -m json.tool\n```\n\nVoice names follow the pattern: `Magpie-Multilingual.\u003CLANG>.\u003CVoiceName>` (e.g., `Magpie-Multilingual.EN-US.Aria`).\n\n## Step 4 — Run Speech Synthesis\n\n### Offline Synthesis (Full Audio in One Response)\n\n**gRPC:**\n\n```bash\npython3 python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n  --server 0.0.0.0:50051 \\\n  --language-code en-US \\\n  --text \"Deploy and run speech synthesis with NVIDIA TTS NIM.\" \\\n  --voice Magpie-Multilingual.EN-US.Aria \\\n  --output output.wav\n```\n\n**HTTP:**\n\n```bash\ncurl -sS http:\u002F\u002Flocalhost:9000\u002Fv1\u002Faudio\u002Fsynthesize --fail-with-body \\\n  -F language=en-US \\\n  -F text=\"Deploy and run speech synthesis with NVIDIA TTS NIM.\" \\\n  -F voice=Magpie-Multilingual.EN-US.Aria \\\n  --output output.wav\n```\n\n### Streaming Synthesis (Lower Latency, Audio Chunks)\n\n**gRPC:**\n\n```bash\npython3 python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n  --server 0.0.0.0:50051 \\\n  --language-code en-US \\\n  --text \"Deploy and run speech synthesis with NVIDIA TTS NIM.\" \\\n  --voice Magpie-Multilingual.EN-US.Aria \\\n  --stream \\\n  --output output.wav\n```\n\n**HTTP (returns raw LPCM, not WAV):**\n\n```bash\ncurl -sS http:\u002F\u002Flocalhost:9000\u002Fv1\u002Faudio\u002Fsynthesize_online --fail-with-body \\\n  -F language=en-US \\\n  -F text=\"Deploy and run speech synthesis with NVIDIA TTS NIM.\" \\\n  -F voice=Magpie-Multilingual.EN-US.Aria \\\n  -F sample_rate_hz=22050 \\\n  --output output.raw\n\n# Convert to WAV with sox\nsox -b 16 -e signed -c 1 -r 22050 output.raw output.wav\n```\n\n### WebSocket (Lowest Latency — Realtime)\n\n```bash\npython3 python-clients\u002Fscripts\u002Ftts\u002Frealtime_tts_client.py \\\n  --server localhost:9000 \\\n  --language-code en-US \\\n  --text \"Deploy and run speech synthesis with NVIDIA TTS NIM.\" \\\n  --voice Magpie-Multilingual.EN-US.Aria \\\n  --output output.wav\n```\n\n### Voice Cloning (Zeroshot \u002F Flow)\n\nProvide a 3–10 second audio prompt of the target voice:\n\n```bash\n# Zeroshot (gRPC)\npython3 python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n  --server 0.0.0.0:50051 \\\n  --text \"This is my cloned voice.\" \\\n  --zero_shot_audio_prompt_file \u002Fpath\u002Fto\u002Fvoice_sample.wav \\\n  --zero_shot_quality 20 \\\n  --output cloned_output.wav\n\n# Flow (requires transcript of the audio prompt)\npython3 python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n  --server 0.0.0.0:50051 \\\n  --text \"This is my cloned voice.\" \\\n  --zero_shot_audio_prompt_file \u002Fpath\u002Fto\u002Fvoice_sample.wav \\\n  --zero_shot_transcript \"The text spoken in the audio prompt.\" \\\n  --output cloned_output.wav\n```\n\n## Helm Deployment (Kubernetes)\n\n```yaml\n# custom-values.yaml\nimage:\n  repository: nvcr.io\u002Fnim\u002Fnvidia\u002Fmagpie-tts-multilingual\n  pullPolicy: IfNotPresent\n  tag: latest\nnim:\n  ngcAPISecret: ngc-api\nimagePullSecrets:\n  - name: ngc-secret\nenvVars:\n  NIM_TAGS_SELECTOR: name=magpie-tts-multilingual\n```\n\n```bash\nhelm install riva-tts \u003Cchart> -f custom-values.yaml\n```\n\nSubstitute `repository` and `NIM_TAGS_SELECTOR` for other models.\n\n## Key Parameters for talk.py\n\n| Parameter | Description | Default |\n|-----------|-------------|---------|\n| `--server` | gRPC endpoint | `0.0.0.0:50051` |\n| `--text` | Text to synthesize | — |\n| `--voice` | Voice name | First available |\n| `--language-code` | Language code (e.g., `en-US`) | `en-US` |\n| `--output` \u002F `-o` | Output WAV file | `output.wav` |\n| `--stream` | Enable streaming | `false` |\n| `--sample-rate-hz` | Output sample rate | `44100` |\n| `--list-voices` | List voices then exit | — |\n| `--zero_shot_audio_prompt_file` | Voice cloning audio prompt | — |\n| `--zero_shot_quality` | Cloning quality (1–40) | `20` |\n\n## Examples\n\nTwo end-to-end invocations — one per deployment route. Both use the Magpie Multilingual Aria voice.\n\n**Cloud (Option A) — Magpie Aria via build.nvidia.com:**\n```bash\npython python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n    --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n    --metadata function-id \"877104f7-e885-42b9-8de8-f6e4c6303969\" \\\n    --metadata authorization \"Bearer $NVIDIA_API_KEY\" \\\n    --text \"Hello from NVIDIA TTS.\" \\\n    --voice \"Magpie-Multilingual.EN-US.Aria\" \\\n    --output audio.wav\n```\n\n**Self-hosted (Option B) — offline synthesis against a local NIM:**\n```bash\npython3 python-clients\u002Fscripts\u002Ftts\u002Ftalk.py \\\n  --server 0.0.0.0:50051 \\\n  --text \"Deploy speech synthesis with NVIDIA TTS NIM.\" \\\n  --voice Magpie-Multilingual.EN-US.Aria \\\n  --output output.wav\n```\n\n\n## Troubleshooting\n\n- **Synthesized audio > 4 MB** → gRPC response cap. Switch to `--stream`, or fall back to the WebSocket client for long-form output.\n- **HTTP streaming output won't open in a media player** → the stream is raw LPCM, not a WAV container. Wrap it with `sox` (see the Streaming Synthesis section above).\n- **`403` on container pull for Zeroshot \u002F Flow** → these are gated TTS models. Request access via NGC before re-pulling.\n- **Voice not found** → voice names are case-sensitive and dot-separated; pass `Magpie-Multilingual.EN-US.Aria` literally, not `aria` or `Aria`.\n\n## Limitations\n\n- x86_64 architecture only; NVIDIA AI Enterprise license required for self-hosting\n- gRPC responses are limited to 4 MB — long synthesis requests must use streaming or be chunked\n- HTTP streaming returns raw LPCM audio (not WAV) — requires client-side wrapping\n- Zeroshot voice cloning requires a reference audio clip; Flow model additionally requires a transcript\n- Restricted-access TTS models require explicit NVIDIA approval before use\n- Voice names are case-sensitive\n\n",{"data":35,"body":51},{"name":4,"license":26,"description":6,"metadata":36},{"author":37,"team":38,"tags":39,"domain":49,"version":50},"Mayank Jain \u003Cmayjain@nvidia.com>","riva",[8,38,40,41,14,42,43,44,45,46,47,48],"nim","tts","magpie","voice-synthesis","grpc","http","websocket","cloud","nvcf","ml","1.0.0",{"type":52,"children":53},"root",[54,63,69,83,105,109,116,121,127,132,157,163,228,234,239,309,315,349,380,403,462,474,482,690,725,765,768,774,814,825,831,841,853,859,864,973,984,1357,1363,1402,1408,1413,1421,1469,1477,1521,1540,1546,1553,1560,1668,1675,1779,1785,1792,1905,1913,2106,2112,2215,2221,2226,2460,2466,2640,2695,2714,2720,3000,3006,3011,3019,3175,3183,3270,3276,3361,3367,3400],{"type":55,"tag":56,"props":57,"children":59},"element","h1",{"id":58},"riva-tts-nim",[60],{"type":61,"value":62},"text","Riva TTS NIM",{"type":55,"tag":64,"props":65,"children":66},"p",{},[67],{"type":61,"value":68},"Riva TTS NIMs ship in two deployment shapes: cloud-hosted (managed by build.nvidia.com, no local GPU) and self-hosted (your own GPU plus Docker). 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Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3479,3482,3485],{"name":3480,"slug":3481,"type":15},"Documentation","documentation",{"name":3483,"slug":3484,"type":15},"MCP","mcp",{"name":3486,"slug":3487,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":3492,"name":3492,"fn":3493,"description":3494,"org":3495,"tags":3496,"stars":3503,"repoUrl":3504,"updatedAt":3505},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3497,3500,3501],{"name":3498,"slug":3499,"type":15},"Containers","containers",{"name":3448,"slug":3449,"type":15},{"name":3502,"slug":503,"type":15},"Python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":3507,"name":3507,"fn":3508,"description":3509,"org":3510,"tags":3511,"stars":3503,"repoUrl":3504,"updatedAt":3516},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3512,3515],{"name":3513,"slug":3514,"type":15},"CI\u002FCD","ci-cd",{"name":3448,"slug":3449,"type":15},"2026-07-14T05:25:59.97109",{"slug":3518,"name":3518,"fn":3519,"description":3520,"org":3521,"tags":3522,"stars":3503,"repoUrl":3504,"updatedAt":3528},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3523,3524,3525],{"name":3513,"slug":3514,"type":15},{"name":3448,"slug":3449,"type":15},{"name":3526,"slug":3527,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":3530,"name":3530,"fn":3531,"description":3532,"org":3533,"tags":3534,"stars":3503,"repoUrl":3504,"updatedAt":3542},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3535,3538,3539],{"name":3536,"slug":3537,"type":15},"Debugging","debugging",{"name":3526,"slug":3527,"type":15},{"name":3540,"slug":3541,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":3544,"name":3544,"fn":3545,"description":3546,"org":3547,"tags":3548,"stars":3503,"repoUrl":3504,"updatedAt":3555},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3549,3552],{"name":3550,"slug":3551,"type":15},"Best Practices","best-practices",{"name":3553,"slug":3554,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":3557,"name":3557,"fn":3558,"description":3559,"org":3560,"tags":3561,"stars":3503,"repoUrl":3504,"updatedAt":3569},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3562,3565,3568],{"name":3563,"slug":3564,"type":15},"Machine Learning","machine-learning",{"name":3566,"slug":3567,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":3571,"name":3571,"fn":3572,"description":3573,"org":3574,"tags":3575,"stars":3503,"repoUrl":3504,"updatedAt":3580},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3576,3579],{"name":3577,"slug":3578,"type":15},"QA","qa",{"name":3437,"slug":3438,"type":15},"2026-07-14T05:25:53.673039",{"slug":3582,"name":3582,"fn":3583,"description":3584,"org":3585,"tags":3586,"stars":3503,"repoUrl":3504,"updatedAt":3591},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. 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Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3612,3613],{"name":3577,"slug":3578,"type":15},{"name":3437,"slug":3438,"type":15},"2026-07-14T05:25:54.928983",{"slug":3616,"name":3616,"fn":3617,"description":3618,"org":3619,"tags":3620,"stars":3503,"repoUrl":3504,"updatedAt":3625},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3621,3624],{"name":3622,"slug":3623,"type":15},"Automation","automation",{"name":3513,"slug":3514,"type":15},"2026-07-30T05:29:03.275638",496]