[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7A3G5PKYPPXB9T2ABZ2GCM5":3,"skill-same:01M7A3G5PKYPPXB9T2ABZ2GCM5":91},{"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":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M7A3G5PKYPPXB9T2ABZ2GCM5","nemo-clinical-data-designer",null,"Use when generating synthetic tabular datasets via Data Designer — sampler columns, LLM columns, custom generators. Not for ASR audio.","leemiracle\u002Fwork4ai","leemiracle","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F10385913?v=4","01M79YCHARF56CHMGGTECMZN8Z","lee","01M79YCHBY9VH2CJW4P65D3DHR",4,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: nemo-clinical-data-designer\nlicense: \"Apache-2.0\"\ndescription: \"Use when generating synthetic tabular datasets via Data Designer — sampler columns, LLM columns, custom generators. Not for ASR audio.\"\nargument-hint: [describe the dataset you want to generate]\nmetadata:\n  author: \"Voice Eval Flywheel team \u003Cbrandoing@nvidia.com>\"\n  tags:\n    - nvidia\n    - data-designer\n    - synthetic-data\n    - dataset-generation\n    - llm\n    - sampler\n---\n\n# NeMo Clinical Data Designer\n\n## Purpose\n\nProduce a synthetic tabular dataset by authoring a NeMo Data Designer config (`data_designer.config`), previewing the output, then committing to a full run. The agent first picks a conversation mode (Interactive vs. Autopilot), drives the user through config construction, and ends at a written-to-disk dataset.\n\n## Instructions\n\nThe flow is five steps, in order:\n\n1. Decide on **conversation mode**. Default is Interactive — ask the user clarifying questions as you build. Switch to Autopilot whenever the user signals they want an opinionated build with minimal back-and-forth (\"just build it\", \"you decide\", \"surprise me\").\n2. Open the matching per-mode runbook — `workflows\u002Finteractive.md` or `workflows\u002Fautopilot.md` — and follow it. The runbooks are the source of truth for which questions to ask and what defaults to pick.\n3. Author a Python config module exposing `load_config_builder()` that returns a `DataDesignerConfigBuilder`. The Output Template section near the end of this file shows the skeleton (PEP 723 deps header + samplers + LLM columns + optional Pydantic schema + custom generators).\n4. Preview before generating: run `data-designer preview` against the config and show the user the sample rows.\n5. After approval, run `data-designer run` (the `run_script` entry point) for the full generation. If the dataset references person attributes, first inspect the person-object schema via `python3 scripts\u002Fget_person_object_schema.py`.\n\n## Examples\n\nWorked example dialogues live in the per-mode runbooks; the SKILL.md doesn't duplicate them:\n\n- For a question-driven build with full user collaboration → `workflows\u002Finteractive.md`.\n- For a hands-off \"you decide\" build → `workflows\u002Fautopilot.md`.\n\nA representative Python config — sampler column + LLM column + Pydantic-typed output + custom column generator — is laid out in the Output Template section further down.\n\n## Scripts\n\n| Script | Purpose |\n|---|---|\n| `scripts\u002Fget_person_object_schema.py` | Inspect the Data Designer person-object field schema (names, demographics, addresses) before adding person-sampler columns to a config. Invoke via `python3 scripts\u002Fget_person_object_schema.py`. |\n\n## Prerequisites\n\n- A Python ≥ 3.10 virtualenv (Data Designer requires it).\n- The `data-designer` package itself — install via `pip install data-designer`.\n- If the config you build references LLM columns or LLM judges, the user must have valid LLM credentials configured per Data Designer's own auth conventions.\n- `pydantic` is only needed when the config declares structured outputs; the PEP 723 header in the generated script will fetch it automatically.\n\n## Limitations\n\n- Output domain is tabular synthetic data exclusively — no audio, images, or unstructured documents. Clinical-ASR audio benchmarks belong to `\u002Fdigital-health-clinical-asr-build`, not here.\n- The skill writes the Python config file but does not invoke the generation pipeline on the user's behalf — the user runs `data-designer preview` and `data-designer run` themselves.\n- LLM columns require network reachability at preview and run time. In sandboxed environments those calls may be blocked; see Troubleshooting for the sandbox-disable handshake.\n\n## Brief to satisfy\n\n$ARGUMENTS\n\nThe brief above is what you need to translate into a Data Designer config. Don't open additional workspace files to \"look around\" — the selected workflow runbook walks you through any context-gathering you actually need (its Learn step). Trust the runbook over instinct here.\n\n## Mode selection + runbook\n\nA simple heuristic: if the user's framing is opinionated-and-hands-off (\"be opinionated\", \"you decide\", \"make reasonable assumptions\", \"just build it\", \"surprise me\", and similar), pick **Autopilot**. For everything else, default to **Interactive** so the user shapes column choices.\n\nAfter selecting, read *only* that mode's runbook and execute it linearly:\n\n- Interactive path → `workflows\u002Finteractive.md`.\n- Autopilot path → `workflows\u002Fautopilot.md`.\n\n## Authoring rules\n\nA handful of constraints that hold regardless of which mode you're in:\n\n- Output column retention defaults to *keep everything*. Only drop a column when the user explicitly asks, or when the column is a pure intermediate (e.g., a person-object sampler whose fields you extract into discrete columns downstream). Err toward keeping; the user can always trim later.\n- Seed datasets are never suggested unilaterally. Only wire one in when the user volunteers seed data or asks to build off existing records. If they do, consult `references\u002Fseed-datasets.md` before configuring the seed source.\n- Person attributes (names, demographics, addresses) require the person-sampling reference — read `references\u002Fperson-sampling.md` before adding any person columns.\n- If a config script already in the workspace matches the brief, surface the conflict to the user and ask whether to edit it in place or author a new one. Never silently overwrite.\n\n## Usage Tips and Common Pitfalls\n\n- **Sampler \u002F validation columns require both a type and a params object.** Pair `sampler_type=\"category\"` with `params=dd.CategorySamplerParams(...)`; either alone won't validate.\n- **Jinja2 in `prompt`, `system_prompt`, and `expr`** — top-level columns are `{{ column_name }}`, nested fields are `{{ column_name.field }}`.\n- **`SamplerColumnConfig` constructor takes `params`** — not `sampler_params`. Easy to typo if you're new to the library.\n- **Reading LLM-judge scores.** `LLMJudgeColumnConfig` returns a nested dict shaped `{score_name: {reasoning: str, score: int}}`. To pull the integer score in a Jinja template for a judge column `quality` with score `correctness`, write `{{ quality.correctness.score }}`. Dropping the trailing `.score` yields the full dict, which is almost never what you want.\n\n## Troubleshooting\n\n- **`data-designer: command not found`** → the package isn't installed in this environment. Tell the user, confirm they're on Python ≥ 3.10, and offer to create a venv + run `pip install data-designer` for them — but wait for explicit permission before installing.\n- **Preview fails on network errors** → most likely a sandbox is blocking outbound calls to the LLM endpoint. Ask whether to retry with the sandbox disabled. If that still fails, ask the user to run the command in their own shell.\n\n## Output deliverable\n\nA single Python file in the user's working directory exporting `load_config_builder()` → `DataDesignerConfigBuilder`. Filename should describe the dataset (`customer_reviews.py`, `medical_term_seeds.py`), not be generic. Use a PEP 723 inline metadata dep header so the file is runnable with `data-designer run` directly.\n\nFor the canonical skeleton — Pydantic-typed output, custom column generators, optional seed-dataset wiring — read `references\u002Foutput-template.md`. Strip blocks from the skeleton when the brief doesn't need them.\n\n","# NeMo Clinical Data Designer\n\n## Purpose\n\nProduce a synthetic tabular dataset by authoring a NeMo Data Designer config (`data_designer.config`), previewing the output, then committing to a full run. The agent first picks a conversation mode (Interactive vs. Autopilot), drives the user through config construction, and ends at a written-to-disk dataset.\n\n## Instructions\n\nThe flow is five steps, in order:\n\n1. Decide on **conversation mode**. Default is Interactive — ask the user clarifying questions as you build. Switch to Autopilot whenever the user signals they want an opinionated build with minimal back-and-forth (\"just build it\", \"you decide\", \"surprise me\").\n2. Open the matching per-mode runbook — `workflows\u002Finteractive.md` or `workflows\u002Fautopilot.md` — and follow it. The runbooks are the source of truth for which questions to ask and what defaults to pick.\n3. Author a Python config module exposing `load_config_builder()` that returns a `DataDesignerConfigBuilder`. The Output Template section near the end of this file shows the skeleton (PEP 723 deps header + samplers + LLM columns + optional Pydantic schema + custom generators).\n4. Preview before generating: run `data-designer preview` against the config and show the user the sample rows.\n5. After approval, run `data-designer run` (the `run_script` entry point) for the full generation. If the dataset references person attributes, first inspect the person-object schema via `python3 scripts\u002Fget_person_object_schema.py`.\n\n## Examples\n\nWorked example dialogues live in the per-mode runbooks; the SKILL.md doesn't duplicate them:\n\n- For a question-driven build with full user collaboration → `workflows\u002Finteractive.md`.\n- For a hands-off \"you decide\" build → `workflows\u002Fautopilot.md`.\n\nA representative Python config — sampler column + LLM column + Pydantic-typed output + custom column generator — is laid out in the Output Template section further down.\n\n## Scripts\n\n| Script | Purpose |\n|---|---|\n| `scripts\u002Fget_person_object_schema.py` | Inspect the Data Designer person-object field schema (names, demographics, addresses) before adding person-sampler columns to a config. Invoke via `python3 scripts\u002Fget_person_object_schema.py`. |\n\n## Prerequisites\n\n- A Python ≥ 3.10 virtualenv (Data Designer requires it).\n- The `data-designer` package itself — install via `pip install data-designer`.\n- If the config you build references LLM columns or LLM judges, the user must have valid LLM credentials configured per Data Designer's own auth conventions.\n- `pydantic` is only needed when the config declares structured outputs; the PEP 723 header in the generated script will fetch it automatically.\n\n## Limitations\n\n- Output domain is tabular synthetic data exclusively — no audio, images, or unstructured documents. Clinical-ASR audio benchmarks belong to `\u002Fdigital-health-clinical-asr-build`, not here.\n- The skill writes the Python config file but does not invoke the generation pipeline on the user's behalf — the user runs `data-designer preview` and `data-designer run` themselves.\n- LLM columns require network reachability at preview and run time. In sandboxed environments those calls may be blocked; see Troubleshooting for the sandbox-disable handshake.\n\n## Brief to satisfy\n\n$ARGUMENTS\n\nThe brief above is what you need to translate into a Data Designer config. Don't open additional workspace files to \"look around\" — the selected workflow runbook walks you through any context-gathering you actually need (its Learn step). Trust the runbook over instinct here.\n\n## Mode selection + runbook\n\nA simple heuristic: if the user's framing is opinionated-and-hands-off (\"be opinionated\", \"you decide\", \"make reasonable assumptions\", \"just build it\", \"surprise me\", and similar), pick **Autopilot**. For everything else, default to **Interactive** so the user shapes column choices.\n\nAfter selecting, read *only* that mode's runbook and execute it linearly:\n\n- Interactive path → `workflows\u002Finteractive.md`.\n- Autopilot path → `workflows\u002Fautopilot.md`.\n\n## Authoring rules\n\nA handful of constraints that hold regardless of which mode you're in:\n\n- Output column retention defaults to *keep everything*. Only drop a column when the user explicitly asks, or when the column is a pure intermediate (e.g., a person-object sampler whose fields you extract into discrete columns downstream). Err toward keeping; the user can always trim later.\n- Seed datasets are never suggested unilaterally. Only wire one in when the user volunteers seed data or asks to build off existing records. If they do, consult `references\u002Fseed-datasets.md` before configuring the seed source.\n- Person attributes (names, demographics, addresses) require the person-sampling reference — read `references\u002Fperson-sampling.md` before adding any person columns.\n- If a config script already in the workspace matches the brief, surface the conflict to the user and ask whether to edit it in place or author a new one. Never silently overwrite.\n\n## Usage Tips and Common Pitfalls\n\n- **Sampler \u002F validation columns require both a type and a params object.** Pair `sampler_type=\"category\"` with `params=dd.CategorySamplerParams(...)`; either alone won't validate.\n- **Jinja2 in `prompt`, `system_prompt`, and `expr`** — top-level columns are `{{ column_name }}`, nested fields are `{{ column_name.field }}`.\n- **`SamplerColumnConfig` constructor takes `params`** — not `sampler_params`. Easy to typo if you're new to the library.\n- **Reading LLM-judge scores.** `LLMJudgeColumnConfig` returns a nested dict shaped `{score_name: {reasoning: str, score: int}}`. To pull the integer score in a Jinja template for a judge column `quality` with score `correctness`, write `{{ quality.correctness.score }}`. Dropping the trailing `.score` yields the full dict, which is almost never what you want.\n\n## Troubleshooting\n\n- **`data-designer: command not found`** → the package isn't installed in this environment. Tell the user, confirm they're on Python ≥ 3.10, and offer to create a venv + run `pip install data-designer` for them — but wait for explicit permission before installing.\n- **Preview fails on network errors** → most likely a sandbox is blocking outbound calls to the LLM endpoint. Ask whether to retry with the sandbox disabled. If that still fails, ask the user to run the command in their own shell.\n\n## Output deliverable\n\nA single Python file in the user's working directory exporting `load_config_builder()` → `DataDesignerConfigBuilder`. Filename should describe the dataset (`customer_reviews.py`, `medical_term_seeds.py`), not be generic. Use a PEP 723 inline metadata dep header so the file is runnable with `data-designer run` directly.\n\nFor the canonical skeleton — Pydantic-typed output, custom column generators, optional seed-dataset wiring — read `references\u002Foutput-template.md`. Strip blocks from the skeleton when the brief doesn't need them.\n\n",{"name":5,"license":15,"metadata":24,"description":7,"argument-hint":33},{"tags":25,"author":32},[26,27,28,29,30,31],"nvidia","data-designer","synthetic-data","dataset-generation","llm","sampler","Voice Eval Flywheel team \u003Cbrandoing@nvidia.com>",[34],"describe the dataset you want to generate","main","ffa016d3c9a8d32821f71f6d9d916c30e04fcb67","讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer",0,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai","https:\u002F\u002Fgithub.com\u002Fleemiracle","i have some experience that make me better, so i want more. follow and star me would be a good choice.","2026-11-12 17:16:40",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add leemiracle\u002Fwork4ai --skill nemo-clinical-data-designer",{"tool":49,"command":50},"bunx","bunx skills add leemiracle\u002Fwork4ai --skill nemo-clinical-data-designer",{"tool":52,"command":53},"pnpm","pnpm dlx skills add leemiracle\u002Fwork4ai --skill nemo-clinical-data-designer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002FSKILL.md",true,[57,63,67,71,75,79,83,87],{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"BENCHMARK.md","REFERENCE","参考文档",3829,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002FBENCHMARK.md",{"path":64,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":65,"sourceHtmlUrl":66},"references\u002Foutput-template.md",2134,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002Freferences\u002Foutput-template.md",{"path":68,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":69,"sourceHtmlUrl":70},"references\u002Fperson-sampling.md",2356,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002Freferences\u002Fperson-sampling.md",{"path":72,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fpreview-review.md",1875,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002Freferences\u002Fpreview-review.md",{"path":76,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fseed-datasets.md",1170,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002Freferences\u002Fseed-datasets.md",{"path":80,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":81,"sourceHtmlUrl":82},"skill-card.md",3859,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002Fskill-card.md",{"path":84,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":85,"sourceHtmlUrl":86},"workflows\u002Fautopilot.md",2631,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002Fworkflows\u002Fautopilot.md",{"path":88,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":89,"sourceHtmlUrl":90},"workflows\u002Finteractive.md",3322,"https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fdigital-health-skills\u002Frepo-docs\u002Fskills\u002Fnemo-clinical-data-designer\u002Fworkflows\u002Finteractive.md",[92,99,106,113,120,127],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":19},"01M7A57J4E78QJZS8MA72P82DV","agentic-ecology-ui","Guidelines and templates for creating a generic web UI to visualize, rank, filter, and annotate vector databases. The UI has a hierarchical structure covering database selection, label selection\u002Fdefinition, ranking\u002Ffiltering configuration, and row visualization\u002Fannotation. Use when building, serving, or adapting an interactive web UI for Hoplite vector databases.","https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透google-deepmind\u002Fagentic_ecology\u002Frepo-docs\u002Fskills\u002Fagentic-ecology-ui\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":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":19},"01M7A57C4RGS1A7VM6CNPNQDBH","agentic-ecology-init","Initializes a local uv-managed project directory for agentic ecology workloads. Sets up Python dependencies using reference pyproject.toml and uv.lock, configures workspace rules, and ensures Agentic Ecology skills are discoverable. Use when initializing a new project workspace or bootstrapping a clean environment.","https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透google-deepmind\u002Fagentic_ecology\u002Frepo-docs\u002Fskills\u002Fagentic-ecology-init\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":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":19},"01M7A576Q1GYF5GAQCWGPPDQWZ","agentic-ecology-camera-traps","Provides capabilities to run SpeciesNet detector and classifier on camera trap images, extract crop-level feature embeddings, and populate a Hoplite vector database for downstream search and agile modeling. Use when ingesting camera trap images, classifying wildlife photos, or indexing camera trap data.","https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透google-deepmind\u002Fagentic_ecology\u002Frepo-docs\u002Fskills\u002Fagentic-ecology-camera-traps\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":19},"01M7A5708M54MMTKXQARDCJQMW","agentic-ecology-bioacoustics","Provides bioacoustic analysis capabilities for ecologists and researchers using the perch-hoplite Python package. A typical use case is to use agile modeling to bootstrap the creation and deployment of a bespoke detector for targeted species on an existing collection of passive acoustic monitoring recordings. Use when processing and annotating audio recordings for bioacoustic applications.","https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透google-deepmind\u002Fagentic_ecology\u002Frepo-docs\u002Fskills\u002Fagentic-ecology-bioacoustics\u002FSKILL.md",[],[],{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":19},"01M7A53GYCZY3AT9C2FR44TC35","sample-skill","Sample skill used by agent-env integration tests. Exercises SKILL.md parsing plus supporting scripts and references.","https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透browser-use\u002Fagentenv-framework\u002Frepo-docs\u002Ftst\u002Fdata\u002Fskills\u002Fsample_skill\u002FSKILL.md",[],[],{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":133,"publishedAt":19},"01M7A4KD50DHSNH1SCNKP1J6S1","simready-foundation-validate-foundation-change","Use for auditing SimReady requirement, validator, feature, profile, adapter, test, and skill consistency.","https:\u002F\u002Fgithub.com\u002Fleemiracle\u002Fwork4ai\u002Fblob\u002Fmain\u002F讲透宇宙\u002F05-侦\u002F讲透NVIDIA\u002Fsimready-foundation\u002Frepo-docs\u002F.codex\u002Fskills\u002Fsimready-foundation-validate-foundation-change\u002FSKILL.md",[],[]]