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Agent skill

Deep Researcher Deploy

by NVIDIA-AI-Blueprints in NVIDIA-AI-Blueprints/deep-researcher-agent

A skill your agent uses when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA Deep Researcher Agent Blueprint infrastructure.

Apache-2.0Auto-check: notesDevOps & Cloud

Install Deep Researcher Deploy

skills CLI
$ npx skills add NVIDIA-AI-Blueprints/deep-researcher-agent --skill deep-researcher-deploy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA-AI-Blueprints/deep-researcher-agent deep-researcher-deploy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA-AI-Blueprints/deep-researcher-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deep-researcher-deploy .claude/skills/deep-researcher-deploy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deep-researcher-deploy
GitHub stars
886
Token cost
~3.5k tokens
SKILL.md length
1,428 words
Files
18 (incl. references)
Skills in repo
15
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA Deep Researcher Agent Blueprint infrastructure.

  • Works in 5 steps: Locate or clone Deep Researcher Agent → Select the deployment mode → Prepare environment and secrets → …
  • Asked to install
  • SKILL.md covers When to Use This Skill, Prerequisites, Workflow and Version Compatibility, plus 5 more sections
  • Calls curl, docker and git; needs NVIDIA_API_KEY and TAVILY_API_KEY

What it does

Deep Researcher Deploy is an agent skill from NVIDIA-AI-Blueprints/deep-researcher-agent. Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA Deep Researcher Agent Blueprint infrastructure.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 19 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/configs.md`). Compatibility notes: Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools. Requires Git, network access to GitHub, and one selected runtime path: Docker…

It sits in DevOps & Cloud, covering Deployment. It works with NVIDIA AI Platform and Docker. The repository describes itself as: The NVIDIA Deep Researcher Agent Blueprint is an open reference example for building intelligent AI agents that connect to your enterprise data, reason using state-of-the-art… The licence is Apache-2.0.

When your agent uses it

  • Asked to install
  • Stop NVIDIA Deep Researcher Agent Blueprint infrastructure

Example prompts

  • “/deep-researcher-deploy”

Requirements

  • Python 3
  • Node.js
  • Docker
  • A credential in NVIDIA_API_KEY
  • A credential in TAVILY_API_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools. Requires Git, network access to GitHub, and one selected runtime path: Docker Compose v2 for the default local deployment, Python 3.11+ and uv for local process or CLI mode, Node.js 20+ and npm for local web UI mode, or kubectl 1.28+ and Helm 3.12+ for Kubernetes and Helm mode.
  • Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Locate or clone Deep Researcher Agent
  2. Select the deployment mode
  3. Prepare environment and secrets
  4. Route to the selected deployment path
  5. Validate and hand off

What it can do on your machine

Read from SKILL.md and the folder at commit 951a1a1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • docker
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use curl, docker and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • NVIDIA_API_KEY
    • TAVILY_API_KEY
    • SERPER_API_KEY
    • EXA_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools. Requires Git, network access to GitHub, and one selected runtime path: Docker Compose v2 for the default local deployment, Python 3.11+ and uv for local process or CLI mode, Node.js 20+ and npm for local web UI mode, or kubectl 1.28+ and Helm 3.12+ for Kubernetes and Helm mode.

    From compatibility in the SKILL.md frontmatter.

Context cost

Deep Researcher Deploy loads about 3.5k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 37 tokens; SKILL.md has 1,428 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:53
    Before writing secrets, verify `deploy/.env` is ignored:
  • NoteMentions a .env fileSKILL.md:56
    git check-ignore deploy/.env
  • NoteMentions a .env fileSKILL.md:59
    Expected output: `deploy/.env` or a matching ignore rule. If it is not ignored, stop and fix the ignore rule before
  • NoteMentions a .env fileSKILL.md:67
    4. Prepare `deploy/.env` without overwriting user secrets.
  • NoteMentions a .env fileSKILL.md:109
    -and-secrets.md` before changing `deploy/.env`.
  • NoteMentions a .env fileSKILL.md:112
    if [ ! -f deploy/.env ]; then
  • NoteMentions a .env fileSKILL.md:113
    cp deploy/.env.example deploy/.env
  • NoteMentions a .env fileSKILL.md:114
    echo "created deploy/.env from deploy/.env.example"
  • NoteMentions a .env fileSKILL.md:118
    hen the file is missing: `created deploy/.env from deploy/.env.example`. Expected output when the file
  • NoteMentions a .env fileSKILL.md:121
    missing, ask the user to update `deploy/.env`; do not ask them to paste

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA-AI-Blueprints/deep-researcher-agent at commit 951a1a1, republished under its Apache-2.0 licence (© NVIDIA-AI-Blueprints). 1,428 words, ~3,519 tokens.

Download SKILL.mdSave it as .claude/skills/deep-researcher-deploy/SKILL.md (or your agent's skills folder). This skill also uses 17 other files; get the full folder from GitHub.
name
deep-researcher-deploy
description
Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA Deep Researcher Agent Blueprint infrastructure.
allowed-tools
Read, Bash
compatibility
Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools. Requires Git, network access to GitHub, and one selected runtime path: Docker Compose v2 for the default local deployment, Python 3.11+ and uv for local process or CLI mode, Node.js 20+ and npm for local web UI mode, or kubectl 1.28+ and Helm 3.12+ for Kubernetes and Helm mode.
license
Apache-2.0
metadata.version
2.2.0
metadata.author
NVIDIA Deep Researcher Agent Blueprint Team <deep-researcher-blueprint@nvidia.com>
metadata.github-url
https://github.com/NVIDIA-AI-Blueprints/deep-researcher-agent
metadata.tags
nvidia, deep-researcher, blueprint, deploy, operations, agent-skills

Deep Researcher Agent Deploy Skill

When to Use This Skill

Use this skill to get a local or self-hosted NVIDIA Deep Researcher Agent Blueprint server running and verified for use by deep-researcher-research.

This skill owns setup, deployment, operational checks, troubleshooting, and shutdown. It does not run deep research itself. After deployment is healthy, hand off the verified server URL to deep-researcher-research. The workflow stays explicit so deployment validation and handoff are repeatable across supported agent clients.

Prerequisites

Users need:

  • Access to clone or update https://github.com/NVIDIA-AI-Blueprints/deep-researcher-agent.
  • Git available in the shell.
  • One deployment runtime:
    • Docker Engine with Docker Compose v2 for the default durable local deployment.
    • Python 3.11+ and uv for local process or CLI mode.
    • Node.js 20+ and npm for local browser UI development mode.
    • kubectl 1.28+, Helm 3.12+, and access to a Kubernetes cluster for Helm mode.
  • Network access to GitHub, NVIDIA-hosted model endpoints, and any selected search provider.
  • Credentials stored outside chat. Hosted-model usage requires NVIDIA_API_KEY; web research requires at least one supported search provider key such as TAVILY_API_KEY, SERPER_API_KEY, or EXA_API_KEY.
  • System capacity for the selected runtime. Docker Compose mode starts the Deep Researcher Agent backend and PostgreSQL by default; browser UI mode also uses frontend port 3000. Self-hosted model or RAG deployments may require GPU resources.

Before writing secrets, verify deploy/.env is ignored:

bash
git check-ignore deploy/.env

Expected output: deploy/.env or a matching ignore rule. If it is not ignored, stop and fix the ignore rule before placing credentials in the file.

Workflow

  1. Locate or clone the Deep Researcher Agent repository.
  2. Confirm the expected repository files exist.
  3. Select the deployment mode.
  4. Prepare deploy/.env without overwriting user secrets.
  5. Check runtime prerequisites for the selected path.
  6. Start the selected deployment.
  7. Run basic validation.
  8. Report the verified DEEP_RESEARCHER_SERVER_URL for deep-researcher-research.
  9. Ask whether to run optional deep research completion validation.
Step 1 - Locate or clone Deep Researcher Agent

If no Deep Researcher Agent checkout exists, read references/locate-or-clone.md before cloning. In an existing checkout, confirm the required files:

bash
pwd
test -f pyproject.toml
test -f deploy/.env.example
test -d configs

Expected output: pwd prints the Deep Researcher Agent repository path; the test commands exit with status 0 and no output.

Step 2 - Select the deployment mode

If the user asks to install, deploy, set up, or run Deep Researcher Agent without naming a mode, ask:

text
How do you want to run Deep Researcher Agent?

1. Skill backend - backend-only service for deep-researcher-research w/o browser UI.
2. CLI - interactive terminal Deep Researcher Agent.
3. UI - browser Deep Researcher Agent app with backend and frontend.
4. Custom - choose an existing Deep Researcher Agent config or review advanced customization docs before deployment.

Wait for the user's answer before starting services.

Do not ask this question when the user already specified a mode, such as Docker Compose, Helm, UI, CLI, or Agent Skill backend. Do not ask the full mode question when deep-researcher-research routed here because a deep research request needs a backend. In that case, prefer Agent Skill backend and ask only for permission to start it if needed.

Step 3 - Prepare environment and secrets

Read references/env-and-secrets.md before changing deploy/.env.

bash
if [ ! -f deploy/.env ]; then
  cp deploy/.env.example deploy/.env
  echo "created deploy/.env from deploy/.env.example"
fi

Expected output when the file is missing: created deploy/.env from deploy/.env.example. Expected output when the file already exists: no output, and the existing file is preserved.

Never print secret values. If credentials are missing, ask the user to update deploy/.env; do not ask them to paste secret values into chat.

Step 4 - Route to the selected deployment path

Match the user request, then read the referenced file before acting:

User IntentReference
No Deep Researcher Agent checkout exists, install Deep Researcher Agent, clone Deep Researcher Agent, locate reporeferences/locate-or-clone.md
Configure environment, check API keys, inspect .envreferences/env-and-secrets.md
Choose an Deep Researcher Agent workflow config, understand config files, set BACKEND_CONFIG or CONFIG_FILEreferences/configs.md
Backend-only local server for deep-researcher-research, Deep Researcher Agent as an Agent Skillreferences/skill-backend.md
Terminal assistant, CLI-only run, no web UIreferences/terminal-cli.md
Quick local development run, start UI/backend without containersreferences/local-web.md
Default durable local deployment, Docker Compose, containers, PostgreSQLreferences/docker-compose.md
Kubernetes, Helm, cluster deploymentreferences/kubernetes-helm.md
Foundational RAG / FRAG integrationreferences/frag.md
Basic health checks, shallow smoke checks, handoff to deep-researcher-researchreferences/validation.md
Optional deep research completion validationreferences/end-to-end-validation.md
Logs, unhealthy services, port conflicts, config failuresreferences/troubleshooting.md
Stop services, restart, rebuild, safe cleanupreferences/shutdown.md
Step 5 - Validate and hand off

After startup, read references/validation.md and run the appropriate checks for the selected mode. For the default local backend, verify health:

bash
curl -sf http://localhost:8000/health

Expected output: a successful JSON health response or an empty successful response depending on the server build. If the command fails, read references/troubleshooting.md and diagnose before claiming the backend is ready.

deep-researcher-research needs a reachable Deep Researcher Agent server URL. If the backend is on the default port, no extra configuration is needed:

bash
DEEP_RESEARCHER_SERVER_URL=http://localhost:8000

If the backend runs elsewhere, tell the user to set:

bash
export DEEP_RESEARCHER_SERVER_URL="http://localhost:<PORT>"

Do not continue into deep research or deep research completion validation unless the user asks for it or confirms the post-deploy validation prompt. This skill's success criterion is a deployed and basically validated server, not report generation quality.

Version Compatibility

IMPORTANT: This skill is designed for NVIDIA Deep Researcher Agent Blueprint version 2.2.0.

Semantic Versioning Compatibility Rules:

text
Skill version: X.Y.Z
Blueprint version: A.B.C

Compatible IF:
1. A == X (Major versions MUST match)
2. B >= Y (Minor version must be equal or greater)
3. C can be anything (Patch version does not affect compatibility)

Examples:

  • Skill version 2.2.0 is compatible with Blueprint version 2.2.0.
  • Skill version 2.2.0 is compatible with Blueprint version 2.3.0.
  • Skill version 2.2.0 is compatible with Blueprint version 2.2.5.
  • Skill version 2.2.0 is not compatible with Blueprint version 3.0.0.
  • Skill version 2.2.0 is not compatible with Blueprint version 2.1.0.

If your Blueprint version is not compatible:

  1. Check for an updated skill version matching your Blueprint version.
  2. Use a Blueprint version compatible with this skill.
  3. Proceed with caution only when the user accepts the compatibility risk; deployment commands or config names may have changed.
Show full SKILL.md (549 more words)Show less

Security Best Practices

  • Never print secret values. Check only whether required environment variables are set.
  • Store credentials in deploy/.env or environment variables, not in chat transcripts, shell history, committed files, or example commands.
  • Do not overwrite deploy/.env when it already exists.
  • Ask before destructive cleanup such as deleting Docker volumes with down -v.
  • Do not claim FRAG is ready unless both RAG_SERVER_URL and RAG_INGEST_URL are configured and reachable.
  • Run verification commands yourself when possible.

Limitations

  • This skill prepares and validates Deep Researcher Agent infrastructure; it does not judge deep research report quality.
  • It cannot provide or inspect secret values. Users must configure credentials outside chat.
  • Helm, FRAG, custom config, and self-hosted model paths depend on infrastructure the user controls.
  • Destructive cleanup, such as deleting Docker volumes, requires explicit user approval.

Examples

Example 1: Deploy a backend-only Skill server with Docker Compose
bash
test -f deploy/.env || cp deploy/.env.example deploy/.env
git check-ignore deploy/.env
cd deploy/compose
BUILD_TARGET=release docker compose --env-file ../.env -f docker-compose.yaml config --quiet
BUILD_TARGET=release docker compose --env-file ../.env -f docker-compose.yaml up -d --build deep-researcher-agent
curl -sf http://localhost:8000/health

Expected output:

text
deploy/.env
<docker compose starts deep-researcher-agent and dependencies>
<health endpoint returns a successful response>

If Docker, ports, credentials, or health checks fail, read references/troubleshooting.md before retrying.

Example 2: Hand off a non-default backend URL to deep-researcher-research
bash
export DEEP_RESEARCHER_SERVER_URL="http://localhost:8100"
curl -sf "$DEEP_RESEARCHER_SERVER_URL/health"

Expected output: a successful health response. Then tell the user to keep DEEP_RESEARCHER_SERVER_URL set before invoking deep-researcher-research.

References

TopicDocumentation
Locate or clone Deep Researcher Agentreferences/locate-or-clone.md
Environment and secretsreferences/env-and-secrets.md
Workflow configsreferences/configs.md
Agent Skill backendreferences/skill-backend.md
CLI deploymentreferences/terminal-cli.md
Local web deploymentreferences/local-web.md
Docker Compose deploymentreferences/docker-compose.md
Kubernetes and Helm deploymentreferences/kubernetes-helm.md
FRAG integrationreferences/frag.md
Basic validationreferences/validation.md
End-to-end validationreferences/end-to-end-validation.md
Troubleshootingreferences/troubleshooting.md
Shutdown and cleanupreferences/shutdown.md

Common Issues

Issue: Backend port is already in use

Symptoms:

  • Docker Compose fails to bind port 8000.
  • curl -sf http://localhost:8000/health reaches an unexpected service or fails.

Causes:

  • Another Deep Researcher Agent backend or local development server is already running.
  • PORT in deploy/.env conflicts with an existing process.

Solutions:

  1. Identify the process:
    bash
    lsof -nP -iTCP:8000 -sTCP:LISTEN
  2. Either stop the conflicting process with the user's approval or set a different port in deploy/.env, such as PORT=8100.
  3. Restart the selected deployment path and verify:
    bash
    curl -sf http://localhost:8100/health
Issue: Required credentials are missing

Symptoms:

  • Infrastructure starts, but model-backed chat or research requests fail.
  • Logs mention unauthorized, forbidden, invalid key, or missing provider configuration.

Causes:

  • NVIDIA_API_KEY is missing or empty.
  • No supported search provider key is configured for web research.

Solutions:

  1. Check presence without printing values by following references/env-and-secrets.md.
  2. Ask the user to update deploy/.env; do not ask them to paste secrets into chat.
  3. Rerun references/validation.md after the user updates credentials.
Issue: Backend is healthy but not compatible with deep-researcher-research

Symptoms:

  • /health succeeds, but /chat or /v1/jobs/async/agents fails.
  • deep-researcher-research reports that async agents are unavailable.

Causes:

  • The selected config is CLI-only or does not expose the web/API backend expected by the skill.
  • BACKEND_CONFIG or CONFIG_FILE points at the wrong Deep Researcher Agent config.

Solutions:

  1. Read references/configs.md and confirm the selected config is API-enabled.
  2. For the default Skill backend, use configs/config_web_default_llamaindex.yml.
  3. Restart the backend and rerun references/validation.md.
Issue: Docker cleanup would remove useful state

Symptoms:

  • Troubleshooting suggests docker compose down -v.
  • The user may have local PostgreSQL job or checkpoint data they want to keep.

Causes:

  • down -v removes Docker volumes.
  • Rebuilds and restarts are often enough for config or image changes.

Solutions:

  1. Prefer a normal restart from references/shutdown.md.
  2. Ask for explicit approval before running volume deletion.
  3. After cleanup, rerun deployment and validation from the selected route.

© NVIDIA-AI-Blueprints, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 17 other files (references) in skills/deep-researcher-deploy of NVIDIA-AI-Blueprints/deep-researcher-agent.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • references/configs.md
  • references/docker-compose.md
  • references/end-to-end-validation.md
  • references/env-and-secrets.md
  • references/frag.md
  • references/kubernetes-helm.md
  • references/local-web.md
  • references/locate-or-clone.md
  • references/shutdown.md
  • references/skill-backend.md
  • references/terminal-cli.md
  • references/troubleshooting.md
  • references/validation.md
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 951a1a1

Compare with similar skills

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Setup Workshopbrevdev/workshop-build-an-agent146—~2.3kAutomated safety check: NotesApache-2.0
Msa Search NimNVIDIA/skills3.6k1 repos~4.6kAutomated safety check: NotesApache-2.0
Nemotron Customizer AirgapNVIDIA-NeMo/Nemotron2.1k—~1.2kAutomated safety check: PassApache-2.0
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Works with

Categories

Questions about Deep Researcher Deploy

What does Deep Researcher Deploy do?

A skill your agent uses when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA Deep Researcher Agent Blueprint infrastructure. Deep Researcher Deploy is an agent skill from NVIDIA-AI-Blueprints/deep-researcher-agent. Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA Deep Researcher Agent Blueprint infrastructure.

When should I use Deep Researcher Deploy?

Deep Researcher Deploy fits situations like: asked to install; stop NVIDIA Deep Researcher Agent Blueprint infrastructure.

How do I install Deep Researcher Deploy in Claude Code?

Run `npx skills add NVIDIA-AI-Blueprints/deep-researcher-agent --skill deep-researcher-deploy -a claude-code`. Or copy the skill folder (skills/deep-researcher-deploy in NVIDIA-AI-Blueprints/deep-researcher-agent) into .claude/skills/deep-researcher-deploy in your project. Claude Code loads it when a task matches its description.

How do I install Deep Researcher Deploy in Codex?

Run `npx skills add NVIDIA-AI-Blueprints/deep-researcher-agent --skill deep-researcher-deploy -a codex`. Or copy the skill folder (skills/deep-researcher-deploy in NVIDIA-AI-Blueprints/deep-researcher-agent) into .agents/skills/deep-researcher-deploy in your project. Codex loads it when a task matches its description.

Can I use Deep Researcher Deploy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA-AI-Blueprints/deep-researcher-agent --skill deep-researcher-deploy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-researcher-deploy, .gemini/skills/deep-researcher-deploy, .github/skills/deep-researcher-deploy and .opencode/skills/deep-researcher-deploy in your project.

What does Deep Researcher Deploy need to run?

Going by SKILL.md and its folder, Deep Researcher Deploy needs the command-line tools its instructions call (curl, docker and git) and credentials named NVIDIA_API_KEY, TAVILY_API_KEY, SERPER_API_KEY and EXA_API_KEY. Our summary lists: Python 3; Node.js; Docker; A credential in NVIDIA_API_KEY; A credential in TAVILY_API_KEY. Its frontmatter pre-approves these tools: Read, Bash. Compatibility (from SKILL.md): Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools. Requires Git, network access to GitHub, and one selected runtime path: Docker Compose v2 for the default local deployment, Python 3.11+ and uv for local process or CLI mode, Node.js 20+ and npm for local web UI mode, or kubectl 1.28+ and Helm 3.12+ for Kubernetes and Helm mode. .

Does Deep Researcher Deploy access the network?

SKILL.md contains no URLs. Its commands use curl, docker and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Deep Researcher Deploy safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Deep Researcher Deploy use?

Deep Researcher Deploy is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deep Researcher Deploy use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.2k tokens, read only when the agent opens those files.

What are the alternatives to Deep Researcher Deploy?

Skills that share tags, products or a category with Deep Researcher Deploy: Setup Workshop (brevdev/workshop-build-an-agent, 146 stars), Msa Search Nim (NVIDIA/skills, 3.6k stars), Nemotron Customizer Airgap (NVIDIA-NeMo/Nemotron, 2.1k stars) and GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Researcher Deploy?

NVIDIA-AI-Blueprints (a GitHub organization) maintains it in NVIDIA-AI-Blueprints/deep-researcher-agent, which has 886 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA-AI-Blueprints/deep-researcher-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.