LangBot Deployment Guide
langbot-app/LangBot
Deploys and configures a LangBot instance with Docker Compose or Kubernetes, covering config.yaml, the Box sandbox runtime, the plugin runtime and the global API key.
Build a Go service binary, package it into a Docker image, import into k3d, and deploy via helm sync.
$ npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install michelangelo-ai/michelangelo ma-sandbox-deploy --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/michelangelo-ai/michelangelo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/ma-sandbox-deploy .claude/skills/ma-sandbox-deploy && rm -rf skills-srcUse ~/.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/
Install the "ma-sandbox-deploy" agent skill from https://github.com/michelangelo-ai/michelangelo/tree/main/.claude/skills/ma-sandbox-deploy into .claude/skills/ma-sandbox-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-sandbox-deploy", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/michelangelo-ai/michelangelo/tree/main/.claude/skills/ma-sandbox-deployType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install michelangelo-ai/michelangelo ma-sandbox-deploy --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/michelangelo-ai/michelangelo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/ma-sandbox-deploy .agents/skills/ma-sandbox-deploy && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ma-sandbox-deploy" agent skill from https://github.com/michelangelo-ai/michelangelo/tree/main/.claude/skills/ma-sandbox-deploy into .agents/skills/ma-sandbox-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-sandbox-deploy", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install michelangelo-ai/michelangelo ma-sandbox-deploy --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/michelangelo-ai/michelangelo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/ma-sandbox-deploy .cursor/skills/ma-sandbox-deploy && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ma-sandbox-deploy" agent skill from https://github.com/michelangelo-ai/michelangelo/tree/main/.claude/skills/ma-sandbox-deploy into .cursor/skills/ma-sandbox-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-sandbox-deploy", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/michelangelo-ai/michelangelo.git --path .claude/skills/ma-sandbox-deploy--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install michelangelo-ai/michelangelo ma-sandbox-deploy --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/michelangelo-ai/michelangelo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/ma-sandbox-deploy .gemini/skills/ma-sandbox-deploy && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ma-sandbox-deploy" agent skill from https://github.com/michelangelo-ai/michelangelo/tree/main/.claude/skills/ma-sandbox-deploy into .gemini/skills/ma-sandbox-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-sandbox-deploy", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install michelangelo-ai/michelangelo ma-sandbox-deployInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/michelangelo-ai/michelangelo.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/ma-sandbox-deploy .github/skills/ma-sandbox-deploy && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ma-sandbox-deploy" agent skill from https://github.com/michelangelo-ai/michelangelo/tree/main/.claude/skills/ma-sandbox-deploy into .github/skills/ma-sandbox-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-sandbox-deploy", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install michelangelo-ai/michelangelo ma-sandbox-deploy --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/michelangelo-ai/michelangelo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/ma-sandbox-deploy .opencode/skills/ma-sandbox-deploy && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ma-sandbox-deploy" agent skill from https://github.com/michelangelo-ai/michelangelo/tree/main/.claude/skills/ma-sandbox-deploy into .opencode/skills/ma-sandbox-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-sandbox-deploy", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ma-sandbox-deployBuild a Go service binary, package it into a Docker image, import into k3d, and deploy via helm sync.
Ma Sandbox Deploy is an agent skill from michelangelo-ai/michelangelo. Build a Go service binary, package it into a Docker image, import into k3d, and deploy via helm sync. Use when iterating on apiserver (or another Go service) and wanting to test changes in a running sandbox without waiting for CI.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Container orchestration and Deployment. It works with Docker. The repository describes itself as: Michelangelo AI: Uber's end-to-end machine learning platform. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit 491a9b2. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
gitdockerkubectlgoFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, docker and kubectl, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Ma Sandbox Deploy loads about 1.4k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 245 words of instructions outside code blocks.
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.
The automated check found no risky patterns in SKILL.md.
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.
The full file from michelangelo-ai/michelangelo at commit 491a9b2, republished under its Apache-2.0 licence (© michelangelo-ai). 245 words, ~1,404 tokens.
.claude/skills/ma-sandbox-deploy/SKILL.md (or your agent's skills folder).For iterating on Go services (primarily apiserver and controllermgr) inside a running sandbox. Requires a sandbox already running — if not, run ma sandbox create first (see /ma-sandbox-setup).
REPO_ROOT=$(git rev-parse --show-toplevel)Run this once at the start of your shell session, or prefix paths below with the result.
The steps below use apiserver as the example. See Adapting for controllermgr for the controllermgr-specific substitutions.
# 1. Build statically-linked linux/arm64 binary (CGO_ENABLED=0 required — Distroless base has no libc)
cd "$REPO_ROOT/go"
CGO_ENABLED=0 GOOS=linux GOARCH=arm64 go build -o /tmp/apiserver-local ./cmd/apiserver/
# 2. Copy config (only needed once, or when config files change)
cp -r "$REPO_ROOT/go/cmd/apiserver/config" /tmp/apiserver-config
# 3. Prepare a clean build context and build Docker image
mkdir -p /tmp/apiserver-build
cp /tmp/apiserver-local /tmp/apiserver-build/apiserver-local
cp -r /tmp/apiserver-config /tmp/apiserver-build/apiserver-config
docker build \
-f "$REPO_ROOT/docker/service.Dockerfile" \
--build-arg BINARY_PATH=apiserver-local \
--build-arg CONFIG_PATH=apiserver-config \
--platform linux/arm64 \
--label git.branch="$(git -C "$REPO_ROOT" rev-parse --abbrev-ref HEAD)" \
--label git.sha="$(git -C "$REPO_ROOT" rev-parse --short HEAD)" \
--label git.dirty="$(git -C "$REPO_ROOT" diff --quiet || echo true)" \
-t michelangelo-apiserver:local \
/tmp/apiserver-build/
# 4. Import image into k3d cluster
k3d image import michelangelo-apiserver:local -c michelangelo-sandbox
# 5. Sync sandbox with local image override
# IMPORTANT: pass --set images.apiserver=... on EVERY sync call — helm's --reuse-values
# silently reverts to the default ghcr.io image if you omit it
source "$REPO_ROOT/python/.venv/bin/activate"
ma sandbox sync \
--set images.apiserver=michelangelo-apiserver:local \
--set images.pullPolicy=IfNotPresent \
--set ui.enabled=true \
--set envoy.enabled=true \
--set controllermgr.enabled=true \
--set worker.enabled=trueCheck whether config files changed since the last sync:
git diff --name-only HEAD | grep 'cmd/apiserver/config/'IF that command returns no output — config is unchanged. Skip steps 2–3 (config copy) and run only the binary rebuild + import:
# Rebuild binary, re-import, rollout restart
CGO_ENABLED=0 GOOS=linux GOARCH=arm64 go build -o /tmp/apiserver-local "$REPO_ROOT/go/cmd/apiserver/"
cp /tmp/apiserver-local /tmp/apiserver-build/apiserver-local
docker build \
-f "$REPO_ROOT/docker/service.Dockerfile" \
--build-arg BINARY_PATH=apiserver-local \
--build-arg CONFIG_PATH=apiserver-config \
--platform linux/arm64 \
--label git.branch="$(git -C "$REPO_ROOT" rev-parse --abbrev-ref HEAD)" \
--label git.sha="$(git -C "$REPO_ROOT" rev-parse --short HEAD)" \
--label git.dirty="$(git -C "$REPO_ROOT" diff --quiet || echo true)" \
-t michelangelo-apiserver:local \
/tmp/apiserver-build/
k3d image import michelangelo-apiserver:local -c michelangelo-sandbox
kubectl rollout restart deployment/michelangelo-apiserver
kubectl rollout status deployment/michelangelo-apiserver --timeout=60sSubstitute these values in every step above:
| apiserver | controllermgr |
|---|---|
./cmd/apiserver/ | ./cmd/controllermgr/ |
/tmp/apiserver-local | /tmp/controllermgr-local |
/tmp/apiserver-config | /tmp/controllermgr-config |
/tmp/apiserver-build/ | /tmp/controllermgr-build/ |
michelangelo-apiserver:local | michelangelo-controllermgr:local |
--set images.apiserver=... | --set images.controllermgr=... |
deployment/michelangelo-apiserver | deployment/michelangelo-controllermgr |
app.kubernetes.io/component=apiserver | app.kubernetes.io/component=controllermgr |
Check whether cmd/controllermgr/ has a config/ directory — if not, omit steps 2 and the CONFIG_PATH build arg.
The :local tag carries no inherent identity — verify across three signals.
1. It's your local build, not the released image:
kubectl get pod -l app.kubernetes.io/component=apiserver \
-o jsonpath='{.items[0].spec.containers[0].image}'Should return michelangelo-apiserver:local, not ghcr.io/michelangelo-ai/apiserver:main.
2. Which branch/commit it was built from (requires the --label flags above):
docker image inspect michelangelo-apiserver:local --format '{{json .Config.Labels}}'
# {"git.branch":"craig.marker/fix-...","git.sha":"d1046e10","git.dirty":"true"}The labels are baked into the image, so they survive k3d image import — inspecting the
local image tells you what the imported (and running) image is.
3. It's your latest build, not a stale pod — compare image build time to pod start
time. If the pod started before your last build, you forgot to rollout restart:
docker image inspect michelangelo-apiserver:local --format 'built: {{.Created}}'
kubectl get pod -l app.kubernetes.io/component=apiserver \
-o jsonpath='started: {.items[0].status.startTime}{"\n"}'© michelangelo-ai, 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
Just SKILL.md in .claude/skills/ma-sandbox-deploy of michelangelo-ai/michelangelo.
Open the folder on GitHubat commit 491a9b2
Ma Sandbox Deploy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ma Sandbox Deploy this skillmichelangelo-ai/michelangelo | 118 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| LangBot Deployment Guidelangbot-app/LangBot | 18k | — | ~1.5k | Automated safety check: Notes | Apache-2.0 | |
| Debug Openshell ClusterNVIDIA/OpenShell | 16k | — | ~20k | Automated safety check: Notes | Apache-2.0 | |
| Aspire MonitoringCommunityToolkit/Aspire | 627 | — | ~3.5k | Automated safety check: Pass | MIT | |
| Demo Local Rolloutcarverauto/serviceradar | 921 | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Devops EngineerYikai-Liao/symusic | 189 | 1 repos | ~1.5k | Automated safety check: Pass | MIT |
langbot-app/LangBot
Deploys and configures a LangBot instance with Docker Compose or Kubernetes, covering config.yaml, the Box sandbox runtime, the plugin runtime and the global API key.
NVIDIA/OpenShell
Debug why an OpenShell gateway deployment is unhealthy, unreachable, or unable to create sandboxes.
CommunityToolkit/Aspire
ANALYSIS SKILL - Observe Aspire apps: logs, traces, metrics, resource state, telemetry export, browser telemetry, and the standalone dashboard.
carverauto/serviceradar
Build unpublished sha-... An agent skill from carverauto/serviceradar.
Yikai-Liao/symusic
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates.
matrixorigin/memoria
Deploy Memoria with Docker Compose or Kubernetes. An agent skill from matrixorigin/memoria.
michelangelo-ai/michelangelo
Tail logs, inspect pods, and diagnose unhealthy services in a running Michelangelo sandbox.
michelangelo-ai/michelangelo
Canonical setup sequence for the Michelangelo local sandbox.
michelangelo-ai/michelangelo
Build, test, and verify a sandbox change across Go, JS, and Python.
michelangelo-ai/michelangelo
Update Michelangelo documentation. An agent skill from michelangelo-ai/michelangelo.
michelangelo-ai/michelangelo
Structured interview for designing and implementing changes to the Michelangelo platform.
michelangelo-ai/michelangelo
Tear down the Michelangelo sandbox cluster and recreate it from scratch.
Works with
Categories
Build a Go service binary, package it into a Docker image, import into k3d, and deploy via helm sync. Ma Sandbox Deploy is an agent skill from michelangelo-ai/michelangelo. Build a Go service binary, package it into a Docker image, import into k3d, and deploy via helm sync.
Ma Sandbox Deploy fits situations like: iterating on apiserver (or another Go service) and wanting to test changes in a running sandbox without waiting for CI; tasks that involve Container orchestration; tasks that involve Deployment.
Run `npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a claude-code`. Or copy the skill folder (.claude/skills/ma-sandbox-deploy in michelangelo-ai/michelangelo) into .claude/skills/ma-sandbox-deploy in your project. Claude Code loads it when a task matches its description.
Run `npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-deploy -a codex`. Or copy the skill folder (.claude/skills/ma-sandbox-deploy in michelangelo-ai/michelangelo) into .agents/skills/ma-sandbox-deploy in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add michelangelo-ai/michelangelo --skill ma-sandbox-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/ma-sandbox-deploy, .gemini/skills/ma-sandbox-deploy, .github/skills/ma-sandbox-deploy and .opencode/skills/ma-sandbox-deploy in your project.
Going by SKILL.md and its folder, Ma Sandbox Deploy needs the command-line tools its instructions call (git, docker, kubectl and go). Our summary lists: Docker.
SKILL.md contains no URLs. Its commands use git and docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Ma Sandbox Deploy is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Ma Sandbox Deploy: LangBot Deployment Guide (langbot-app/LangBot, 18k stars), Debug Openshell Cluster (NVIDIA/OpenShell, 16k stars), Aspire Monitoring (CommunityToolkit/Aspire, 627 stars) and Demo Local Rollout (carverauto/serviceradar, 921 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
michelangelo-ai (a GitHub organization) maintains it in michelangelo-ai/michelangelo, which has 118 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 10, 2026.
Source: michelangelo-ai/michelangelo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.