Build production agent workflows with Tensorlake's.
This skill helps coding agents use Tensorlake to build real agent systems with sandboxed execution and orchestration. It covers both the Python (pip install tensorlake) and TypeScript (npm install tensorlake) SDKs. It is designed for modern agent use cases like multi-agent applications, isolated code execution, long-running workflows, and tool-using agents that need a real workspace.
Instead of treating Tensorlake as just another API, this skill teaches agents how to use Tensorlake as infrastructure: run tasks in isolated environments with the Sandbox SDK, coordinate durable workflows with the sandbox-native Orchestration SDK, and compose reliable agent systems for production use.
Use it when you want your coding agent to build:
- multi-agent applications
- sandboxed coding or execution workflows
- agent teams with separate workspaces
- long-running or stateful agent systems
- production-ready orchestration patterns
It guides agents to:
- use the Sandbox SDK for agent execution environments and isolated tool calls
- use the Orchestration SDK for sandbox-native durable workflow orchestration and multi-agent coordination
- combine both SDKs to build production-style agent systems
- choose Tensorlake patterns that are better than a single-agent or stateless approach
The skill is especially useful for tasks like:
- running code, scripts, or services inside isolated sandboxes
- giving each agent its own workspace, files, and execution environment
- building agentic applications with an orchestrator and specialist sub-agents
- coordinating parallel agents and collecting their outputs
- building demos and prototypes that show why agent infrastructure matters Works with any LLM provider (OpenAI, Anthropic) and any agent framework (LangChain, etc.). Tensorlake is the infrastructure layer — bring your own models and frameworks.
The skill triggers automatically when you ask the agent to:
- Run LLM-generated code in a secure sandbox
- Build agentic workflows or multi-agent pipelines
- Orchestrate complex multi-step AI applications
- Integrate Tensorlake with any LLM, framework, database, or API
- Ask questions about Tensorlake APIs or documentation
| Agent | File | How to Install |
|---|---|---|
| Claude Code | SKILL.md |
See Claude Code installation |
| Google ADK | SKILL.md |
See Google ADK installation |
| OpenAI Codex | AGENTS.md |
See Codex installation |
npx skills add tensorlakeai/tensorlake-skillsWorks with Claude Code, Cursor, Cline, GitHub Copilot, Windsurf, and more via skills.sh.
Tensorlake requires a TENSORLAKE_API_KEY configured in the local environment. Get one at cloud.tensorlake.ai, then either run tensorlake login (Python) / npx tl login (TypeScript) or configure the variable through your shell profile, .env file, or secret manager. Do not paste API keys into chat, commit them to source control, or print them in terminal output.
tensorlake-skills/
├── SKILL.md # Skill definition (Claude Code, Google ADK)
├── AGENTS.md # Skill definition (OpenAI Codex)
├── CHANGELOG.md # Changes tracked per SDK version
├── .claude-plugin/
│ ├── plugin.json # Claude Code plugin metadata
│ └── marketplace.json # Marketplace listing
├── scripts/
│ └── bump-version.sh # Version bump automation
├── .github/
│ ├── workflows/
│ │ └── sync-check.yml # Weekly drift detection (CI)
│ └── scripts/
│ ├── fetch_docs.py # Fetch live doc pages
│ ├── check_drift.py # Compare fetched vs bundled
│ └── sources.yaml # Map: reference file → source URLs
└── references/
├── applications_sdk.md # Orchestration API reference
├── sandbox_sdk.md # Sandbox API reference
├── sandbox_persistence.md # Sandbox state: snapshots, suspend/resume, state machine
├── documentai_sdk.md # DocumentAI API reference
├── integrations.md # Integration patterns (LangChain, OpenAI, ChromaDB, Qdrant, etc.)
├── platform.md # Webhooks, auth, access control, EU data residency
├── sandbox_advanced.md # Skills-in-sandboxes, AI code execution, data analysis, CI/CD
└── troubleshooting.md # Common issues, production integration, benchmarks
This skill uses SemVer for its own version, independent of the TensorLake SDK version it documents.
- Major — breaking changes (renamed/removed reference files, restructured skill)
- Minor — new reference files, significant content additions, new SDK version coverage
- Patch — fixes, small content updates, drift corrections
The TensorLake SDK version being documented is tracked separately in sources.yaml and in the source headers at the top of each reference file.
Use scripts/bump-version.sh to update the version across all files:
./scripts/bump-version.sh patch # 2.0.0 -> 2.0.1
./scripts/bump-version.sh minor # 2.0.0 -> 2.1.0
./scripts/bump-version.sh major # 2.0.0 -> 3.0.0
./scripts/bump-version.sh minor --sdk 0.5.0 # bump + update SDK version in changelogThe script:
- Reads the current version from
SKILL.mdfrontmatter - Bumps major, minor, or patch
- Updates
SKILL.mdandAGENTS.md - Stamps the
[Unreleased]section inCHANGELOG.mdwith the new version and today's date - Prints the git commands to commit and tag
# 1. Make your changes to reference files, SKILL.md, etc.
# 2. Add an [Unreleased] section to CHANGELOG.md with your changes
# 3. Run the bump script
./scripts/bump-version.sh minor
# 4. Commit, tag, and push
git add -A
git commit -m "release: v2.1.0"
git tag v2.1.0
git push origin HEAD && git push origin v2.1.0Each reference file has a source header that tracks which doc pages it was built from:
<!--
Source:
- https://docs.tensorlake.ai/sandboxes/lifecycle.md
- https://docs.tensorlake.ai/sandboxes/commands.md
SDK version: tensorlake 0.4.49
Last verified: 2026-04-22
-->The full mapping is in .github/scripts/sources.yaml.
A weekly GitHub Action (sync-check.yml) fetches the live TensorLake docs and compares them against the bundled reference files. If new APIs, removed endpoints, or changed signatures are detected, it opens a GitHub Issue with a summary of what drifted.
| Frequency | Action |
|---|---|
| Weekly (automated) | CI drift-check runs, opens issue if divergence detected |
| Per SDK release | Manual update of reference files + bump version |
| Monthly | Review gap coverage — are new doc pages appearing that need a new reference file? |
MIT