Wendy Self Hosted logo

Wendy Self Hosted

Organization
wendylabsinc
wendy-self-hosted

The Wendy dual-path rule: the wendy stack must always run without the SaaS stack. MANDATORY when: (1) any feature integrates wendyos/wendy-agent with pki-core, cloud, or wendy-auth, (2) self-hosted, on-prem, or airgapped deployments, (3) deployment profiles or workload identity backends, (4) code that talks to GCP metadata, PSC, or Google JWKS, (5) degraded-mode or offline behavior, (6) making pki-core (or wendy-auth) self-hostable.

Overview

Publisherwendylabsinc
Repositoryclaude-skills
Skill namewendy-self-hosted
Stars
62
Forks
5
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wendylabsinc on GitHub. Read the source before you install it.

Installation

Install the Wendy Self Hosted AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/wendylabsinc/claude-skills/tree/main/wendy-self-hosted
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wendylabsinc/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/wendy-self-hosted .claude/skills/wendy-self-hosted
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wendy Self Hosted in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wendy Self Hosted on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wendy Self Hosted is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wendy Self-Hosted Rule

The wendy stack (wendyos / wendy-agent) must ALWAYS be able to run without the SaaS stack.

Every agent working on interop must keep the self-hosted path intact — or create it where it doesn't exist yet. A SaaS-only feature is an incomplete feature.

This is not a preference or a stretch goal. Wendy serves critical-infrastructure partners who will not — and in some cases legally cannot — run their fleet control loop through a vendor's multi-tenant cloud. If a change only works when pki-core, Wendy Cloud, or wendy-auth are the managed GCP services, it is not done.

Scope

  • wendy-agent / wendyos — always. The device fleet's core loop (enrollment, renewal, postbox polling, image pull) must work with zero SaaS reachability from the device's perspective beyond whatever the operator's own private infrastructure provides.
  • pki-core — must itself be self-hostable. It is the trust root for both profiles; it cannot itself hard-depend on GCP.
  • wendy-auth — potentially later, but write for it now. No path in wendy-auth service code should hard-assume GCP or SaaS reachability, even before a self-hosted wendy-auth ships. Retrofitting this later is far more expensive than not regressing it now.

The Two Deployment Profiles

The contract treats self-hosted as a first-class deployment profile, not an afterthought — cite this when someone claims SaaS-only is fine (§11.1):

Managed (GCP)Self-hosted
Network isolationPrivate Service Connect endpoints inside a VPC Service Controls perimeter — no public IPs, no open-internet egress for control-plane RPCsThe operator's own private network / service mesh; no PSC/VPC-SC
Transport auth (fabric)v1 target: uniform GCP-WI-OIDC across all fabric legs (Google-signed, audience-bound OIDC token, verified via Google's JWKS + a caller-service-account allowlist)mTLS carries the weight — retained as the self-hosted backend (post-v1) (§6, §11.1)

Either way, the invariant holds: authority is never transport-derived — the transport just says which service is calling; a principal's authority always rides as an in-message signed artifact.

Never Assume

No service or device code path may hard-assume reachability of:

  • The GCP metadata server
  • PSC (Private Service Connect) endpoints
  • VPC-SC (VPC Service Controls)
  • Google's JWKS endpoint
  • Artifact Registry
  • A reachable cloud control plane at all

The contract's degraded-mode behaviors exist precisely for this last one — e.g. online enrollment is designed to survive the cloud being unreachable at redemption time (v0.12, §5.4), and device runtime behavior fails safe (hold state, alert) rather than treating cloud reachability as a precondition for correctness. If a design only works with a live cloud connection, it needs a fail-safe degraded mode, not a shrug.

Provider/backend selection is by config, never by a hard-wired issuer: no code path may assume a specific identity provider or trust root (D11).

How to Add the Seam

When a feature needs a SaaS service (GCP metadata, PSC, a Google-issued token, cloud reachability, etc.), don't wire the SaaS backend in directly. Instead:

  1. Define the interface the feature actually needs (e.g. "give me a verifiable service identity," not "call the GCP metadata server").
  2. Make the managed (GCP) implementation one backend behind that interface — not the only path.
  3. Provide the self-hosted backend — or, if it isn't built yet, stub it behind the seam with a tracked issue. The seam existing and being selected by config is what matters; the self-hosted backend can land later, but the interface must not assume GCP going in.
  4. Select the backend by config, handing peers the matching trust bundle. No environment-sniffing, no "it's probably GCP" defaults baked into call sites.

Use the D11 pluggable-identity-provider pattern as the reference shape for this: see references/identity-backends.md for how the mesh workload-identity lifecycle implements exactly this seam with three interchangeable backends.

Source of Truth

This skill distills the AAA Contract v0.21 (Approved 2026-08-29). The living contract lives in Linear: AAA Contract — Authentication, Authorization, Accounting. It is versioned and evolving: check it for flow-level detail, and if it disagrees with this skill, the contract wins — then update this skill.

Related Skills

  • wendy-aaa-interop — the responsibility split and two-key authority this rule protects.
  • wendy-certificates — requesting and holding certificates; each backend here has to work.
  • wendy-device-enrollment — device lifecycle; online/offline enrollment both keep degraded mode.
  • wendy-device-interop — agent runtime behavior; the fail-safe catalogue this rule requires.
  • wendy-security-baselines — invariants any interop PR must respect, alongside this one.

Each of those skills carries a mandatory pointer back to this one — if you're touching interop code from any of them, this rule applies.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wendy Self Hosted AI skill do?

The Wendy dual-path rule: the wendy stack must always run without the SaaS stack. MANDATORY when: (1) any feature integrates wendyos/wendy-agent with pki-core, cloud, or wendy-auth, (2) self-hosted, on-prem, or airgapped deployments, (3) deployment profiles or workload identity backends, (4) code that talks to GCP metadata, PSC, or Google JWKS, (5) degraded-mode or offline behavior, (6) making pki-core (or wendy-auth) self-hostable.

Why use Wendy Self Hosted on TypingMind?

Because you install it once and use it with any model. Wendy Self Hosted is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wendy Self Hosted in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wendylabsinc/claude-skills/tree/main/wendy-self-hosted. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Wendy Self Hosted?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wendy Self Hosted?

As many as you like. As long as a model supports skills, you can use Wendy Self Hosted with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wendy Self Hosted AI skill free?

It is published on GitHub by wendylabsinc. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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