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Configure Server

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jpicklyk
configure-server

Configures how the MCP Task Orchestrator SERVER runs and is reached — transport (HTTP vs STDIO), the REST API, port publishing, config mounts, and config-sync. Use when a user says: run the server, register the image, set up the Docker container, enable the REST API, set up config-sync, reconfigure the server, change transport, expose the API, or reconnect to a different endpoint. NOT for first-time onboarding (that's quick-start) and NOT for note schemas / gates / traits / actor_authentication policy (that's manage-schemas) — this skill only decides how the container is launched and reached.

Overview

Publisherjpicklyk
Repositorytask-orchestrator
Skill nameconfigure-server
Stars
204
Forks
22
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Configure Server 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.
  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/jpicklyk/task-orchestrator.git /tmp/task-orchestrator
mkdir -p .claude/skills
cp -r /tmp/task-orchestrator/claude-plugins/task-orchestrator/skills/configure-server .claude/skills/configure-server
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Configure Server 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 Configure Server 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 Configure Server 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.

Configure Server — Runtime & Transport Setup

Decides how the MCP Task Orchestrator container is launched and reached: transport, REST API mode, port publishing, config mount, and config-sync. This is a runtime/deployment concern, distinct from quick-start (first-time onboarding narrative) and manage-schemas (workflow gates/traits/ resources:/actor_authentication content inside .taskorchestrator/config.yaml). If the user wants schema or gate changes — including resource-lease declarations — redirect to /manage-schemas instead of proceeding here.

One operator escape hatch worth knowing when launching the container: RESOURCE_LEASES_ENFORCED=false (env, default true) disables resource-lease gate enforcement server-wide — a kill switch for lease contention incidents, same gate-policy category as DEGRADED_MODE_POLICY. Configuring which resources exist and which traits declare them stays in /manage-schemas; this skill only knows the switch.

The full fragment catalog (exact env tuples, loopback caveat, Windows/MSYS caveat, .mcp.json shapes) lives in references/runtime-config.md — this skill's job is the decision flow and rendering, not re-deriving that catalog. Read it before rendering any command.


Step 1 — Offer the recommended default first

Before walking the full decision tree, offer the one-tap recommended path via AskUserQuestion:

AskUserQuestion(questions: [{
  question: "How do you want to run the server?",
  header: "Server setup",
  multiSelect: false,
  options: [
    { label: "Recommended default", description: "HTTP + REST API enabled, unauthenticated, loopback-bound (127.0.0.1). Enables config-sync out of the box. Best for a single developer working across multiple projects." },
    { label: "Customize", description: "Walk through transport, REST mode, config mount, and debug logging one at a time." }
  ]
}])

Recommended default → skip straight to Step 5 (Render — HTTP) with: REST = unauthenticated, config mount = none, debug = off. Customize → Step 2.

Why this is the default: it is the majority deployment shape for a single developer who wants config-sync (per-project config that hot-reloads without a restart) to just work across every project they open, without hand-managing tokens. The image itself still ships conservative (STDIO, REST off, 0.0.0.0 bind) — this posture is entirely rendered by this skill, never an image change.


Step 2 — Transport

AskUserQuestion(questions: [{
  question: "Which transport?", header: "Transport", multiSelect: false,
  options: [
    { label: "HTTP (Recommended)", description: "Detached daemon, serves /mcp on a published port. Required for REST API and config-sync." },
    { label: "STDIO", description: "Per-session process, no port, no REST API, no config-sync. Simpler, no persistent daemon." }
  ]
}])

STDIO ⊥ REST — hard constraint: if the user picks STDIO, skip Steps 3-4 entirely (REST mode and port are incoherent for a --rm -i per-session process) and go straight to Step 5 (Render — STDIO). Tell the user plainly: "STDIO has no REST API and no config-sync — those require a persistent HTTP daemon. If you want config-sync later, re-run this skill and choose HTTP."

HTTP → Step 3.


Step 3 — REST API mode (HTTP only)

AskUserQuestion(questions: [{
  question: "REST API mode?", header: "REST API", multiSelect: false,
  options: [
    { label: "Unauthenticated (Recommended)", description: "No token needed. Loopback-bound only. Enables config-sync with zero extra setup." },
    { label: "Bearer token", description: "Token-authenticated. For shared/multi-user setups." },
    { label: "Off", description: "MCP only, no REST, no config-sync." }
  ]
}])
  • Unauthenticated → always render the loopback SECURITY caveat from references/runtime-config.md ("Loopback footgun") before the command, and force -p 127.0.0.1:3001:3001 in the render — never a wider publish.
  • Bearer → ask for the host path to the token YAML (free-text/"Other" answer), e.g. ~/.taskorchestrator-secrets/api-tokens.yaml. If the file doesn't exist yet, point at current/docs/api-rest.md §1 for the token-generation snippet — this skill does not generate tokens.
  • Off → REST fragment is just -e API_ENABLED=false; config-sync will no-op (tell the user).

Step 4 — Config mount and debug (HTTP only)

AskUserQuestion(questions: [{
  question: "Mount this project's config as the server's global/fallback config?", header: "Config mount", multiSelect: false,
  options: [
    { label: "This project (fallback)", description: "Mount ./.taskorchestrator read-only as AGENT_CONFIG_DIR — good for a single-project server." },
    { label: "None (multi-project)", description: "No mount. Per-project config flows in via config-sync into the DB per root — good for one server shared across projects." }
  ]
}])

Then ask Yes/No for debug logging (LOG_LEVEL=DEBUG + DATABASE_SHOW_SQL=true).


Step 5 — Render

Look up the exact fragments in references/runtime-config.md — do not improvise env values.

STDIO

Render the .mcp.json args array shape (see reference doc, ".mcp.json shapes"):

json
{
  "mcpServers": {
    "mcp-task-orchestrator": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "-v", "mcp-task-data:/app/data", "ghcr.io/jpicklyk/task-orchestrator:latest"]
    }
  }
}

Add the config-mount fragment (as args entries, not env flags) if the user wants a project mount. No REST, no port, no config-sync — say so.

HTTP — three coordinated pieces (all required, this is the default path now)

  1. The detached docker run command — compose the recommended-default tuple (or the customized equivalent) from references/runtime-config.md:

    docker run -d --name mcp-task-orchestrator-http --restart unless-stopped \
      -v mcp-task-data:/app/data \
      -e MCP_TRANSPORT=http -e API_ENABLED=true -e API_AUTH_MODE=none -e API_ALLOW_UNAUTHENTICATED=true \
      -p 127.0.0.1:3001:3001 \
      ghcr.io/jpicklyk/task-orchestrator:latest

    (Substitute the REST-off or bearer fragment, and add the config-mount / debug fragments, per the user's Step 3-4 answers.) On Windows, run this via PowerShell with ${PWD} for volume paths — see "Windows / MSYS path caveat" in the reference doc.

  2. The .mcp.json HTTP entry (NOT an args array):

    json
    {
      "mcpServers": {
        "mcp-task-orchestrator": {
          "type": "http",
          "url": "http://localhost:3001/mcp"
        }
      }
    }
  3. Client-side config-sync env — export in the user's own shell/profile, not the container:

    TASK_ORCHESTRATOR_API_URL=http://localhost:3001

    Add TASK_ORCHESTRATOR_API_TOKEN=<token> only for bearer mode. Omitting this env var is the single most common way config-sync silently no-ops (verified config-sync.mjs:84-92) — always render it, never treat it as optional polish.

If REST mode is unauthenticated, always print the SECURITY caveat (verbatim from references/runtime-config.md → "Loopback footgun") immediately before or after the docker run block.


Step 6 — HTTP lifecycle (verify it's actually working)

For any HTTP render, walk through:

  1. Run the docker command from Step 5.
  2. Verify the container is up:
    bash
    docker ps --filter name=mcp-task-orchestrator-http --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
    docker logs --since 20s mcp-task-orchestrator-http
  3. Reconnect the client: update .mcp.json (Step 5, piece 2) if not already in place, then run /mcp in Claude Code — confirm mcp-task-orchestrator shows connected with all tools listed.
  4. If REST is enabled, sanity-check it: curl http://localhost:3001/api/v1/health should return 200.

HTTP-first policy for new infrastructure features

New infrastructure features — config-sync, SSE events, the plan-capture hook — are HTTP-only, with a graceful no-op on STDIO. Each checks for its own REST env var (TASK_ORCHESTRATOR_API_URL, etc.) and silently skips when absent, rather than failing. STDIO remains fully supported for MCP tool calls themselves — it is positioned as the local/evaluation mode: no persistent daemon, no REST surface, and consequently none of these convenience features. When recommending a setup for ongoing project work (not a one-off trial), prefer the HTTP render in Step 5 for this reason, in addition to config-sync's per-project hot-reload benefit already covered in Step 1.

Reconfiguring later

Re-running this skill is safe — it always renders a fresh command from the current answers; it does not read or depend on any previously-rendered state. To change an existing container's settings, stop/remove it first (docker stop mcp-task-orchestrator-http && docker rm mcp-task-orchestrator-http), then re-render and run the new command. (Maintainers building the image from source have a dedicated detect-and-reuse flow in /deploy_to_docker — not needed for the published-image path this skill covers.)

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 Configure Server AI skill do?

Configures how the MCP Task Orchestrator SERVER runs and is reached — transport (HTTP vs STDIO), the REST API, port publishing, config mounts, and config-sync. Use when a user says: run the server, register the image, set up the Docker container, enable the REST API, set up config-sync, reconfigure the server, change transport, expose the API, or reconnect to a different endpoint. NOT for first-time onboarding (that's quick-start) and NOT for note schemas / gates / traits / actor_authentication policy (that's manage-schemas) — this skill only decides how the container is launched and reached.

Why use Configure Server on TypingMind?

Because you install it once and use it with any model. Configure Server 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 Configure Server in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jpicklyk/task-orchestrator/tree/main/claude-plugins/task-orchestrator/skills/configure-server. 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 Configure Server?

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 Configure Server?

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

Is the Configure Server AI skill free?

Yes. It is published on GitHub by jpicklyk under the MIT license. 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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