Agent Starters logo

Agent Starters

CommunityPopular
mvschwarz
agent-starters

Use when creating, refreshing, packaging, inspecting, promoting, or deprecating a named per-seat starting point — Agent Starter manifest authoring, the 6-state lifecycle (captured → named → inspectable → used → promoted → deprecated), provenance honesty, and refusal rules. NOT a VM image; a managed starting point composed from agent role + startup context + optional native session source + provenance.

Overview

Publishermvschwarz
Repositoryopenrig
Skill nameagent-starters
Stars
5K
Forks
366
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Agent Starters 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/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/agent-starters
  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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/specs/agents/shared/skills/core/agent-starters .claude/skills/agent-starters
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Starters 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 Agent Starters 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 Agent Starters 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.

Agent Starters

A named, reusable per-seat starting point. Composes an agent role, startup context, optional native session source, and provenance into something a user or rig can choose when creating, refreshing, or packaging a seat.

This registry-backed startup context is distinct from the rig agent-image surface and from a VM image. See the current behavior below before treating optional session provenance as an executable native session source.

Use this when

  • Creating a new seat from a known-good starting context.
  • Refreshing a seat with rig expand and starter_ref.
  • Authoring a new starter (registry entry).
  • Inspecting an existing starter's provenance, freshness, or recommended status.
  • Promoting / deprecating starters in the registry.
  • Composing a starter with a Composable Priming Pack (record manifest id/version, runtime, source session id or transcript path, ready-check evidence, freshness state).

Don't use this when

  • You want VM-style deterministic state capture. Starters don't capture VM state.
  • You want to copy provider auth material into a starter. Starters refer to session sources and context; they do NOT copy credentials.

What makes a starter valuable

A starter is valuable when it is functional — it carries the context the seat needs to do its task well. Functional is the only measure of a good starter. Size is not: a smaller starter is not a better one, and a bigger one is not worse. Whatever it took for the seat to become genuinely capable at its job is the right starter — 80K tokens or 800K.

Capture the seat as it naturally is at a functional, proven point. Don't pad it with context the seat doesn't use, and — just as important — don't strip context out to make it smaller. Size is an outcome of what the seat needed, never a target.

Never compact, summarize, or shrink a seat in order to make or "lean" a starter. There is nothing valuable in "smaller," and compaction is lossy — you would trade away the exact capability the starter exists to preserve. (Compaction is a separate last-resort step for a seat genuinely near its context limit, with its own before/after plan — never part of capturing a starter.)

Authoring lifecycle — 6 conceptual states

  1. Captured — a useful seat/session/context pattern is identified.
  2. Named — it becomes an Agent Starter with stable id and owner.
  3. Inspectable — runtime, context inputs, session source, and provenance are visible.
  4. Used — a rig member or rig expand operation starts from it.
  5. Promoted — evidence shows it is recommended for a role or bundle.
  6. Deprecated — replaced, stale, unsafe, or incompatible.

Failure modes (5)

  1. Overclaiming image semantics — UI/docs imply deterministic VM-style state capture. Say "starter," name what's included, show provenance.
  2. Hidden provenance — users can't tell what session, context, or spec a starter came from. Refuse promotion until provenance is inspectable.
  3. Stale starter — points at outdated doctrine, missing files, or invalid native session source. Inspect must report staleness honestly.
  4. Secret leakage — starter packages or displays provider auth material. Refuse. Refer to session sources and context, never copy credentials.
  5. Runtime mismatch — starter used with unsupported runtime. Refuse with a clear error.

Registry entry and current behavior

Save one entry as <registry-root>/reviewer-v1.yaml. The resolver selects an explicit root or OPENRIG_AGENT_STARTER_ROOT; absent those, it checks the home registry ~/.openrig/agent-starters and a configured fallback.

yaml
starter_id: reviewer-v1
role: Review the assigned change against its stated outcome.
context: Read the current task and the source needed to judge it.

The current resolver checks the entry shape and credential boundary, then delivers this YAML itself as one required guidance_merge startup file on a fresh start. It does not interpret arbitrary context refs, load a native conversation from this example, or seal an image. A RigSpec member combining starter_ref with session_source.mode: fork is currently rejected; use a separate supported session-source path when native continuity is the outcome.

Member usage:

yaml
members:
  - id: reviewer
    starter_ref:
      name: reviewer-v1

When a starter points at a primed session produced from a Composable Priming Pack, record:

  • manifest id/version
  • runtime
  • source session id or transcript path
  • ready-check evidence
  • freshness state

Proof matrix

SurfaceTest typeAuthority
Registry schema accepts minimal starterunitdaemon or config-layer prototype
Inspect shows provenance and included contextunit / snapshotdaemon or CLI
Member can use starter_refintegrationdaemon
Unsupported runtime or stale source refuses honestlyunit + integrationdaemon
No secret material copied into starter artifactgrep / fixturetester
Bundle can include or reference starterpackage inspectionbundle layer

Dependencies on other primitives

  • Separate path: session-source-fork — native conversation-source continuity; currently not composable with starter_ref
  • Firm: specification-system — declarative starter and member references
  • Soft: rig-bundles-and-shareable-artifacts — shareable starter packaging
  • Soft: context-engineering-and-retrieval — richer declarative context assembly
  • Soft: seat-continuity-and-handover — refresh and swap workflows over starters

Required-before-RSI

Agent Starters need queryable provenance and honest inspect output before RSI loops can rely on them for seat refresh. A workflow must be able to answer: "what starter did this seat use, what source session or context was included, and is that starter still recommended?"

See also

  • session-source-fork skill — low-level fork primitive that makes native session-based starters possible

Frequently asked questions

What does the Agent Starters AI skill do?

Use when creating, refreshing, packaging, inspecting, promoting, or deprecating a named per-seat starting point — Agent Starter manifest authoring, the 6-state lifecycle (captured → named → inspectable → used → promoted → deprecated), provenance honesty, and refusal rules. NOT a VM image; a managed starting point composed from agent role + startup context + optional native session source + provenance.

Why use Agent Starters on TypingMind?

Because you install it once and use it with any model. Agent Starters 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 Agent Starters in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/agent-starters. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Starters?

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 Agent Starters?

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

Is the Agent Starters AI skill free?

Yes. It is published on GitHub by mvschwarz under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇