Agent skill

Agent Starters

by mvschwarz in mvschwarz/openrig

Covers authoring, inspecting, refreshing, promoting and deprecating named Agent Starters, the reusable starting points for agent seats in a rig.

Apache-2.0Auto-check passedAgent Workflows

Install Agent Starters

skills CLI
$ npx skills add mvschwarz/openrig --skill agent-starters -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mvschwarz/openrig agent-starters --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/_canonical/core/agent-starters .claude/skills/agent-starters && rm -rf skills-src

Use ~/.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/

Facts

Skill name
agent-starters
GitHub stars
5.9k
Token cost
~1.7k tokens
SKILL.md length
796 words
Files
1
Skills in repo
49
Repo updated
First seen
Licence
Apache-2.0

At a glance

Covers authoring, inspecting, refreshing, promoting and deprecating named Agent Starters, the reusable starting points for agent seats in a rig.

  • Works in 6 steps: Captured — a useful seat/session/context… → Named — it becomes an Agent Starter with… → Inspectable — runtime, context inputs,… → …
  • Creating a new seat from a known-good starting context
  • SKILL.md covers Use this when, Don't use this when, What makes a starter valuable and Authoring lifecycle — 6…, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

An Agent Starter is a named, reusable starting point for one seat in a rig. It combines an agent role, startup context, an optional native session source and provenance, and can be chosen when creating, refreshing or packaging a seat. It is a registry-backed context rather than a VM image, so it does not capture VM state or copy provider credentials; starters only refer to session sources.

The skill covers writing a registry entry and refreshing a seat with rig expand and starter_ref. A starter moves through six states: captured, named, inspectable, used, promoted and deprecated. Provenance has to be recorded honestly, including manifest id and version, runtime, source session id or transcript path, ready-check evidence and freshness when a starter is composed with a priming pack. A starter is judged only by whether it carries the context the seat needs, and the skill says never to compact or shrink a seat just to get a smaller one.

When your agent uses it

  • Creating a new seat from a known-good starting context
  • Refreshing a seat with rig expand and a starter reference
  • Inspecting a starter's provenance, freshness or recommended status
  • Promoting or deprecating starters in the registry

Example prompts

  • “Capture the reviewer seat as a named starter and write its manifest.”
  • “Show me the provenance and freshness of the starter our QA seat was built from.”
  • “Deprecate the old research starter and promote the refreshed one.”

Requirements

  • The rig CLI with its starter registry

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit 1f69831. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Starters loads about 1.7k tokens when it runs. Until then it costs about 105 tokens; SKILL.md has 796 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~105
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from mvschwarz/openrig at commit 1f69831, republished under its Apache-2.0 licence (© mvschwarz). 796 words, ~1,682 tokens.

Download SKILL.mdSave it as .claude/skills/agent-starters/SKILL.md (or your agent's skills folder).
name
agent-starters
description
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.

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.
Show full SKILL.md (289 more words)Show less

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

© mvschwarz, 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

Files

Just SKILL.md in skills/_canonical/core/agent-starters of mvschwarz/openrig.

Open the folder on GitHubat commit 1f69831

Compare with similar skills

Agent Starters 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.

Agent Starters compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Starters this skillmvschwarz/openrig5.9k—~1.7kAutomated safety check: PassApache-2.0
Harness Engineering10xChengTu/harness-engineering1021 repos~1kAutomated safety check: PassNone
Munder Difflin Hive SyncHarnessMD/munder-difflin8.6k—~331Automated safety check: NotesMIT
Durable Session StateZaxbyHub/opencode-swarm490—~896Automated safety check: PassMIT
Orca CLIstablyai/orca87k2 repos~593Automated safety check: PassMIT
Coding Agent Session Findercode-yeongyu/oh-my-openagent70k1 repos~2.8kAutomated safety check: PassCustom licence

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Categories

Questions about Agent Starters

What does Agent Starters do?

Covers authoring, inspecting, refreshing, promoting and deprecating named Agent Starters, the reusable starting points for agent seats in a rig. An Agent Starter is a named, reusable starting point for one seat in a rig. It combines an agent role, startup context, an optional native session source and provenance, and can be chosen when creating, refreshing or packaging a seat.

When should I use Agent Starters?

Agent Starters fits situations like: creating a new seat from a known-good starting context; refreshing a seat with rig expand and a starter reference; inspecting a starter's provenance, freshness or recommended status; promoting or deprecating starters in the registry.

How do I install Agent Starters in Claude Code?

Run `npx skills add mvschwarz/openrig --skill agent-starters -a claude-code`. Or copy the skill folder (skills/_canonical/core/agent-starters in mvschwarz/openrig) into .claude/skills/agent-starters in your project. Claude Code loads it when a task matches its description.

How do I install Agent Starters in Codex?

Run `npx skills add mvschwarz/openrig --skill agent-starters -a codex`. Or copy the skill folder (skills/_canonical/core/agent-starters in mvschwarz/openrig) into .agents/skills/agent-starters in your project. Codex loads it when a task matches its description.

Can I use Agent Starters in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mvschwarz/openrig --skill agent-starters -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-starters, .gemini/skills/agent-starters, .github/skills/agent-starters and .opencode/skills/agent-starters in your project.

What does Agent Starters need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Starters is instructions for the agent only. Our summary lists: The rig CLI with its starter registry.

Does Agent Starters access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent Starters safe to install?

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.

What licence does Agent Starters use?

Agent Starters 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.

How many tokens does Agent Starters use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Starters?

Skills that share tags, products or a category with Agent Starters: Harness Engineering (10xChengTu/harness-engineering, 102 stars), Munder Difflin Hive Sync (HarnessMD/munder-difflin, 8.6k stars), Durable Session State (ZaxbyHub/opencode-swarm, 490 stars) and Orca CLI (stablyai/orca, 87k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Starters?

mvschwarz (a GitHub user) maintains it in mvschwarz/openrig, which has 5,854 GitHub stars. The repository holds 49 skills in this directory. The repository was last updated on October 8, 2026.

Source: mvschwarz/openrig on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.