Download packages/daemon/assets/plugins/openrig-core/skills/agent-operated-software/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does agent-operated-software live?
The publisher keeps it at https://github.com/mvschwarz/openrig.
Which agents can use agent-operated-software?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
>-
name: agent-operated-software
description: >-
Use when designing, building, operating, or diagnosing an ongoing application whose live backend
or control loop includes OpenRig agents, including applications with a Markdown, YAML, or JSON
agent control plane or a thin surface over specialist agent roles.
metadata:
openrig:
stage: provisional
audience: any agent building or operating an agent-backed application
sibling_skills:
- agent-operated-workflows
- forming-an-openrig-mental-model
- mission-slice-sop
- openrig-user
Agent-Operated Software
Agent-Operated Software is an ongoing application whose functioning runtime includes an OpenRig
agent or rig in its live backend or control loop. The application surface can be thin: structured
state and specialist roles do the work, while the UI makes that work legible and steerable.
Start with the shared taxonomy
AI-enabled software: application code owns the control loop and calls a model as one capability.
Agent-Operated Workflow: an agent owns one bounded procedure with a start and stop condition;
see agent-operated-workflows.
Agent-Operated Software: agents participate in an ongoing application's functioning runtime.
An application may compose many Agent-Operated Workflows. A bounded workflow does not by itself make
its surrounding product Agent-Operated Software, and a model call is neither category unless an agent
owns part of the control loop.
Agents are part of the backend
In this architecture, behavior is not located only in functions. It is distributed across agents,
skills, instructions, bootstrap files, schemas, Markdown, YAML, JSON, folders, and deterministic
tools. A surprising result may therefore be a code defect or a coherence gap in this control plane.
Trace the path that actually produced the behavior before choosing which layer to repair.
This is not permission to tolerate defects in OpenRig core or another rock-solid substrate. There,
reproduce the bug, fix the code, and hold the full gate. The faster coherence-first posture belongs
to recoverable agent-operated application layers whose state and behavior can be inspected and
repaired cheaply.
Markdown is the control plane
Markdown is maximally useful to an agent while remaining legible enough for a human to steer. Put
intent, work state, evidence, and decisions into stable addressed artifacts rather than private chat
or an opaque custom database. Keep schemas and conventions aligned with the running behavior: the
agent population acts on those files as executable context.
Use progressive disclosure. A skill's name and description are the hot trigger; its body is cold
procedure. Descriptions say when to load, not how to work. The body should route the reader to exact
sources instead of copying them into another doctrine fork.
Scripts can act as prompts. A good script returns the context or exact effect an agent needs; when it
cannot, its failure teaches what it observed, why it stopped, and safe next actions. Keep deterministic
mechanics in tools and judgment in agents.
Make artifacts self-certifying
When a tool produces an artifact, do not make every consumer invent a completeness test:
Write to a partial path, never the final path.
Verify the property that can actually be wrong, such as duration, streams, resolution, or schema.
Flush and atomically rename the partial artifact to its final path.
Write the manifest, then a .done sentinel last.
Consumers fail closed on the shared proof. A filename or exit code is not content verification.
Build with an agent SDLC
Select the rigor required by the outcome and the project's policy. Scope relationships,
attributed evidence-backed item judgment, and genuinely distinct higher judgment have separate
owners. One item judgment derives its upstream readiness; agents do not synchronize parent
checkboxes, queue tags, or status prose. Capture alone is not acceptance, and readiness does not
publish or advance a workflow. Use openrig-operating-model and rig proof --help for the
supported judgment, correction, and read path. Keep historical locks and evidence addressable.
The human supplies steering, taste, and decisions the project's authority reserves for them.
For studio applications, the running product is usually the best iteration surface. A separate mockup
earns its cost only when it resolves a real design uncertainty that the running app cannot expose as
cheaply. This removes a redundant artifact, not the visual review or proof contract.
Repair the narrowest authoritative layer
Find the generator or source that every affected agent actually consumes. Fix a code defect in code;
fix a missing trigger in the skill; fix a malformed contract in its schema. Then verify through the
consumer. Editing a rendered or installed copy creates a temporary fork, not a repair.
The payoff compounds: a correction to the shared application control plane changes the next agent's
starting point. That is how an ongoing agent-operated system improves without making one human its
permanent router.
How do I install agent-operated-software?
Download packages/daemon/assets/plugins/openrig-core/skills/agent-operated-software/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does agent-operated-software live?
The publisher keeps it at https://github.com/mvschwarz/openrig.
Which agents can use agent-operated-software?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.