Design and maintain applications where OpenRig agents help run the system. Organize instructions, shared state, and artifact checks, then trace unexpected behavior to the code, skill, or schema that needs repair.
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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.
安装前可以查看交给 AI 代理的指令内容。
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.
agent-operated-workflows.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.
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 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.
When a tool produces an artifact, do not make every consumer invent a completeness test:
.done sentinel last.Consumers fail closed on the shared proof. A filename or exit code is not content verification.
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.
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.
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Automate website interactions through a command-line browser tool. Navigate pages, fill forms, extract text, save screenshots or PDFs, reuse login sessions, and compare page states to check the effects of actions or website changes.
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Design and run bounded procedures in environments where current conditions are uncertain. The skill separates agent judgment from precise tool actions, verifies each effect, and records checkpoints so interrupted work can resume from observed evidence.
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Create reusable starting configurations that combine an agent’s role, startup context, and source history. The skill guides registry entries, checks whether their sources are current, and manages their progression from initial capture to recommendation or retirement.
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Guides the design and review of agent startup context, including role files and links to authoritative records. It helps identify stale guidance, missing updates, overloaded startup files, and instructions that lose the purpose of the work.
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Configure selected OpenRig command permissions in Claude Code or Codex at the agreed project or user scope. Preserve existing restrictions, verify that settings load and behave as intended, and record additions so they can be removed later.
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Turn rough meeting notes, customer requests, and product ideas into structured backlog entries, a list of possible improvements. Capture who needs each idea and why, categorize it, and update matching entries with new evidence.
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