agent-operated-workflows — Claude Code skill: purpose and setup
This skill explains how an agent can run a bounded procedure when the real system may differ from the expected path. Use it to plan actions, verify results, and leave evidence for a later resume.
npx skills add mvschwarz/openrig --skill agent-operated-workflows -a claude-codeWhat agent-operated-workflows does
The skill helps decide whether an agent should own a procedure’s control loop or whether ordinary code is the better fit. Agents make sense when the expected path is clear but the system’s current state is variable or only partly knowable. A parser or fixed data conversion with a closed state space is simpler as regular code.
It assigns distinct responsibilities: a runbook defines policy and stopping conditions, tools perform narrow actions, the agent interprets observations and checks effects, and a human handles destructive ambiguity. The procedure reads state, takes one bounded action, verifies the outcome, then records a checkpoint. An interrupted mutation is indeterminate, so retrying without checking its result is unsafe.
The skill also covers tool design: read and plan modes should be explicit, while changes should be narrow and independently verifiable. Its OpenRig upgrade example pairs a runbook with helper scripts. Follow that runbook for the upgrade procedure; this skill provides the reusable operating contract.
What it helps with
- choose whether an agent or regular code should control a procedure
- verify an action against the state that could disprove its success
- record a checkpoint so another agent can resume the work
- handle an interrupted mutation without assuming it failed
Example prompts for the agent
- Decide whether an agent should own this procedure’s control loop or whether ordinary code is enough.
- Plan the procedure by inspecting state first, taking one bounded action at a time, and checking each effect.
- The procedure stopped during a mutation. Identify what is verified and which next actions are safe without a blind retry.
How to install agent-operated-workflows
1Skills CLI (Vercel)
npx skills add mvschwarz/openrig --skill agent-operated-workflows -a claude-code2Manually: put the files in place
~/.claude/skills/agent-operated-workflows/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/agent-operated-workflows/
1Skills CLI (Vercel)
npx skills add mvschwarz/openrig --skill agent-operated-workflows -a codex2Manually: put the files in place
.agents/skills/agent-operated-workflows/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add mvschwarz/openrig --skill agent-operated-workflows -a cursor2Manually: put the files in place
.cursor/skills/agent-operated-workflows/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install mvschwarz/openrig agent-operated-workflows2Manually: put the files in place
.github/skills/agent-operated-workflows/SKILL.md1Skills CLI (Vercel)
npx skills add mvschwarz/openrig --skill agent-operated-workflows -a gemini-cli2Platform CLI
gemini skills install https://github.com/mvschwarz/openrig --path packages/daemon/assets/plugins/openrig-core/skills/agent-operated-workflowsManually: put the files in place
.devin/skills/agent-operated-workflows/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add mvschwarz/openrig --skill agent-operated-workflows -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about agent-operated-workflows
How do I install agent-operated-workflows in Claude Code?
Run npx skills add mvschwarz/openrig --skill agent-operated-workflows -a claude-code. The skill file is at ~/.claude/skills/agent-operated-workflows/SKILL.md.
What does agent-operated-workflows do?
It sets out how an agent can manage a bounded procedure by inspecting state, choosing actions, and verifying outcomes. It also explains when regular code is a better owner for the control loop.
How is an Agent-Operated Workflow different from AI-enabled software?
In AI-enabled software, the application controls the loop and calls a model as one capability. In an Agent-Operated Workflow, an agent controls one procedure with defined start and stop conditions.
Source text
SKILL.md · Apache-2.0
---
name: agent-operated-workflows
description: >-
Use when a bounded real-world procedure has a deterministic happy path but brownfield, variable,
or partially knowable state; when an operation must resume from verified evidence; or when deciding
whether agent judgment or ordinary code should own a procedure's control loop.
metadata:
openrig:
stage: provisional
audience: any agent operating or designing a bounded procedure
sibling_skills:
- agent-operated-software
- openrig-user
- systematic-debugging
---
# Agent-Operated Workflows
An **Agent-Operated Workflow** is a bounded procedure whose control loop belongs to an agent. The
agent observes, interprets, sequences bounded actions, verifies their effects, adapts, and escalates
genuine ambiguity. Deterministic code remains the exact substrate; it does not pretend to understand
every state of a brownfield world.
## Name the architecture before using it
- **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.
- **Agent-Operated Software:** an OpenRig agent or rig participates in an ongoing application's live
backend or control loop. It may run many workflows; see `agent-operated-software`.
Calling a model is not enough. A deterministic program that consults a model still owns its loop.
## Decide whether the agent earns the loop
Use an agent when the happy path is deterministic but the environment is brownfield, variable,
partially knowable, or disproportionately expensive to model exhaustively. The judgment is in
discovering what is true and choosing the next safe act.
Prefer ordinary code when the state space is closed and code is simpler and more reliable than
judgment. A parser, fixed data transformation, or fully enumerated state machine does not become
better because an agent drives it.
## Keep ownership explicit
| Owner | Responsibility |
|---|---|
| Runbook | owns policy, the mental model, checkpoints, stop conditions, and escalation rules |
| Deterministic tools | own context gathering and bounded exact actions |
| Agent | owns interpretation, sequencing, adaptation, and effect verification |
| Human | owns destructive ambiguity and product policy |
The runbook guides judgment; tools enforce the invariants that must not be arguable.
## Operate
1. Establish identity, durable state, the runbook, and the promised outcome.
2. Inspect with side-effect-free reads or an explicit plan mode.
3. Choose one bounded action from observed state; do not mechanically replay the happy path.
4. Verify the effect at the consumer or state surface that could prove it wrong.
5. Record an observable checkpoint before moving on, so another agent can resume.
6. Continue, stop at the declared condition, or escalate with evidence and the decision needed.
An interrupted or timed-out mutation is **indeterminate**, never failed by assumption and never safe
to retry blind. Where a compatibility period is safer, separate reversible preparation from the
destructive finalizer. Idempotence is a claim to prove per action, not a label for the whole run.
## Build
Expose composable verbs, not an optimistic end-to-end controller. Help is inert. Read and plan modes
are explicit. Mutations are narrow and independently readable. A failure should say what was
observed, why the tool stopped, what remains unchanged, the safe next choices, and where its evidence
lives. Keep checkpoints durable, effects observable, and recovery possible without private developer
knowledge.
A capability composition is a reusable recipe; this is the execution architecture that operates
capabilities toward an outcome. Promote a proven workflow to the composition shelf only after use.
**Public example:** load `skills/core/openrig-upgrade/SKILL.md` with
`rig context get` for the upgrade runbook. Its adjacent `scripts/` directory
contains the bounded inspection, backup, plugin-refresh and migration helpers.
Follow that runbook for the procedure; this skill carries the reusable contract.
© mvschwarz · Apache-2.0 · mvschwarz/openrig