Hermes Workflows Creating

Project: skylarbpayne/hermes-workflows. "Code-first durable workflows for agent work, Review Queues, Workflow Workers, and receipts."

License: Apache License 2.0. Full license text is in LICENSE alongside this file.

Snapshot: Frozen copy of hermes-workflows-creating/SKILL.md as of 2026-07-03. The maintained version lives upstream and may have evolved since this snapshot.

Hermes Workflows — creating workflows

Use this when designing or implementing a workflow definition. This skill is generic authoring guidance, not a place for project-specific workflow shapes.

Product model

Hermes Workflows are ordinary Python orchestration code with durable execution state.

Use the public authoring primitives:

• @workflow for the durable workflow entrypoint.
• agent(...) for judgment-heavy or generative work.
• bash(...) for deterministic local commands and receipts.
• ask(...) for typed review input and external-action approval gates.
• parallel(...) for fan-out/fan-in.
• pipeline(...) for staged transformations over items.
• goal(...) for loop-until-done semantics when available/appropriate.

Do not expose runtime plumbing (ctx.handoff, raw signals, internal waits, leases, outbox commands) in normal workflow authoring unless debugging the runtime itself.

Authoring checklist

1. Define the purpose and side-effect boundary.
2. Define typed dataclass inputs and outputs.
3. Split steps by ownership:
   • agent judgment/synthesis/editing → agent(...)
   • deterministic command/check/evidence → bash(...)
   • human decision or external side-effect authorization → ask(...)
4. Make artifacts reviewable inline in the Review Queue; paths alone are not enough.
5. Add explicit gates before external effects: send, publish, schedule, commit/push/PR/merge, deploy, payment, credentials, destructive data changes.
6. Record receipts: commands run, stdout/stderr/exit code, artifact paths, external handles, side-effect ledger.
7. Add smoke tests that run without provider credentials by using mock_output where appropriate.

Minimal skeleton

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Literal

from hermes_workflows import agent, ask, bash, parallel, workflow


@dataclass
class MyWorkflowInput:
    topic: str


@dataclass
class DraftPacket:
    title: str
    body: str
    risks: list[str] = field(default_factory=list)


@dataclass
class ReviewDecision:
    action: Literal["approve", "request_changes", "drop"]
    feedback: str | None = None


@workflow
async def my_workflow(inputs: MyWorkflowInput) -> dict:
    req = inputs if isinstance(inputs, MyWorkflowInput) else MyWorkflowInput(**dict(inputs or {}))

    preflight = await bash(
        "python -V && git status --short",
        key="preflight",
        timeout_seconds=60,
    )

    draft = await agent(
        "draft_packet",
        prompt="Create a concise reviewable draft from the input.",
        input={"request": req, "preflight": preflight.stdout[-4000:]},
        returns=DraftPacket,
        mock_output={"title": req.topic, "body": "Demo draft", "risks": []},
    )

    decision = await ask(
        "Review this draft before any external side effect.",
        key="review_draft_packet",
        input={"draft": draft, "side_effects": {"published": False}},
        returns=ReviewDecision,
    )

    return {"status": decision.action, "draft": draft, "decision": decision}

Step design rules

• Prefer small named steps with stable keys; keys should describe the public review/action, not internal plumbing.
• Keep workflow bodies intention-level. Hide retry loops, per-item keys, and feedback routing in helper functions when they get noisy.
• Rejections must roundtrip into workflow logic. If a human says request_changes, feed the feedback into a targeted regeneration step.
• For fan-out review, use ask(...) inside parallel(...) only when each review is independent and can emit its own Review Queue request.
• For code workflows, distinguish plan approval, implementation/review approval, and landing approval. One approval does not authorize all later side effects.
• For generated files, return paths plus summaries/checks/hashes where useful.

Testing pattern

At minimum:

PYTHONPATH=src:. python -m compileall -q path/to/workflow.py
PYTHONPATH=src:. python -m pytest -q tests/test_your_workflow.py

Test:

• first wait/review key appears;
• typed dataclass outputs rehydrate correctly;
• approval/request-changes paths behave as expected;
• side-effect flags remain false before explicit final gates;
• mock-output path runs without provider credentials;
• deterministic receipts are preserved in status/artifacts.

Placement guidance

• Runtime/package examples can live in the Hermes Workflows repo.
• Normal user/team workflows should live in the owning workspace or a separate private workflows repo and be exposed through that workspace’s workflow registry/catalog.
• Do not bake private business/blog/event workflows into the runtime package unless explicitly making a public example.

Do not put here

• One-off content/event/email/coding workflow designs for a specific user or demo.
• Personal infrastructure preferences.
• Repo-specific launch history.
• Runtime implementation details better suited to package-development docs.