hermes-workflows-creating
Design and author Hermes Workflows with typed inputs/outputs, agent/bash/ask/parallel/pipeline/goal primitives, Review Queue gates, artifacts, and…
Skill by hugobowne
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: design, data, productivity, agent-building
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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~/.claude/skills/hermes-workflows-creating
What's inside
- Skill name
hermes-workflows-creating- Description
- Design and author Hermes Workflows with typed inputs/outputs, agent/bash/ask/parallel/pipeline/goal primitives, Review Queue gates, artifacts, and verification receipts. Use when creating or modifying workflow definitions.
- License
- MIT
- Instructions
- 137 lines in SKILL.md · view on GitHub
Files (4)
- LICENSE
- README.md
- SKILL.md
- hero.png · images/
SKILL.md (first lines)
> **Project:** [skylarbpayne/hermes-workflows](https://github.com/skylarbpayne/hermes-workflows). "Code-first durable workflows for agent work, Review Queues, Workflow Workers, and receipts."
>
> **License:** [Apache License 2.0](https://github.com/skylarbpayne/hermes-workflows/blob/main/LICENSE). Full license text is in [`LICENSE`](LICENSE) alongside this file.
>
> **Snapshot:** Frozen copy of [`hermes-workflows-creating/SKILL.md`](https://github.com/skylarbpayne/hermes-workflows/blob/main/src/hermes_workflows/plugin_skills/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
```python
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:
```bash
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;From hugobowne/show-us-your-agent-skills, MIT.
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Signals
- Stars
- 70
- Forks
- 10
- Open issues
- 0
- Last commit (this folder)
- Jul 3, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
hermes-workflows-creating skill. Agent Mart. https://agentmart.dev/skills/hugobowne-hermes-workflows-creating (last updated 2026-10-24)