oh-my-hermes is an operating layer for Hermes Agent that organizes requests into workflows for planning, research, creation, coding handoffs, operations, and project memory. Hermes users run these workflows through the desktop app, CLI, or messenger app, while the catalogue add-ons extend its native capabilities.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rlaope/oh-my-hermes --skill omh-data-pipelinesgit clone --depth 1 https://github.com/rlaope/oh-my-hermesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-data-pipelines)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-data-pipelines"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-data-pipelines/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-data-pipelines"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-data-pipelines.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.01948 |
| Opus 5.5 | $0.00038 | $0.00779 |
| Sonnet 5.5 | $0.00019 | $0.00390 |
| Haiku 4.5 | $0.00010 | $0.00195 |
Grade A, and why
omh-data-pipelines scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipelines
This is an OMH data-pipelines workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
data-pipelines exists because pipeline work had no owner: backend owns a service's schema migration, data-analysis analyzes data it is handed, and relational-db owns a database's locks and indexes, while a backfill that duplicated events or a schema change with unknown readers reached memory and event lanes with no idempotency contract or replay bound at all.
First Steps
- Ask what makes a row unique at the sink before planning any rerun.
- Bound the window and the targets before ordering any replay or backfill step.
Do Not Use When
- The ask is a service's own database migration, API, or queue design; use
omh-backend. - The ask is analyzing, charting, or summarizing a dataset that was handed over; use
omh-data-analysis. - The ask is a slow query, an index, or DDL locking a live table; use
omh-relational-db. - The ask is remembering or syncing what the assistant knows about the user.
Examples
Good example:
- Prompt: our airflow etl backfill is producing duplicate events
- Expected behavior: Find the sink's unique key, name the append that duplicated rows, write the idempotency contract (event-id dedupe or partition overwrite), then bound the backfill window and gate it on key uniqueness and row count against the prior window.
- Why: Rerunning an appending backfill doubles the duplicates it was meant to fix.
Bad example:
- Prompt: just delete the duplicates and rerun the whole history
- Expected behavior: Refuse the unbounded rerun: fix the write to be idempotent first, then backfill a bounded window behind a quality gate.
- Why: Deleting duplicates without fixing the write guarantees the next rerun duplicates again.
Completion Checklist
- The sink's unique key and the idempotency contract are stated.
- Every replay or backfill is bounded by window and target.
- Every downstream reader of a schema change is named with its impact.
- Every load names its data-quality gate and the value that stops it.
- OMH ran nothing, and every count cites observed output or is marked unverified.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed cf425772f6ad
- 3d ago Changed · +1 lines 977cae5e3e14
- 16d ago Changed · +3 lines a0d9bbee334a
- 18d ago First seen · 135 lines · 96 tokens per session scan A ca3b74f8ff73
omh-data-pipelines is a skill published in the GitHub repository rlaope/oh-my-hermes (3,392 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 1,948 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-29.
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