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-memory-newgit 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-memory-new)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-memory-new"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-memory-new/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-memory-new"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-memory-new.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.01826 |
| Opus 5 | $0.00041 | $0.00913 |
| Sonnet 5 | $0.00016 | $0.00365 |
| Haiku 4.5 | $0.00008 | $0.00183 |
Grade A, and why
omh-memory-new 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 11d ago.
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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory New
This is a Hermes-native memory-new workflow skill.
Why This Exists
memory-new exists so Hermes users can ask for this workflow in chat and receive a structured, evidence-bounded OMH operating surface instead of ad hoc narration.
Do Not Use When
- The request is already handled by a narrower explicit skill with stronger evidence.
- The user asks OMH to secretly run external platforms, connectors, schedulers, file exports, or runtime agents.
- The only safe answer is to ask for missing authority, credentials, target, or observed evidence first.
Examples
Good example:
- Prompt: memory-new remember this bounded product decision as one durable OMH candidate after asking source, scope, and target.
- Expected behavior: Produce
prepare_memory_newwith required context, wrapper actions, and not-evidence boundaries. - Why: The prompt names a real workflow surface that Hermes can orchestrate without hiding execution.
Bad example:
- Prompt: memory-new retain this raw token, transcript, or temporary progress as durable memory.
- Expected behavior: Report the missing observed evidence or authority instead of claiming the external step happened.
- Why: Prepared OMH guidance is not platform, runtime, connector, file, memory, or delivery evidence.
Completion Checklist
- Confirm the workflow target, evidence boundary, and stop condition are named.
- Report which outputs are prepared, observed, blocked, or missing.
- Name the smallest next verification or handoff instead of claiming completion from narration.
Recovery Notes
- If required context is missing, ask one blocking question or route back to the narrower workflow.
- If runtime or wrapper evidence is unavailable, keep the status as not_observed and expose the next observable action.
Workflow Lane
- Current lane: Retained knowledge (
memory-new,memory-sync,decision-recall,wiki) - memory, rejected alternatives, wiki notes, retrieval, and staleness. - If intent belongs to another lane, hand back to
oh-my-hermesor name the adjacent workflow. - Shared product, routing, compatibility, and evidence rules:
omh-routing/references/skill-common-rail.md.
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.
- 11d ago First seen · 151 lines · 82 tokens per session scan A 497c96610517
omh-memory-new is a skill published in the GitHub repository rlaope/oh-my-hermes (1,648 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 1,826 once invoked, about $0.0004 per session on Opus 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 2026-08-30.
Other skills, from other repositories
memory-types-team-stores
Use when configuring shared team memory stores and knowledge graphs.
memory-types
Use when managing persistent project memory, facts, and developer context.
fable-handoff
Compact session decisions, durable evidence, open blockers, and exact next actions into structured continuation state for cross-session resumption. Use when pausing a coding session, transferring context to another agent, summarizing long-running work, or creating durable continuation checkpoints — even if the user…
fable-memory
Manage persistent file-based memory, indexing cross-session user preferences, feedback, and architectural constraints in structured MEMORY.md stores. Use when recording user feedback, storing project conventions, recalling cross-session architectural constraints, or indexing durable project memory — even if the user…
experience-manager
A project knowledge system for recording, finding, reviewing, and improving lessons learned. It stores rules, working strategies, technical knowledge, and past history at different levels.
context-engineering
Diagnose missing or overloaded agent context and configure project instructions when setup or context quality is the task.