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 witt3rd/oh-my-hermes --skill omh-ralphgit clone --depth 1 https://github.com/witt3rd/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/witt3rd/oh-my-hermes/omh-ralph)<a href="https://agentmods.dev/skills/witt3rd/oh-my-hermes/omh-ralph"><img src="https://agentmods.dev/badge/skills/witt3rd/oh-my-hermes/omh-ralph/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/witt3rd/oh-my-hermes/omh-ralph"><img src="https://agentmods.dev/badge/skills/witt3rd/oh-my-hermes/omh-ralph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 228 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 28 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00019 | $0.02658 |
| Opus 5 | $0.00010 | $0.01329 |
| Sonnet 5 | $0.00004 | $0.00532 |
| Haiku 4.5 | $0.00002 | $0.00266 |
Grade A, and why
omh-ralph 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 12d 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OMH Ralph — Verified Execution (v2)
Requires the OMH plugin. Install
plugins/omh/from this repo to~/.hermes/plugins/omh/.
When to Use
- You have a plan (from omh-ralplan or manual) and need verified execution
- The user says: "ralph", "don't stop", "until done", "must complete", "keep going"
- You need guaranteed verification — not just "looks done" but evidence-backed completion
- Multi-step implementation where each task must be independently verified
When NOT to Use
- No plan or spec exists (use omh-deep-interview and/or omh-ralplan first)
- Trivial single-file changes (just do them directly)
- The user explicitly wants to skip verification
Architecture: One Task Per Invocation
Each ralph invocation does ONE unit of work and exits. The caller re-invokes for the next task. This eliminates context window exhaustion and makes every invocation a clean checkpoint.
Invocation N: read state → pick task → execute → verify → update state → EXIT
Invocation N+1: read state → pick next task → execute → verify → update state → EXIT
...
Final invocation: all tasks pass → architect review → mark complete → EXIT
Procedure
Step 0: Resolve Instance and Acquire Lock
Ralph and autopilot mutate a shared .omh/plans/ plan. Two sessions
running ralph against the same plan would race on ralph-tasks state
and produce non-deterministic outcomes. Use per-instance state +
advisory lock to make concurrent plans safe.
- Resolve
instance_idfrom the plan path:- Default plan source:
.omh/plans/ralplan-*.mdor.omh/plans/ralph-plan.md. instance_id = basename(plan_path) without ".md"(engine slugifies).- If no plan exists yet (Step 2 will choose), use
instance_id="default".
- Default plan source:
- Acquire the lock before reading/writing state:
lock = omh_state(action="lock", mode="ralph", lock_key="{instance_id}", session_id="{HERMES_SESSION_ID or uuid}", holder_note="ralph executing {plan_path}")acquired=true: continue.acquired=false: reportheld_byto the user (pid + session_id + started_at). Offer: wait / cancel the holder (omh_state(action="cancel", mode="ralph", instance_id="{instance_id}"), then on next invocation the dead holder's lock will be released automatically by stale-pid detection) / pick a different plan.
- Pass
instance_idto every subsequentomh_statecall in this invocation — both theralphmode and theralph-tasksmode. - Release the lock at every exit point (success, blocked, cancel,
max-iterations, exception):
Wrap the body of the procedure so the unlock fires even on error.omh_state(action="unlock", mode="ralph", lock_key="{instance_id}", session_id="{HERMES_SESSION_ID or uuid}")
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.
- 12d ago First seen · 256 lines · 19 tokens per session scan A d0f2547c885c
omh-ralph is a skill published in the GitHub repository witt3rd/oh-my-hermes (321 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 2,658 once invoked, about $0.0001 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.
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