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-ai-job-searchgit 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-ai-job-search)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-ai-job-search"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-ai-job-search/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-ai-job-search"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-ai-job-search.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 | $0.00069 | $0.01380 |
| Opus 5.5 | $0.00028 | $0.00552 |
| Sonnet 5.5 | $0.00014 | $0.00276 |
| Haiku 4.5 | $0.00007 | $0.00138 |
Grade A, and why
omh-ai-job-search 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 4d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ai Job Search
This is an OMH ai-job-search workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
Cross-session career work needs stable posting identity, candidate-evidence links and a strict boundary between prepared applications and confirmed events.
First Steps
- Read
toolkit/README.mdandreferences/execution-and-qa.md; load only the relevant profile/discovery, fit/drafting or interview/outcomes reference for the current action. - Confirm an approved private workspace outside the product checkout through the active profile; ask only action-specific questions and keep group replies public-safe.
- Actually invoke
toolkit/scripts/job_state.pythrough the host terminal: validate captures, merge approved local tracking input, then summary and read-back; produce requested documents rather than stopping at a prepared card.
Do Not Use When
- For employer-side hiring and candidate assessment, use
omh-people-ops. - For background task scheduling or service repair, use the existing automation or debugging owner.
- For repository symbol lookup or implementation work, use the selected coding owner.
Examples
Good example:
- Prompt: Find library coordinator opportunities and tailor my supported experience to a selected posting.
- Expected behavior: Capture verified sources, preserve unknown constraints, draft evidence-mapped documents and review them privately.
- Why: Career evidence and application state need a coherent method.
Bad example:
- Prompt: Debug a failed cron job in the application server.
- Expected behavior: Use the debugging owner; do not collect career facts.
- Why: Generic task vocabulary does not establish a career request.
Completion Checklist
- Requested private artifacts were written and inspected; public delivery contains only safe summaries.
- Unknowns, source failures, draft versions and reviewer identity remain explicit.
- Actual helper exits and tracker read-back support every count and event claim, or the blocker is stated.
What ships with it
10 files 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.
- references/execution-and-qa.md 3.9 KB
- references/fit-and-drafting.md 2.4 KB
- references/interview-and-outcomes.md 2.0 KB
- references/procedure.md 3.2 KB
- references/profile-and-discovery.md 3.4 KB
- toolkit/examples/synthetic-captures.json 1.3 KB
- toolkit/LICENSE.upstream 1.0 KB
- toolkit/README.md 17 KB
- toolkit/scripts/job_state.py 33 KB runs code
- toolkit/upstream-manifest.json 2.4 KB
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.
- 4d ago First seen · 137 lines · 69 tokens per session scan A 639ddd029421
omh-ai-job-search is a skill published in the GitHub repository rlaope/oh-my-hermes (4,366 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 1,380 once invoked, about $0.0003 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 2026-10-24.
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notion-knowledge-capture
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Use when reading, searching, creating, or editing notes in an authorized Obsidian vault while preserving frontmatter, wikilinks, and unrelated notes.
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lark-contact
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