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-geospatial-analysisgit 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-geospatial-analysis)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-geospatial-analysis"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-geospatial-analysis/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-geospatial-analysis"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-geospatial-analysis.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 | $0.00057 | $0.01523 |
| Opus 5.5 | $0.00023 | $0.00609 |
| Sonnet 5.5 | $0.00011 | $0.00305 |
| Haiku 4.5 | $0.00006 | $0.00152 |
Grade A, and why
omh-geospatial-analysis 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 2d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geospatial Analysis
This is an OMH geospatial-analysis workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
Polygon computations can return plausible but wrong numbers when CRS, coordinate units or geometry are invalid. This skill couples fail-closed checks to a runnable optional overlay producer and inspectable replay artifacts.
First Steps
- Read
toolkit/README.mdandreferences/execution-and-qa.mdbefore executingtoolkit/scripts/analyze.py; installation and skill discovery never run it. - Confirm the two layers, explicit EPSG declarations, study extent, projected metre CRS, output directory and finite host timeout; never guess a missing CRS.
- Use the original synthetic rectangle fixture only for backend QA; do not describe it as a public flood, facility or site-suitability finding.
Do Not Use When
- The task only aggregates supplied tables or charts without coordinate computation; use
omh-data-analysis. - The task only needs source discovery or an explanation from documentation; use
omh-web-research. - The task is connecting a generic tool server, not executing a domain calculation; use Hermes' native MCP configuration guidance.
Examples
Good example:
- Prompt: Intersect these two GeoJSON layers in a metric CRS and compute overlap area.
- Expected behavior: Require authoritative source CRS, validate geometry, execute real intersection, then inspect result GeoJSON, measured summary and replay manifest.
- Why: A spatial computation has measurable geometry and units rather than a report-only output.
Bad example:
- Prompt: Explain the map metaphor in this essay and plot the existing CSV totals.
- Expected behavior: Answer the writing question or use
data-analysisfor the table; do not run a spatial backend. - Why: Map vocabulary alone does not establish a coordinate computation.
Completion Checklist
- Source declarations and metric target are explicit; invalid geometry is rejected, not silently repaired.
- The actual backend command and exit were observed; output GeoJSON, summary and manifest were read back and hashed, or the blocker is stated.
- Pair sums versus union area, projected measurement limits and synthetic versus public-world evidence remain distinct.
What ships with it
9 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 4.3 KB
- references/full-contract.md 3.0 KB
- references/procedure.md 3.0 KB
- toolkit/examples/input.json 501 B
- toolkit/examples/overlay.json 351 B
- toolkit/LICENSE.upstream 1.3 KB
- toolkit/README.md 6.3 KB
- toolkit/scripts/analyze.py 17 KB runs code
- toolkit/upstream-manifest.json 3.1 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.
- 2d ago First seen · 138 lines · 57 tokens per session scan A 6d2d3fb39c8e
omh-geospatial-analysis is a skill published in the GitHub repository rlaope/oh-my-hermes (3,912 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,523 once invoked, about $0.0002 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 2026-10-23.
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