Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill comps-analysisgit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/comps-analysis)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/comps-analysis"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/comps-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/nousresearch/hermes-agent/comps-analysis"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/comps-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00013 | $0.07304 |
| Opus 5.5 | $0.00005 | $0.02922 |
| Sonnet 5.5 | $0.00003 | $0.01461 |
| Haiku 4.5 | $0.00001 | $0.00730 |
Grade A, and why
comps-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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- comps-analysis — 100% identical, 0 lines differ
- comps-analysis — 100% identical, 0 lines differ
- comps-analysis — 100% identical, 0 lines differ
- comps-analysis — 92% identical, 2 lines differ
- comps-analysis — 92% identical, 2 lines differ
- comps-analysis — 92% identical, 2 lines differ
- comps-analysis — 78% identical, 63 lines differ
- comps-analysis — 78% identical, 63 lines differ
How it starts
The opening of the file, as written. The whole thing — 663 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Environment
This skill assumes headless openpyxl — you are producing an .xlsx file on disk.
Follow the excel-author skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx.
Comparable Company Analysis
⚠️ CRITICAL: Data Source Priority (READ FIRST)
ALWAYS follow this data source hierarchy:
- FIRST: Check for MCP data sources - If S&P Kensho MCP, FactSet MCP, or Daloopa MCP are available, use them exclusively for financial and trading information
- DO NOT use web search if the above MCP data sources are available
- ONLY if MCPs are unavailable: Then use Bloomberg Terminal, SEC EDGAR filings, or other institutional sources
- NEVER use web search as a primary data source - it lacks the accuracy, audit trails, and reliability required for institutional-grade analysis
Why this matters: MCP sources provide verified, institutional-grade data with proper citations. Web search results can be outdated, inaccurate, or unreliable for financial analysis.
Overview
This skill teaches the agent to build institutional-grade comparable company analyses that combine operating metrics, valuation multiples, and statistical benchmarking. The output is a structured Excel/spreadsheet that enables informed investment decisions through peer comparison.
Reference Material & Contextualization:
An example comparable company analysis is provided in examples/comps_example.xlsx. When using this or other example files in this skill directory, use them intelligently:
DO use examples for:
- Understanding structural hierarchy (how sections flow)
- Grasping the level of rigor expected (statistical depth, documentation standards)
- Learning principles (clear headers, transparent formulas, audit trails)
DO NOT use examples for:
- Exact reproduction of format or metrics
- Copying layout without considering context
- Applying the same visual style regardless of audience
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.
- 1mo ago First seen · 663 lines · 13 tokens per session scan A dcc2e6a91c58
comps-analysis is a skill published in the GitHub repository NousResearch/hermes-agent (254,120 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 7,304 once invoked, about $0.0001 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 2026-08-30.
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