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 nobodyohm-web/Thot --skill comps-analysisgit clone --depth 1 https://github.com/nobodyohm-web/ThotWrote 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/nobodyohm-web/thot/comps-analysis)<a href="https://agentmods.dev/skills/nobodyohm-web/thot/comps-analysis"><img src="https://agentmods.dev/badge/skills/nobodyohm-web/thot/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/nobodyohm-web/thot/comps-analysis"><img src="https://agentmods.dev/badge/skills/nobodyohm-web/thot/comps-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 | $0.00013 | $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.
This is a copy
100% identical to comps-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 nobodyohm-web/Thot (0 stars, last pushed 1mo ago), 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. It is 100% identical to comps-analysis, differing in 0 lines, and is treated as a copy.
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