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 leecyno1/boutique-skills --skill company-valuationgit clone --depth 1 https://github.com/leecyno1/boutique-skillsWrote 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/leecyno1/boutique-skills/company-valuation)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/company-valuation"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/company-valuation/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/leecyno1/boutique-skills/company-valuation"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/company-valuation.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 | $0.00234 | $0.03956 |
| Opus 5 | $0.00117 | $0.01978 |
| Sonnet 5 | $0.00047 | $0.00791 |
| Haiku 4.5 | $0.00023 | $0.00396 |
Grade A, and why
company-valuation 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 8d 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
97% identical to company-valuation — 13 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 — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Valuation
Triangulates intrinsic value via three methods, then blends them to an implied share price:
- DCF — 5-year FCFF projection, discount at WACC, terminal value.
- Relative — apply peer median P/E, EV/Revenue, EV/EBITDA.
- SOTP — when 2+ distinct reporting segments exist, value each at pure-play peer multiples.
Always present a WACC × terminal-growth sensitivity table and Bull/Base/Bear scenarios.
Disclaimer: Research/educational output. Not financial advice.
Step 1: Detection Flow
Detect data source and runtime deps. The skill supports 3 method paths — pick the richest one available.
Environment status:
!`python3 -c "import yfinance, numpy, pandas; print('YFIN_OK')" 2>/dev/null || echo "YFIN_MISSING"`
!`(command -v funda && funda --version) 2>/dev/null || echo "FUNDA_CLI_MISSING"`
!`python3 -c "import yfinance as yf; t=yf.Ticker('^TNX'); p=t.fast_info.last_price; print(f'RF_10Y={p/100:.4f}')" 2>/dev/null || echo "RF_FETCH_FAIL"`
Decision tree:
| Condition | Method path |
|---|---|
YFIN_OK |
Path A (primary): yfinance for financials + peer multiples |
YFIN_MISSING but FUNDA_CLI_MISSING is not set |
Path B: delegate to finance-data-providers:funda-data skill for fundamentals |
| Both missing | Path C: pip-install yfinance, then Path A. python3 -m pip install -q yfinance numpy pandas |
RF_FETCH_FAIL |
Use default rf = 0.045 and note stale risk-free rate in output |
If RF_10Y= printed, use that value as rf in Step 4d instead of the hardcoded 4.5%.
Step 2: Choose Methods & Set Defaults
Method applicability
| Company type | DCF | Relative | SOTP | Fallback |
|---|---|---|---|---|
| Mature cash-flow (CPG, telecom, utilities) | ✅ primary | ✅ | ❌ | — |
| High-growth SaaS / software | ✅ with care | ✅ primary | ❌ | Use EV/Revenue + Rule of 40 |
| Multi-segment conglomerate | ✅ | ✅ | ✅ primary | See references/sotp.md |
| Banks / insurance | ❌ | ✅ (P/B, P/TBV) | ❌ | DDM or excess return; note in output |
| Pre-revenue | ❌ | EV/Revenue only | ❌ | Flag low confidence |
| REITs | ❌ | ✅ (P/FFO, P/AFFO) | ❌ | NAV-based |
| Cyclicals (energy, semis, industrials) | ✅ on mid-cycle | ✅ | sometimes | Normalize through-cycle |
What ships with it
5 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.
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.
- 8d ago First seen · 307 lines · 234 tokens per session scan A f11f5ce2d802
company-valuation is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 234 tokens to every session and 3,956 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to company-valuation, differing in 13 lines, and is treated as a copy.
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