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.
git clone --depth 1 https://github.com/yuping322/financial-services-plugins-newWrote 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/commands/yuping322/financial-services-plugins-new/dcf)<a href="https://agentmods.dev/commands/yuping322/financial-services-plugins-new/dcf"><img src="https://agentmods.dev/badge/commands/yuping322/financial-services-plugins-new/dcf/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/commands/yuping322/financial-services-plugins-new/dcf"><img src="https://agentmods.dev/badge/commands/yuping322/financial-services-plugins-new/dcf.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 | $0.00011 | $0.00874 |
| Opus 5.5 | $0.00004 | $0.00350 |
| Sonnet 5.5 | $0.00002 | $0.00175 |
| Haiku 4.5 | $0.00001 | $0.00087 |
Grade A, and why
dcf 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 dcf — 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DCF Valuation Command
Build an institutional-quality DCF model that uses comparable company analysis to inform valuation ranges.
Workflow
Step 1: Gather Company Information
If a company name or ticker is provided, use it. Otherwise ask:
- "What company would you like to value?"
Step 2: Run Comparable Company Analysis
First, load the comps-analysis skill to build trading comps:
Use skill: "comps-analysis" to:
- Identify 4-6 comparable public companies
- Pull operating metrics (Revenue, EBITDA, margins, growth)
- Pull valuation multiples (EV/Revenue, EV/EBITDA, P/E)
- Calculate statistical summary (median, 25th/75th percentiles)
Key outputs to capture from comps:
- Median EV/EBITDA multiple → informs terminal value exit multiple
- Median EV/Revenue multiple → sanity check on DCF output
- Peer growth rates → benchmark for revenue projections
- Peer margins → benchmark for margin assumptions
Step 3: Build DCF Model
Load the dcf-model skill to construct the valuation:
Use skill: "dcf-model" to:
- Gather historical financials and market data
- Build revenue projections (Bear/Base/Bull cases)
- Model operating expenses and FCF
- Calculate WACC using CAPM
- Discount cash flows and calculate terminal value
- Bridge to equity value and implied share price
Use comps to inform DCF assumptions:
| Comps Output | DCF Input |
|---|---|
| Peer median EV/EBITDA | Terminal exit multiple range |
| Peer 25th-75th EV/EBITDA | Sensitivity analysis range |
| Peer median growth rate | Benchmark for revenue assumptions |
| Peer median EBITDA margin | Target margin in terminal year |
| Peer median P/E | Cross-check implied P/E from DCF |
Step 4: Cross-Check Valuation
After DCF is complete, validate:
- Implied EV/EBITDA from DCF vs peer median
- If DCF implies 25x but peers trade at 12x, investigate why
- Implied P/E from DCF vs peer median
- Terminal value as % of EV (should be 50-70%)
- Implied growth embedded in valuation vs peer growth rates
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 · 103 lines · 11 tokens per session scan A ba4c3e0a2f6b
dcf is a command published in the GitHub repository yuping322/financial-services-plugins-new (18 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 11 tokens to every session and 874 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to dcf, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
valuation-methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction.
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audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.
scan
Scan AWS account for cost optimization.
audit
Scan for non-kernel money math. Rebuild critical flows as JournalEntrys. Replay and prove. Complements /ledger-verify.