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 yuping322/financial-services-plugins-new --skill dcf-modelgit 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/skills/yuping322/financial-services-plugins-new/dcf-model)<a href="https://agentmods.dev/skills/yuping322/financial-services-plugins-new/dcf-model"><img src="https://agentmods.dev/badge/skills/yuping322/financial-services-plugins-new/dcf-model/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/yuping322/financial-services-plugins-new/dcf-model"><img src="https://agentmods.dev/badge/skills/yuping322/financial-services-plugins-new/dcf-model.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 | $0.00085 | $0.11578 |
| Opus 5.5 | $0.00034 | $0.04631 |
| Sonnet 5.5 | $0.00017 | $0.02316 |
| Haiku 4.5 | $0.00009 | $0.01158 |
Grade A, and why
dcf-model 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
91% identical to dcf-model — 106 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 — 1,211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DCF Model Builder
Overview
This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).
Tools
- Default to using all of the information provided by the user and MCP servers available for data sourcing.
Critical Constraints - Read These First
These constraints apply throughout all DCF model building. Review before starting:
Sensitivity Tables:
- Populate ALL 75 cells (3 tables × 25 cells) with full DCF recalculation formulas
- Use openpyxl loops to write formulas programmatically
- NO placeholder text, NO linear approximations, NO manual steps required
- Each cell must recalculate full DCF for that assumption combination
Cell Comments:
- Add cell comments AS each hardcoded value is created
- Format: "Source: [System/Document], [Date], [Reference], [URL if applicable]"
- Every blue input must have a comment before moving to next section
- Do not defer to end or write "TODO: add source"
Model Layout Planning:
- Define ALL section row positions BEFORE writing any formulas
- Write ALL headers and labels first
- Write ALL section dividers and blank rows second
- THEN write formulas using the locked row positions
- Test formulas immediately after creation
Formula Recalculation:
- Run
python recalc.py model.xlsx 30before delivery - Fix ALL errors until status is "success"
- Zero formula errors required (#REF!, #DIV/0!, #VALUE!, etc.)
Scenario Blocks:
- Create separate blocks for Bear/Base/Bull cases
- Show assumptions horizontally across projection years within each block
- Use IF formulas:
=IF($B$6=1,[Bear cell],IF($B$6=2,[Base cell],[Bull cell])) - Verify formulas reference correct scenario block cells
DCF Process Workflow
Step 1: Data Retrieval and Validation
Fetch data from MCP servers, user provided data, and the web.
Data Sources Priority:
- MCP Servers (if configured) - Structured financial data from providers like Daloopa
- User-Provided Data - Historical financials from their research
- Web Search/Fetch - Current prices, beta, debt and cash when needed
What ships with it
3 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.
- 1mo ago First seen · 1,211 lines · 85 tokens per session scan A 77f28dcf6fe9
dcf-model is a skill published in the GitHub repository yuping322/financial-services-plugins-new (18 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 85 tokens to every session and 11,578 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to dcf-model, differing in 106 lines, and is treated as a copy.
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