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 johnson7788/MultiUserClaw --skill dcf-modelgit clone --depth 1 https://github.com/johnson7788/MultiUserClawWrote 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/johnson7788/multiuserclaw/dcf-model)<a href="https://agentmods.dev/skills/johnson7788/multiuserclaw/dcf-model"><img src="https://agentmods.dev/badge/skills/johnson7788/multiuserclaw/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/johnson7788/multiuserclaw/dcf-model"><img src="https://agentmods.dev/badge/skills/johnson7788/multiuserclaw/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 | $0.00053 | $0.12829 |
| Opus 5.5 | $0.00021 | $0.05132 |
| Sonnet 5.5 | $0.00011 | $0.02566 |
| Haiku 4.5 | $0.00005 | $0.01283 |
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
100% identical to dcf-model — 2 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 — 1,271 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.
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:
Formulas Over Hardcodes (NON-NEGOTIABLE):
- Every projection, margin, discount factor, PV, and sensitivity cell MUST be a live Excel formula — never a value computed in Python and written as a number
- When using openpyxl:
ws["D20"] = "=D19*(1+$B$8)"is correct;ws["D20"] = calculated_revenueis WRONG - The only hardcoded numbers permitted are: (1) raw historical inputs, (2) assumption drivers (growth rates, WACC inputs, terminal g), (3) current market data (share price, debt balance)
- If you catch yourself computing something in Python and writing the result — STOP. The model must flex when the user changes an assumption.
Verify Step-by-Step With the User (DO NOT build end-to-end):
- After data retrieval → show the user the raw inputs block (revenue, margins, shares, net debt) and confirm before projecting
- After revenue projections → show the projected top line and growth rates, confirm before building margin build
- After FCF build → show the full FCF schedule, confirm logic before computing WACC
- After WACC → show the calculation and inputs, confirm before discounting
- After terminal value + PV → show the equity bridge (EV → equity value → per share), confirm before sensitivity tables
- Catch errors at each stage — a wrong margin assumption discovered after sensitivity tables are built means rebuilding everything downstream
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 · 1,271 lines · 53 tokens per session scan A a6acd237bc40
dcf-model is a skill published in the GitHub repository johnson7788/MultiUserClaw (326 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 12,829 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to dcf-model, differing in 2 lines, and is treated as a copy.
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