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 chuancyzhang/deepseek-cowork --skill dcf-modelgit clone --depth 1 https://github.com/chuancyzhang/deepseek-coworkWrote 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/chuancyzhang/deepseek-cowork/dcf-model)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/dcf-model"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/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/chuancyzhang/deepseek-cowork/dcf-model"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/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 | $0.12861 |
| Opus 5.5 | $0.00034 | $0.05144 |
| Sonnet 5.5 | $0.00017 | $0.02572 |
| Haiku 4.5 | $0.00009 | $0.01286 |
Grade A, and why
dcf-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to dcf-model — 61 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DCF Model Builder
Overview
This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).
Tools
- Default to using all of the information provided by the user and MCP servers available for data sourcing.
Critical Constraints - Read These First
These constraints apply throughout all DCF model building. Review before starting:
Environment: Office JS vs Python/openpyxl:
- If running inside Excel (Office Add-in / Office JS environment): Use Office JS directly — do NOT use Python/openpyxl. Write formulas via
range.formulas = [["=D19*(1+$B$8)"]]. No separate recalc step needed; Excel calculates natively. Userange.format.*for styling. The same formulas-over-hardcodes rule applies: set.formulas, never.valuesfor derived cells. - If generating a standalone .xlsx file (no live Excel session): Use Python/openpyxl as described below, then run
recalc.pybefore delivery. - The rest of this skill uses openpyxl examples — translate to Office JS API calls when in that environment, but all principles (formula strings, cell comments, section checkpoints, sensitivity table loops) apply identically.
⚠️ Office JS merged cell pitfall: When building section headers with merged cells, do NOT call .merge() then set .values on the merged range — Office JS still reports the range's original dimensions and will throw InvalidArgument: The number of rows or columns in the input array doesn't match the size or dimensions of the range. Instead, write the value to the top-left cell alone, then merge and format the full range:
// WRONG — throws InvalidArgument:
const hdr = ws.getRange("A7:H7");
hdr.merge();
hdr.values = [["MARKET DATA & KEY INPUTS"]]; // 1×1 array vs 1×8 range → fails
// CORRECT — value first on single cell, then merge + format the range:
ws.getRange("A7").values = [["MARKET DATA & KEY INPUTS"]];
const hdr = ws.getRange("A7:H7");
hdr.merge();
hdr.format.fill.color = "#1F4E79";
hdr.format.font.bold = true;
hdr.format.font.color = "#FFFFFF";
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 · 1,264 lines · 85 tokens per session scan A 2bb3ed672ab2
dcf-model is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 85 tokens to every session and 12,861 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to dcf-model, differing in 61 lines, and is treated as a copy.
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