A discounted cash flow valuation model, which estimates a company's value from the cash it may generate in the future, adjusted for time and risk.

Use it to project free cash flow, calculate WACC and terminal value, estimate equity value per share, run sensitivity tests, and optionally compare peers.

It turns forecasts, discount rates, terminal value, and share counts into an estimate that can be compared with a market price.

dcf — Skill for Claude Code by agentii-ai

Install

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.

Any agent

npx skills add agentii-ai/agentii-investment-intelligence --skill dcf

Clone the repo

git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence

Made for: Claude Code.

Or install models-and-pitches, the plugin that ships this one along with the rest of its 9 skills, 9 commands.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/dcf/github.svg)](https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/dcf)

Your own site

<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/dcf"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/dcf"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/dcf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,018 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00051	$0.02018
Opus 5	$0.00026	$0.01009
Sonnet 5	$0.00010	$0.00404
Haiku 4.5	$0.00005	$0.00202

Measured 5d ago against content hash 45e6dc687ff6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 5d 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.

plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Preflight

Run the canonical pre-flight sequence — MCP health probe, ticker resolution, workspace style.md override, memory load, and coverage check. See contracts/preflight.md.

Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md.

Triggers

analyze dcf model
run dcf model analysis
produce dcf model report
dcf model breakdown
dcf model deep dive
build a dcf model
assess dcf model
quantify dcf model
compare dcf model across peers
review dcf model for
generate dcf model on
dcf model for investment decision

Defaults

Parameter	Default	Notes
lookback_years	3	Historical data window
include_peers	false	Whether to surface a peer comparison block

Methodology

Retrieval Scope

This skill performs unstructured document search at scale across SEC filings and earnings call transcripts (10-K, 10-Q, 8-K). The three-layer agent-use-ready retrieval protocol (Document Discovery → Page Map → Deep Read) applies to all unstructured document search at scale.

Retrieval Strategy

See contracts/retrieval.md for the canonical decision tree; skill-specific retrieval detail is in references/methodology.md.

Temporal Scope

Default: 12 fiscal quarters (max 20). Financial modeling: trailing 12 quarters (3 fiscal years) for long-range projection inputs.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

Step-by-step execution detail is in references/methodology.md.

Deliverable Chain

Inputs → Build → Validate → Output → Next

Inputs: resolved ticker + search_xbrl_facts (Income Statement, Balance Sheet, Cash Flow) + get_company_financials + get_realtime_quote for current price.
Build: write a self-contained Python script using openpyxl that creates the DCF workbook (projections, WACC, terminal value, sensitivity tables) per ## Output Structure. Execute via Bash: python3 script.py. Verify the .xlsx exists. If import openpyxl fails, fall back to .md summary with data_availability: degraded (see contracts/office-tooling.md).
Validate: run LibreOffice recalc; audit hardcoded_count == 0 for tagged cells per ## Validation Gates; verify projection horizon ≥ 5 years, terminal growth < risk-free proxy.
Output: write the artifact path per ## Output File. (Optional) render an executive-summary .pptx via Bash+python-pptx; convert .xlsx → PDF via LibreOffice.
Next: append to agentii.md; hand off to a downstream pitch/review skill if requested.

Read the full file on GitHub · 137 lines

Files

What ships with it

9 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.

Changes

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.

5d ago Changed 45e6dc687ff6
10d ago First seen · 137 lines · 51 tokens per session scan A bec882e24583

Subscribe to this mod's changes

dcf is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (203 stars, last pushed yesterday), licensed Apache-2.0. It adds 51 tokens to every session and 2,018 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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