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 OpenMinis/MinisSkills --skill reverse-dcfgit clone --depth 1 https://github.com/OpenMinis/MinisSkillsWrote 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/openminis/minisskills/reverse-dcf)<a href="https://agentmods.dev/skills/openminis/minisskills/reverse-dcf"><img src="https://agentmods.dev/badge/skills/openminis/minisskills/reverse-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/skills/openminis/minisskills/reverse-dcf"><img src="https://agentmods.dev/badge/skills/openminis/minisskills/reverse-dcf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00158 | $0.02650 |
| Opus 5.5 | $0.00063 | $0.01060 |
| Sonnet 5.5 | $0.00032 | $0.00530 |
| Haiku 4.5 | $0.00016 | $0.00265 |
Grade A, and why
reverse-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.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse DCF: Deriving Market-Implied Expectations
Core Idea
Forward DCF: given assumptions, calculate a target price. The parameters can be adjusted arbitrarily and end up "serving the conclusion."
Reverse DCF: given the market price, back-solve the implied growth rate and translate "expensive" or "cheap" into a verifiable proposition.
The essential difference:
- Forward DCF outputs "fair value." It is abstract, and you can always be right, but your wallet will not get any thicker.
- Reverse DCF outputs "market expectations": specific, verifiable numbers that can be compared with reality.
Applicability
Necessary condition: base-year FCFF must be positive.
Applying a standard two-stage DCF to companies with negative FCFF, such as early-stage high-growth companies or companies in an asset-heavy expansion phase, produces meaningless results.
Alternative: use PS-implied revenue back-solving (see "Handling Special Cases" below).
Data requirements:
- Latest complete annual report (income statement, balance sheet, and cash flow statement)
- Current stock price/market capitalization
- Details of interest-bearing debt (short-term borrowings, long-term borrowings, lease liabilities, etc.)
- Risk-free rate (10-year government bond yield)
- Industry β and ERP (Damodaran data recommended)
Practical Steps
Step 1: Determine Enterprise Value (EV)
EV = Market Capitalization + Net Debt
Net Debt = Interest-Bearing Debt - Cash and Cash Equivalents - Financial Assets Held for Trading
Notes:
- For companies like SK Hynix that are in a "net cash" position (cash > interest-bearing debt), EV will be lower than market capitalization.
- For A-share companies, check whether financial assets held for trading include wealth management products, which are treated as cash equivalents.
- STAR Market companies often have large amounts of IPO or financing proceeds sitting on the balance sheet, so EV may be far lower than market capitalization.
Step 2: Calculate Base-Year FCFF (the core step and the easiest place to make mistakes)
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 · 215 lines · 158 tokens per session scan A b22b7a8e6402
reverse-dcf is a skill published in the GitHub repository OpenMinis/MinisSkills (502 stars, last pushed 2d ago), licensed MIT. It adds 158 tokens to every session and 2,650 once invoked, about $0.0006 per session on Opus 5.5. 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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