Company ValuationFinance Skill
A six-file workflow for DCF, peer multiples, conditional SOTP and scenario analysis using yfinance or a funda-data fallback. The examples mix forward P/E with historical EPS and use different terminal methods in the main model and sensitivity grid. The SOTP reference also supplies performance expectations without supporting results. Review the findings and rebuild those calculations before relying on any implied price; installation and valuation outputs have not been tested.
- Assessment score
- 78/100
- Snapshot risk
- High
- Audit conclusion
- Needs review
How the assessment score works
Pineify weights evidence recorded from the captured files: Skill structure 15%, Instruction clarity 20%, Dependency transparency 20%, Permission transparency 20%, Safety guardrails 15%, Finance task fit 10%. Each signal receives full credit for Yes, one-quarter credit for unknown, and none for No.
The score is not an output accuracy test or user rating. Read the findings and financial assumptions even when the score is high. Risk labels summarize recorded permissions and findings; they do not measure investment risk or certify that installation is safe.
Discovery
What this Skill helps an agent do.
Use the tasks and topics below to judge whether the captured workflow fits your question. Read its findings, dependencies and data requirements before relying on an output.
Domains and assets
Topics
Trust
Evidence from the captured directory.
This content snapshot assessment does not certify the repository, current files, or installation environment. "Not verified" means the captured files did not support a Yes or No conclusion.
License
GitHub reports the repository license as MIT License.
Review state
- Automated review
- Automated review
- Install tested
- No
- Human reviewed
- Not verified
Snapshot
- Files reviewed
- 6
- Captured
- 2026-07-29 07:31 UTC
- Snapshot ID
- fsh-company-valuation-2026-07-29-001
Permissions
FilesystemRead
YesThe workflow reads four bundled reference files and inspects installed Python dependencies. Additional filings may be supplied locally.
FilesystemWrite
YesPath C runs pip install, which writes to the Python environment. The absence of a saved valuation report does not make this read-only.
NetworkAccess
Yesyfinance pulls financial data, analyst estimates, and market data from Yahoo Finance APIs. Live 10Y Treasury yield fetched via yfinance (^TNX).
ShellExecution
YesThe skill executes Python scripts in the shell for DCF, relative valuation, and SOTP calculations. Path C auto-installs dependencies via pip.
CodeExecution
YesThe skill executes Python code for financial modeling — DCF projections, WACC calculation, peer multiple analysis, sensitivity grids, and scenario analysis.
ExternalCredentials
Not verifiedThe primary yfinance path has no user API-key step; the optional external funda-data path has a separate access contract not established by this directory.
Dependencies
yfinance
python3 -m pip install yfinance numpy pandas (auto-installed if missing)
Python library for pulling Yahoo Finance financial statements, analyst estimates, and market data.
numpy
python3 -m pip install numpy (auto-installed if missing)
Numerical computing library used in DCF and relative valuation calculations.
pandas
python3 -m pip install pandas (auto-installed if missing)
Data manipulation library for handling income statements, cash flows, and balance sheets.
Python 3.8+
Python 3.8 or later
Python runtime required for all valuation calculations.
funda-data skill
Optional: finance-data-providers:funda-data skill as fallback data source
Alternative data source for fundamentals when yfinance is missing.
Data sources named by the Skill
Yahoo Finance (yfinance)
Python library providing access to Yahoo Finance financial data including income statements, cash flows, balance sheets, analyst estimates, and market data.
Snapshot assessment
78/100
Pineify Finance Skill snapshot assessment, version 1.0.0. Generated 2026-10-20 00:00 UTC.
High risk · Needs review
Skill structure
100/100Instruction clarity
100/100Dependency transparency
65/100Permission transparency
75/100Safety guardrails
45/100Finance task fit
80/100Audit findings
High
SOTP performance ranges have no supporting evidence
references/sotp.md asserts catalyst win rates of 55–65%, average winners of 20–40%, position sizes and loss limits without a dataset, sample period or transaction assumptions. These are unsupported upstream assertions, not measured results or recommended allocations. Its fictional DiverseTech arithmetic example remains illustrative. A calculated valuation discount does not establish a profitable trade.
High
Rebuild the valuation examples before use
SKILL.md applies peer forwardPE to EPS_TTM, takes the absolute value of cash-flow working-capital changes, and labels a fixed 15x exit multiple as a peer median. Its main DCF averages Gordon and exit terminal values while the sensitivity grid uses only Gordon, so the highlighted base need not reconcile. Missing cash/debt is replaced by zero, beta=0 by 1, and rev is undefined in the CAGR fragment. Preserve missing values, align periods and signs, and reconcile one terminal method before interpreting prices.
Medium
Dependency manifest is absent from the snapshot
No requirements.txt or pyproject.toml is included in the captured directory. Dependencies are documented in README prerequisites and auto-installed via Path C (pip install).
Medium
Default ranges are assumptions rather than market benchmarks
Undated ERP/beta/peer and discount rules need fresh sourced inputs. Forward and trailing multiples need matching earnings; EV/EBITDA and EV/revenue estimates need not agree within 15% when margins differ. SBC treatment requires consistent future compensation and dilution treatment; existing diluted shares alone do not account for all future awards. SOTP annual corporate costs require capitalization or discounting before subtracting from a value, and subsidiary ownership and debt must reconcile without double-counting minority interests.
Low
Auto-installs Python dependencies via pip if missing
Path C executes python3 -m pip install -q yfinance numpy pandas when yfinance is not detected. This modifies the user Python environment.
Low
Relies on yfinance — unofficial Yahoo Finance data
yfinance is an unofficial library that scrapes Yahoo Finance APIs. Data may be delayed, incomplete, or break without notice. The skill acknowledges this in its caveats and recommends cross-checking with primary filings.
Sources and relations
Context around this Skill.
Repository
himself65/finance-skills
topic
Equity valuation
topic
DCF modeling
topic
Relative valuation & peer comps
topic
WACC & cost of capital
agent
Claude Code
collection
Market analysis
guide
Reviewing finance skills before install
Registry
Read this record as data.
The public record includes the page-safe snapshot, evidence, assessment, taxonomy, and relations. Internal revision fields stay outside the public projection.
- Record ID
- github:himself65/finance-skills:plugins/market-analysis/skills/company-valuation
- Snapshot ID
- fsh-company-valuation-2026-07-29-001