Agent skill

Financial Data Collector

by daymade in daymade/claude-code-skills

MITAuto-check passedBusiness, Finance & HR

Install Financial Data Collector

skills CLI

$ npx skills add daymade/claude-code-skills --skill financial-data-collector -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install daymade/claude-code-skills financial-data-collector --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/daymade-financial/financial-data-collector .claude/skills/financial-data-collector && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "financial-data-collector" agent skill from https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/financial-data-collector into .claude/skills/financial-data-collector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data-collector", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/financial-data-collector

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add daymade/claude-code-skills --skill financial-data-collector -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install daymade/claude-code-skills financial-data-collector --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/daymade-financial/financial-data-collector .agents/skills/financial-data-collector && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "financial-data-collector" agent skill from https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/financial-data-collector into .agents/skills/financial-data-collector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data-collector", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add daymade/claude-code-skills --skill financial-data-collector -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install daymade/claude-code-skills financial-data-collector --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/daymade-financial/financial-data-collector .cursor/skills/financial-data-collector && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "financial-data-collector" agent skill from https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/financial-data-collector into .cursor/skills/financial-data-collector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data-collector", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/daymade/claude-code-skills.git --path daymade-financial/financial-data-collector

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add daymade/claude-code-skills --skill financial-data-collector -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install daymade/claude-code-skills financial-data-collector --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/daymade-financial/financial-data-collector .gemini/skills/financial-data-collector && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "financial-data-collector" agent skill from https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/financial-data-collector into .gemini/skills/financial-data-collector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data-collector", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install daymade/claude-code-skills financial-data-collector

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add daymade/claude-code-skills --skill financial-data-collector -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/daymade-financial/financial-data-collector .github/skills/financial-data-collector && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "financial-data-collector" agent skill from https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/financial-data-collector into .github/skills/financial-data-collector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data-collector", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add daymade/claude-code-skills --skill financial-data-collector -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install daymade/claude-code-skills financial-data-collector --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/daymade-financial/financial-data-collector .opencode/skills/financial-data-collector && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "financial-data-collector" agent skill from https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/financial-data-collector into .opencode/skills/financial-data-collector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data-collector", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: financial-data-collector
GitHub stars: 1.4k
Token cost: ~1.4k tokens
SKILL.md length: 309 words
Files: 6 (incl. scripts, references)
Skills in repo: 104
Repo updated: Oct 10, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Works in 3 steps: Collect Data → Validate Data → Deliver JSON
Pull financial/market data
SKILL.md covers Critical Constraints, Workflow, Output Schema (Summary) and Known yfinance Pitfalls
Runs Python scripts from its folder; calls python

What it does

Financial Data Collector is an agent skill from daymade/claude-code-skills. Collects real financial data for any US publicly traded company from free public sources (yfinance) and outputs structured JSON for downstream skills (DCF modeling, comps analysis, earnings review): market data, historical financials, WACC inputs, analyst estimates — never fabricated fallback values. Use to collect or pull financial/market data, or gather DCF inputs, for a ticker.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/output-schema.md`, `references/yfinance-pitfalls.md` and `scripts/collect_data.py`).

It sits in Business, Finance & HR, covering Stock and market analysis and Financial modeling. It works with yfinance. The repository describes itself as: Professional Claude Code skills marketplace featuring production-ready skills for enhanced development workflows. The licence is MIT.

When your agent uses it

Pull financial/market data
Gather DCF inputs

Example prompts

“Use the financial-data-collector skill to collect real financial data for any US publicly traded company from free public sources (yfinance) and…”
“/financial-data-collector”

Requirements

Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Collect Data
Validate Data
Deliver JSON

What it can do on your machine

Read from SKILL.md and the folder at commit 0e52df5. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Financial Data Collector loads about 1.4k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 309 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~102
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from daymade/claude-code-skills at commit 0e52df5, republished under its MIT licence (© daymade). 309 words, ~1,379 tokens.

Download SKILL.mdSave it as .claude/skills/financial-data-collector/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: financial-data-collector
description: Collects real financial data for any US publicly traded company from free public sources (yfinance) and outputs structured JSON for downstream skills (DCF modeling, comps analysis, earnings review): market data, historical financials, WACC inputs, analyst estimates — never fabricated fallback values. Use to collect or pull financial/market data, or gather DCF inputs, for a ticker.
disable-model-invocation: true

Financial Data Collector

Collect and validate real financial data for US public companies using free data sources. Output is a standardized JSON file ready for consumption by other financial skills.

Critical Constraints

NO FALLBACK values. If a field cannot be retrieved, set it to null with _source: "missing". Never substitute defaults (e.g., beta or 1.0). The downstream skill decides how to handle missing data.

Data source attribution is mandatory. Every data section must have a _source field.

CapEx sign convention: yfinance returns CapEx as negative (cash outflow). Preserve the original sign. Document the convention in output metadata. Do NOT flip signs.

yfinance FCF ≠ Investment bank FCF. yfinance FCF = Operating CF + CapEx (no SBC deduction). Flag this in output metadata so downstream DCF skills don't overstate FCF.

Workflow

Step 1: Collect Data

Run the collection script:

bash

python scripts/collect_data.py TICKER [--years 5] [--output path/to/output.json]

The script collects in this priority:

yfinance — market data, historical financials, beta, analyst estimates
yfinance ^TNX — 10Y Treasury yield as risk-free rate proxy
User supplement — for years where yfinance returns NaN (report to user, do not guess)

Step 2: Validate Data

bash

python scripts/validate_data.py path/to/output.json

Checks: field completeness, cross-field consistency (Market Cap = Price × Shares), range sanity (WACC 5-20%, beta 0.3-3.0), sign conventions.

Step 3: Deliver JSON

Single file: {TICKER}_financial_data.json. Schema in references/output-schema.md.

Do NOT create: README, CSV, summary reports, or any auxiliary files.

Output Schema (Summary)

json

{
  "ticker": "META",
  "company_name": "Meta Platforms, Inc.",
  "data_date": "2026-03-02",
  "currency": "USD",
  "unit": "millions_usd",
  "data_sources": { "market_data": "...", "2022_to_2024": "..." },
  "market_data": { "current_price": 648.18, "shares_outstanding_millions": 2187, "market_cap_millions": 1639607, "beta_5y_monthly": 1.284 },
  "income_statement": { "2024": { "revenue": 164501, "ebit": 69380, "tax_expense": ..., "net_income": ..., "_source": "yfinance" } },
  "cash_flow": { "2024": { "operating_cash_flow": ..., "capex": -37256, "depreciation_amortization": 15498, "free_cash_flow": ..., "change_in_nwc": ..., "_source": "yfinance" } },
  "balance_sheet": { "2024": { "total_debt": 30768, "cash_and_equivalents": 77815, "net_debt": -47047, "current_assets": ..., "current_liabilities": ..., "_source": "yfinance" } },
  "wacc_inputs": { "risk_free_rate": 0.0396, "beta": 1.284, "credit_rating": null, "_source": "yfinance + ^TNX" },
  "analyst_estimates": { "revenue_next_fy": 251113, "revenue_fy_after": 295558, "eps_next_fy": 29.59, "_source": "yfinance" },
  "metadata": { "_capex_convention": "negative = cash outflow", "_fcf_note": "yfinance FCF = OperatingCF + CapEx. Does NOT deduct SBC." }
}

Full schema with all field definitions: references/output-schema.md

<correct_patterns>

Handling Missing Years

python

if pd.isna(revenue):
    result[year] = {"revenue": None, "_source": "yfinance returned NaN — supplement from 10-K"}
# Report missing years to the user. Do NOT skip or fill with estimates.

CapEx Sign Preservation

python

capex = cash_flow.loc["Capital Expenditure", year_col]  # -37256.0
result["capex"] = float(capex)  # Preserve negative

Datetime Column Indexing

python

year_col = [c for c in financials.columns if c.year == target_year][0]
revenue = financials.loc["Total Revenue", year_col]

Field Name Guards

python

if "Total Revenue" in financials.index:
    revenue = financials.loc["Total Revenue", year_col]
elif "Revenue" in financials.index:
    revenue = financials.loc["Revenue", year_col]
else:
    revenue = None

</correct_patterns>

<common_mistakes>

Mistake 1: Default Values for Missing Data

python

# ❌ WRONG
beta = info.get("beta", 1.0)
growth = data.get("growth") or 0.02

# ✅ RIGHT
beta = info.get("beta")  # May be None — that's OK

Mistake 2: Assuming All Years Have Data

python

# ❌ WRONG — 2020-2021 may be NaN
revenue = float(financials.loc["Total Revenue", year_col])

# ✅ RIGHT
value = financials.loc["Total Revenue", year_col]
revenue = float(value) if pd.notna(value) else None

Mistake 3: Using yfinance FCF in DCF Models Directly

yfinance FCF does NOT deduct SBC. For mega-caps like META, SBC can be $20-30B/yr, making yfinance FCF ~30% higher than investment-bank FCF. Always flag this in output.

Mistake 4: Flipping CapEx Sign

python

# ❌ WRONG — double-negation risk downstream
capex = abs(cash_flow.loc["Capital Expenditure", year_col])

# ✅ RIGHT — preserve original, document convention
capex = float(cash_flow.loc["Capital Expenditure", year_col])  # -37256.0

</common_mistakes>

Known yfinance Pitfalls

See references/yfinance-pitfalls.md for detailed field mapping and workarounds.

Files

SKILL.md and 5 other files (scripts, references) in daymade-financial/financial-data-collector of daymade/claude-code-skills.

SKILL.md
.gitignore
references/output-schema.md
references/yfinance-pitfalls.md
scripts/collect_data.py
scripts/validate_data.py

Open the folder on GitHubat commit 0e52df5

Compare with similar skills

Financial Data Collector next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Financial Data Collector compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Financial Data Collector this skilldaymade/claude-code-skills	1.4k	—	~1.4k	Automated safety check: Pass	MIT	today
Stock Value AnalyzerFunnyKun/stock-value-analyzer	141	—	~3.3k	Automated safety check: Pass	None	3 mo ago
Stock Analysis24mlight/StockClaw	101	2 repos	~2k	Automated safety check: Notes	MIT	6 mo ago
Equity ResearchrollingSirius/equity-research-skill	453	—	~1.5k	Automated safety check: Pass	MIT	1 mo ago
Regimejackson-video-resources/markov-hedge-fund-method	484	—	~1.6k	Automated safety check: Pass	Custom licence	4 mo ago
Yahoo Finance24mlight/StockClaw	101	2 repos	~1.1k	Automated safety check: Notes	MIT	6 mo ago

Similar skills

Stock Value Analyzer
FunnyKun/stock-value-analyzer
基于邱国鹭《投资中最简单的事》方法论的股票价值分析器（v2.0 双层架构）。通过"三好原则"（好行业、好公司、好价格）系统评估一只股票是否值得投资。v2.0 在原定性框架之上注入一套可量化、可复现的硬模型层——反向 DCF 反解市场隐含增速、情景概率加权估值、EPV 盈利能力价值、分行业估值路由、杜邦三/五因子分解、ROIC vs WACC、Piotroski F-Score、Beneish…
141 GitHub stars~3.3k tokensUpdated 3 mo ago
Business, Finance & HRAuto-check passed
Stock Analysis
24mlight/StockClaw
Analyze stocks and cryptocurrencies using Yahoo Finance data.
101 GitHub starsUsed in 2 repos~2k tokens
Business, Finance & HRAuto-check: notes
Equity Research
rollingSirius/equity-research-skill
撰写机构级个股投资研究报告（二级市场深度研究）。Use whenever the user wants to research, analyze, or value a specific publicly-traded stock — e.g.
453 GitHub stars~1.5k tokensUpdated 1 mo ago
Business, Finance & HRAuto-check passed
Regime
jackson-video-resources/markov-hedge-fund-method
Detect the market regime (Bull / Bear / Sideways) for ANY asset and turn it into a tradeable signal or a risk filter.
484 GitHub stars~1.6k tokensUpdated 4 mo ago
Business, Finance & HRAuto-check passed
Yahoo Finance
24mlight/StockClaw
Get stock prices, quotes, fundamentals, earnings, options, dividends, and analyst ratings using Yahoo Finance.
101 GitHub starsUsed in 2 repos~1.1k tokens
Business, Finance & HRAuto-check: notes
Stock Analysis
liusai0820/Stock-Analysis-Skill
股票智能分析技能。输入股票代码（A股/港股/美股），自动完成： 1. An agent skill from liusai0820/Stock-Analysis-Skill.
141 GitHub stars~904 tokensUpdated 7 mo ago
Business, Finance & HRAuto-check: notes

Questions about Financial Data Collector

What does Financial Data Collector do?

Collects real financial data for any US publicly traded company from free public sources (yfinance) and outputs structured JSON for downstream skills (DCF modeling, comps analysis, earnings review)…. Financial Data Collector is an agent skill from daymade/claude-code-skills. Collects real financial data for any US publicly traded company from free public sources (yfinance) and outputs structured JSON for downstream skills (DCF modeling, comps analysis, earnings review): market data, historical financials, WACC inputs, analyst estimates — never fabricated fallback values.

When should I use Financial Data Collector?

Financial Data Collector fits situations like: pull financial/market data; gather DCF inputs.

How do I install Financial Data Collector in Claude Code?

Run `npx skills add daymade/claude-code-skills --skill financial-data-collector -a claude-code`. Or copy the skill folder (daymade-financial/financial-data-collector in daymade/claude-code-skills) into .claude/skills/financial-data-collector in your project. Claude Code loads it when a task matches its description.

How do I install Financial Data Collector in Codex?

Run `npx skills add daymade/claude-code-skills --skill financial-data-collector -a codex`. Or copy the skill folder (daymade-financial/financial-data-collector in daymade/claude-code-skills) into .agents/skills/financial-data-collector in your project. Codex loads it when a task matches its description.

Can I use Financial Data Collector in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add daymade/claude-code-skills --skill financial-data-collector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/financial-data-collector, .gemini/skills/financial-data-collector, .github/skills/financial-data-collector and .opencode/skills/financial-data-collector in your project.

What does Financial Data Collector need to run?

Going by SKILL.md and its folder, Financial Data Collector needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Financial Data Collector access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Financial Data Collector safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Financial Data Collector use?

Financial Data Collector is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Financial Data Collector use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Financial Data Collector?

Skills that share tags, products or a category with Financial Data Collector: Stock Value Analyzer (FunnyKun/stock-value-analyzer, 141 stars), Stock Analysis (24mlight/StockClaw, 101 stars), Equity Research (rollingSirius/equity-research-skill, 453 stars) and Regime (jackson-video-resources/markov-hedge-fund-method, 484 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Financial Data Collector?

daymade (a GitHub user) maintains it in daymade/claude-code-skills, which has 1,448 GitHub stars. The repository holds 104 skills in this directory. The repository was last updated on October 10, 2026.

Source: daymade/claude-code-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Financial Data Collector

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Financial Data Collector

Critical Constraints

Workflow

Step 1: Collect Data

Step 2: Validate Data

Step 3: Deliver JSON

Output Schema (Summary)

Handling Missing Years

CapEx Sign Preservation

Datetime Column Indexing

Field Name Guards

Mistake 1: Default Values for Missing Data

Mistake 2: Assuming All Years Have Data

Mistake 3: Using yfinance FCF in DCF Models Directly

Mistake 4: Flipping CapEx Sign

Known yfinance Pitfalls

Files

Compare with similar skills

Similar skills

More from daymade/claude-code-skills

Related

Questions about Financial Data Collector