Openbb Data Fetcher
monarchjuno/vibe-investing
Fetch financial, market, economic, fundamental, news, options, crypto, ETF, index, and macro data through the OpenBB Python interface instead of the OpenBB MCP server.
Validate data quality in market analysis documents and blog articles before publication.
Install Data Quality Checker
$ npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install tradermonty/claude-trading-skills data-quality-checker --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-quality-checker .claude/skills/data-quality-checker && rm -rf skills-srcUse ~/.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/
Install the "data-quality-checker" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/data-quality-checker into .claude/skills/data-quality-checker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checker", 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 install https://github.com/tradermonty/claude-trading-skills/tree/main/skills/data-quality-checkerType 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.
$ npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install tradermonty/claude-trading-skills data-quality-checker --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-quality-checker .agents/skills/data-quality-checker && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-quality-checker" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/data-quality-checker into .agents/skills/data-quality-checker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checker", 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.
$ npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install tradermonty/claude-trading-skills data-quality-checker --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-quality-checker .cursor/skills/data-quality-checker && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "data-quality-checker" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/data-quality-checker into .cursor/skills/data-quality-checker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checker", 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 https://github.com/tradermonty/claude-trading-skills.git --path skills/data-quality-checker--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install tradermonty/claude-trading-skills data-quality-checker --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-quality-checker .gemini/skills/data-quality-checker && rm -rf skills-srcUse ~/.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/
Install the "data-quality-checker" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/data-quality-checker into .gemini/skills/data-quality-checker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checker", 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.
$ gh skill install tradermonty/claude-trading-skills data-quality-checkerInstalls 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).
$ npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-quality-checker .github/skills/data-quality-checker && rm -rf skills-srcUse ~/.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/
Install the "data-quality-checker" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/data-quality-checker into .github/skills/data-quality-checker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checker", 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.
$ npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install tradermonty/claude-trading-skills data-quality-checker --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-quality-checker .opencode/skills/data-quality-checker && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "data-quality-checker" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/data-quality-checker into .opencode/skills/data-quality-checker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checker", 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
data-quality-checker- GitHub stars
- 3k
- Used in
- 1 other repo
- Token cost
- ~1.5k tokens
- SKILL.md length
- 555 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 74
- Repo updated
- First seen
- Licence
- MIT
At a glance
Validate data quality in market analysis documents and blog articles before publication.
- Works in 5 steps: Receive Input Document → Execute Validation Script → Load Reference Standards → …
- Checking for price scale inconsistencies (ETF vs futures)
- SKILL.md covers Overview, When to Use, Prerequisites and Workflow, plus 3 more sections
- Runs Python scripts from its folder; calls python3
What it does
Data Quality Checker is an agent skill from tradermonty/claude-trading-skills. Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/common_data_errors.md`, `references/instrument_notation_standard.md` and `scripts/check_data_quality.py`).
It sits in Data & Analytics, covering Data cleaning, Market research and Stock and market analysis. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.
When your agent uses it
- Checking for price scale inconsistencies (ETF vs futures)
- Instrument notation errors
- Date/day-of-week mismatches
- Allocation total errors
Example prompts
- “/data-quality-checker”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit eab8d5c. 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 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
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
Data Quality Checker loads about 1.5k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 555 words of instructions outside code blocks.
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 tradermonty/claude-trading-skills at commit eab8d5c, republished under its MIT licence (© tradermonty). 555 words, ~1,480 tokens.
Download SKILL.mdSave it as
.claude/skills/data-quality-checker/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- data-quality-checker
- description
- Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.
Overview
Detect common data quality issues in market analysis documents before publication. The checker validates five categories: price scale consistency, instrument notation, date/weekday accuracy, allocation totals, and unit usage. All findings are advisory -- they flag potential issues for human review rather than blocking publication.
When to Use
- Before publishing a weekly strategy blog or market analysis report
- After generating automated market summaries
- When reviewing translated documents (English/Japanese) for data accuracy
- When combining data from multiple sources (FRED, FMP, FINVIZ) into one report
- As a pre-flight check for any document containing financial data
Prerequisites
- Python 3.9+
- No external API keys required
- No third-party Python packages required (uses only standard library)
Workflow
Step 1: Receive Input Document
Accept the target markdown file path and optional parameters:
--file: Path to the markdown document to validate (required)--checks: Comma-separated list of checks to run (optional; default: all)--as-of: Reference date for year inference in YYYY-MM-DD format (optional)--output-dir: Directory for report output (optional; default:reports/)
Step 2: Execute Validation Script
Run the data quality checker script:
python3 skills/data-quality-checker/scripts/check_data_quality.py \
--file path/to/document.md \
--output-dir reports/To run specific checks only:
python3 skills/data-quality-checker/scripts/check_data_quality.py \
--file path/to/document.md \
--checks price_scale,dates,allocationsTo provide a reference date for year inference (useful for documents without explicit year in dates):
python3 skills/data-quality-checker/scripts/check_data_quality.py \
--file path/to/document.md \
--as-of 2026-02-28Step 3: Load Reference Standards
Read the relevant reference documents to contextualize findings:
references/instrument_notation_standard.md-- Standard ticker notation, digit-count hints, and naming conventions for each instrument classreferences/common_data_errors.md-- Catalog of frequently observed errors including FRED data delays, ETF/futures scale confusion, holiday oversights, allocation total pitfalls, and unit confusion patterns
Use these references to explain findings and suggest corrections.
Step 4: Review Findings
Examine each finding in the output:
- ERROR -- High confidence issues (e.g., date-weekday mismatches verified by calendar computation). Strongly recommend correction.
- WARNING -- Likely issues that need human judgment (e.g., price scale anomalies, notation inconsistencies, allocation sums off by more than 0.5%).
- INFO -- Informational notes (e.g., mixed bp/% usage that may be intentional).
Step 5: Generate Quality Report
The script produces two output files:
- JSON report (
data_quality_YYYY-MM-DD_HHMMSS.json): Machine-readable list of findings with severity, category, message, line number, and context. - Markdown report (
data_quality_YYYY-MM-DD_HHMMSS.md): Human-readable report grouped by severity level.
Present the findings to the user with explanations referencing the knowledge base. Suggest specific corrections for each issue.
Show full SKILL.md (195 more words)Show less
Output Format
JSON Finding Structure
{
"severity": "WARNING",
"category": "price_scale",
"message": "GLD: $2,800 has 4 digits (expected 2-3 digits)",
"line_number": 5,
"context": "GLD: $2,800"
}Markdown Report Structure
# Data Quality Report
**Source:** path/to/document.md
**Generated:** 2026-02-28 14:30:00
**Total findings:** 3
## ERROR (1)
- **[dates]** (line 12): Date-weekday mismatch: January 1, 2026 (Monday) -- actual weekday is Thursday
## WARNING (2)
- **[price_scale]** (line 5): GLD: $2,800 has 4 digits (expected 2-3 digits)
> `GLD: $2,800`
- **[allocations]**: Allocation total: 110.0% (expected ~100%)Resources
scripts/check_data_quality.py-- Main validation scriptreferences/instrument_notation_standard.md-- Notation and price scale referencereferences/common_data_errors.md-- Common error patterns and prevention
Key Principles
Advisory mode: All findings are warnings for human review. The script always exits with code 0 on successful execution, even when findings are present. Exit code 1 is reserved for script failures (file not found, parse errors).
Section-aware allocation checking: Only percentages within allocation sections (identified by headings like "配分", "Allocation", or table columns like "ウェイト", "目安比率") are checked. Random percentages in body text (probability, RSI, YoY growth) are ignored.
Bilingual support: Handles both English and Japanese date formats, weekday names, and section headings. Full-width characters (％, 〜, en-dash) are normalized before processing.
Year inference: For dates without an explicit year, the checker infers the year using (in priority order): the
--as-ofoption, a YYYY pattern found in the document title/metadata, or the current year with a 6-month cross-year heuristic.Digit-count heuristic: Price scale validation uses digit counts (number of digits before the decimal point) rather than absolute price ranges. This approach is resilient to price changes over time while still catching ETF/futures confusion errors.
© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (scripts, references) in skills/data-quality-checker of tradermonty/claude-trading-skills.
- SKILL.md
- references/common_data_errors.md
- references/instrument_notation_standard.md
- requirements.txt
- scripts/check_data_quality.py
- scripts/tests/conftest.py
- scripts/tests/test_check_data_quality.py
Open the folder on GitHubat commit eab8d5c
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Data Quality Checker 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Data Quality Checker this skilltradermonty/claude-trading-skills | 3k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Openbb Data Fetchermonarchjuno/vibe-investing | 299 | — | ~2.9k | Automated safety check: Notes | MIT | |
| Research Methodologychekusu/wanman | 688 | — | ~533 | Automated safety check: Pass | Apache-2.0 | |
| 0xarchiveLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.4k | Automated safety check: Notes | MIT | |
| Manussanjay3290/ai-skills | 431 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Question2reportrefraction-ray/xalpha | 2.7k | — | ~3.2k | Automated safety check: Pass | MIT |
Similar skills
Research Methodology
chekusu/wanman
Methodology for market research and data collection, ensuring data quality and source traceability
0xarchive
LeoYeAI/openclaw-master-skills
Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3.
Manus
sanjay3290/ai-skills
Delegate complex, long-running tasks to Manus AI agent for autonomous execution.
Question2report
refraction-ray/xalpha
Turn a natural-language financial question into a polished, self-contained HTML report.
Dingo Verify
MigoXLab/dingo
A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.
More from tradermonty/claude-trading-skills
All 74 skills in this repoTechnical Analyst
tradermonty/claude-trading-skills
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.
Theme Detector
tradermonty/claude-trading-skills
Detect and analyze trending market themes across sectors. An agent skill from tradermonty/claude-trading-skills.
Trader Memory Core
tradermonty/claude-trading-skills
Track investment theses across their lifecycle — from screening idea to closed position with postmortem.
Edge Strategy Reviewer
tradermonty/claude-trading-skills
Critically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism.
Sector Analyst
tradermonty/claude-trading-skills
This skill should be used when analyzing sector rotation patterns and market cycle positioning.
Stanley Druckenmiller Investment
tradermonty/claude-trading-skills
Druckenmiller Strategy Synthesizer - Integrates 8 upstream skill outputs (Market Breadth, Uptrend Analysis, Market Top, Macro Regime, FTD Detector, VCP Screener, Theme Detector, CANSLIM Screener)…
Related
Categories
Questions about Data Quality Checker
What does Data Quality Checker do?
Validate data quality in market analysis documents and blog articles before publication. Data Quality Checker is an agent skill from tradermonty/claude-trading-skills. Validate data quality in market analysis documents and blog articles before publication.
When should I use Data Quality Checker?
Data Quality Checker fits situations like: checking for price scale inconsistencies (ETF vs futures); instrument notation errors; date/day-of-week mismatches; allocation total errors.
How do I install Data Quality Checker in Claude Code?
Run `npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a claude-code`. Or copy the skill folder (skills/data-quality-checker in tradermonty/claude-trading-skills) into .claude/skills/data-quality-checker in your project. Claude Code loads it when a task matches its description.
How do I install Data Quality Checker in Codex?
Run `npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -a codex`. Or copy the skill folder (skills/data-quality-checker in tradermonty/claude-trading-skills) into .agents/skills/data-quality-checker in your project. Codex loads it when a task matches its description.
Can I use Data Quality Checker 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 tradermonty/claude-trading-skills --skill data-quality-checker -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-quality-checker, .gemini/skills/data-quality-checker, .github/skills/data-quality-checker and .opencode/skills/data-quality-checker in your project.
What does Data Quality Checker need to run?
Going by SKILL.md and its folder, Data Quality Checker needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Data Quality Checker 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 Data Quality Checker 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 Data Quality Checker use?
Data Quality Checker 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 Data Quality Checker use?
About 1.5k tokens (SKILL.md is roughly 5.9k 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 2.7k tokens, read only when the agent opens those files.
What are the alternatives to Data Quality Checker?
Skills that share tags, products or a category with Data Quality Checker: Openbb Data Fetcher (monarchjuno/vibe-investing, 299 stars), Research Methodology (chekusu/wanman, 688 stars), 0xarchive (LeoYeAI/openclaw-master-skills, 2.2k stars) and Manus (sanjay3290/ai-skills, 431 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Quality Checker?
tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,973 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 5, 2026.
Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.