Filter stocks by financial metrics and perform comparative analysis.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionstock-screenerExecute the skills CLI command in your project's root directory to begin installation:
Fetches stock-screener from dkyazzentwatwa/chatgpt-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate stock-screener. Access via /stock-screener in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Filter stocks by financial metrics and perform comparative analysis.
from stock_screener import StockScreener
screener = StockScreener()
# Load stock data
screener.load_csv("stocks.csv")
# Apply filters
results = screener.filter(
pe_ratio=(0, 20),
market_cap_min=1e9,
dividend_yield_min=2.0
)
print(results)
# Basic screening
python stock_screener.py --input stocks.csv --pe-max 20 --div-min 2.0
# Multiple filters
python stock_screener.py --input stocks.csv --pe 5 25 --pb-max 3 --cap-min 1B
# Sector filter
python stock_screener.py --input stocks.csv --sector Technology --pe-max 30
# Rank by metric
python stock_screener.py --input stocks.csv --rank-by dividend_yield --top 20
# Compare specific stocks
python stock_screener.py --input stocks.csv --compare AAPL MSFT GOOGL
# Export results
python stock_screener.py --input stocks.csv --pe-max 15 --output screened.csv
symbol,name,sector,price,pe_ratio,pb_ratio,market_cap,dividend_yield,eps,revenue_growth,profit_margin
AAPL,Apple Inc,Technology,175.50,28.5,45.2,2.8e12,0.5,6.16,8.5,25.3
MSFT,Microsoft,Technology,380.00,35.2,12.8,2.8e12,0.8,10.79,12.3,36.7
JNJ,Johnson & Johnson,Healthcare,155.00,15.2,5.8,3.8e11,2.9,10.20,5.2,22.1
class StockScreener:
def __init__(self)
# Data Loading
def load_csv(self, filepath: str) -> 'StockScreener'
def load_dataframe(self, df: pd.DataFrame) -> 'StockScreener'
# Filtering
def filter(self, **criteria) -> pd.DataFrame
def filter_by_sector(self, sectors: List[str]) -> 'StockScreener'
def filter_by_metric(self, metric: str, min_val: float = None,
max_val: float = None) -> 'StockScreener'
# Screening Presets
def value_screen(self) -> pd.DataFrame
def growth_screen(self) -> pd.DataFrame
def dividend_screen(self) -> pd.DataFrame
def quality_screen(self) -> pd.DataFrame
def custom_screen(self, criteria: Dict) -> pd.DataFrame
# Analysis
def compare(self, symbols: List[str]) -> pd.DataFrame
def rank_by(self, metric: str, ascending: bool = True) -> pd.DataFrame
def sector_summary(self) -> pd.DataFrame
def metric_distribution(self, metric: str) -> Dict
# Scoring
def score_stocks(self, weights: Dict[str, float] = None) -> pd.DataFrame
def percentile_rank(self, metrics: List[str]) -> pd.DataFrame
# Export
def to_csv(self, filepath: str) -> str
def to_json(self, filepath: str) -> str
def summary_report(self) -> str
screener.filter(
pe_ratio=(5, 20), # P/E between 5 and 20
pb_ratio_max=3.0, # P/B ratio under 3
ps_ratio_max=5.0, # Price/Sales under 5
peg_ratio_max=1.5 # PEG ratio under 1.5
)
screener.filter(
market_cap_min=1e9, # Min $1B market cap
market_cap_max=10e9, # Max $10B (mid-cap)
revenue_min=500e6 # Min $500M revenue
)
screener.filter(
dividend_yield_min=2.0, # Min 2% dividend
dividend_yield_max=8.0, # Max 8% (avoid yield traps)
payout_ratio_max=75 # Sustainable payout
)
screener.filter(
revenue_growth_min=10, # Min 10% revenue growth
earnings_growth_min=15, # Min 15% earnings growth
eps_growth_min=10 # Min 10% EPS growth
)
screener.filter(
profit_margin_min=15, # Min 15% profit margin
roe_min=15, # Min 15% return on equity
debt_to_equity_max=1.0, # Max 1.0 D/E ratio
current_ratio_min=1.5 # Min 1.5 current ratio
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
stock-screener is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added stock-screener from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in stock-screener — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend stock-screener for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
stock-screener reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: stock-screener is focused, and the summary matches what you get after install.
We added stock-screener from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for stock-screener matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: stock-screener is the kind of skill you can hand to a new teammate without a long onboarding doc.
stock-screener has been reliable in day-to-day use. Documentation quality is above average for community skills.
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