CANSLIM S&P 500 Analyzer

Analyze the top 100 S&P 500 stocks by market capitalization using the local analyzer.py script, then summarize the strongest candidates for the user.

When to use

Use this skill when the user asks to:

• Run a CANSLIM analysis on large-cap U.S. stocks.
• Screen S&P 500 leaders by growth, momentum, and institutional-quality signals.
• Generate a ranked shortlist of CANSLIM-style candidates.

Inputs

Expected local files:

• Scripts/analyzer.py
• Scripts/requirements.txt (if dependencies are not already installed)

Expected script output:

• canslim_results.json (in root directory)
• Optional: canslim_results.csv

Execution rules

Follow this checklist exactly:

1. Confirm that Scripts/analyzer.py exists.
2. If dependencies are missing, install them from Scripts/requirements.txt.
3. Change to the Scripts directory or run python Scripts/analyzer.py from root.
4. Wait for the script to finish successfully.
5. Read canslim_results.json.
6. Rank stocks by CANSLIM score from highest to lowest.
7. Present the best candidates in a Markdown table.
8. Explain which CANSLIM letters each top stock passed or failed.
9. If no stock is a strong match, show the top 3 closest candidates instead.

Analysis guidance

Interpret the script output using these principles:

• C: strong recent quarterly earnings growth.
• A: strong annual growth trend.
• N: price near new highs or supported by a fresh catalyst.
• S: favorable supply-demand signal such as strong volume.
• L: market leadership versus weaker peers.
• I: meaningful institutional sponsorship.
• M: favorable trend or market direction signal.

Do not invent missing metrics. If any field is unavailable, say that the data is unavailable and continue with the remaining signals.

Output format

Return:

• A 1-2 sentence overall assessment.
• A Markdown table with the top candidates.
• A short bullet list explaining why the top names ranked highly.
• A note listing any missing data, weak signals, or caveats.

Use this table format:

Constraints

• Use only the files generated by this skill run.
• Do not claim the results are investment advice.
• Do not fabricate company names, prices, or scores.
• If the script fails, clearly report the failure and suggest checking dependencies or network access for market data.

Failure handling

If execution fails:

• State which step failed.
• Include the error message if available.
• Recommend the smallest next action, such as installing dependencies or rerunning the script.