Stock Valuation using Aswath Damodaran methodologies
FlaggedSet up, run, compare, and debug StockValuation.io, a local-first DCF valuation platform, including Docker startup, ticker valuations, LLM provider changes, p...
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# StockValuation.io
Use this skill when the user wants help with StockValuation.io setup, local DCF runs, LLM provider or model experiments, prompt dumps, Docker logs, or valuation API usage.
Workflow
- Identify the goal: setup or startup, run a valuation, compare models, debug a failure, or inspect repo internals.
- If the repo is available, inspect `README.md`, `.env.example`, `docker-compose.local.yml`, and relevant service files before answering.
- For installation, startup, and basic valuation runs, read `{baseDir}/references/setup-and-run.md`.
- For provider or model changes, prompt dumping, or controlled comparisons, read `{baseDir}/references/model-and-provider-experiments.md`.
- For runtime failures, health checks, logs, or recovery steps, read `{baseDir}/references/troubleshooting.md`.
- Prefer exact commands, explicit service names, and reproducible steps.
Operating Rules
- Prefer the manual clone plus Docker Compose path by default.
- If the user wants the installer, tell them to download or inspect `install.sh` locally before running it instead of recommending `curl | bash`.
- Never ask the user to paste real API keys into chat. Tell them to set keys in their local environment or `.env`.
- Never print `.env` contents, echo live secrets, or suggest committing local secret files.
- Treat prompt dumping as privacy-sensitive. When `DUMP_PROMPTS=true`, prompt contents are written to `PROMPT_DUMP_DIR` on disk.
- Treat container teardown and volume deletion as destructive. Only suggest `down -v` when the user explicitly asks to reset local state.
- When only LLM settings change, restart `valuation-agent` and `bullbeargpt` unless the user also changed other infrastructure.
- When comparing experiments, keep the ticker, env changes, and output differences explicit so the comparison stays attributable.
Useful Repo Signals
- Frontend UI: `http://localhost:4200`
- Valuation service: `http://localhost:8081`
- Valuation agent: `http://localhost:5001`
- BullBearGPT: `http://localhost:5002`
- Main flow often starts with `POST /api-s/valuate`
- High-value repo files when present: `README.md`, `.env.example`, `docker-compose.local.yml`, `shared/llm_models.py`, and `scripts/`
Use Cases
- Run local DCF valuations on stock tickers using Aswath Damodaran methodologies
- Compare valuation outputs across different LLM providers and models
- Set up and debug StockValuation.io's Docker-based local development environment
- Dump and inspect LLM prompts used during valuation for prompt engineering
- Troubleshoot Docker container failures with health checks and log analysis
Pros & Cons
Pros
- +Local-first architecture keeps sensitive financial data on your machine
- +Supports multiple LLM providers for comparative valuation analysis
- +Clear operating rules around security — no API keys in chat, no secrets in logs
Cons
- -Requires Docker and multiple running services — complex setup for non-technical users
- -Skill content is workflow guidance only — actual valuation logic lives in the external platform
FAQ
What does Stock Valuation using Aswath Damodaran methodologies do?
Set up, run, compare, and debug StockValuation.io, a local-first DCF valuation platform, including Docker startup, ticker valuations, LLM provider changes, p...
What platforms support Stock Valuation using Aswath Damodaran methodologies?
Stock Valuation using Aswath Damodaran methodologies is available on Claude Code, OpenClaw.
What are the use cases for Stock Valuation using Aswath Damodaran methodologies?
Run local DCF valuations on stock tickers using Aswath Damodaran methodologies. Compare valuation outputs across different LLM providers and models. Set up and debug StockValuation.io's Docker-based local development environment.
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