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

1. Identify the goal: setup or startup, run a valuation, compare models, debug a failure, or inspect repo internals.
2. If the repo is available, inspect README.md, .env.example, docker-compose.local.yml, and relevant service files before answering.
3. For installation, startup, and basic valuation runs, read {baseDir}/references/setup-and-run.md.
4. For provider or model changes, prompt dumping, or controlled comparisons, read {baseDir}/references/model-and-provider-experiments.md.
5. For runtime failures, health checks, logs, or recovery steps, read {baseDir}/references/troubleshooting.md.
6. 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/