agentleFS
daringanitchdaringanitch/claude-stock-analyst/skills/stock-moat/SKILL.md

stock-moat

Skill9 starsChanged 6 months agoMITView on GitHub

Competitive moat analysis for a stock. Use when the user asks about competitive advantage, moat, defensibility, pricing power, or how a company protects its market position.

The file

Files
SKILL.md

Deep moat analysis using the Morningstar 5-source framework:

  1. COST ADVANTAGE — can they produce cheaper than competitors? Sustainable?
  2. SWITCHING COSTS — what does it cost a customer to leave? Cite evidence (churn rates, contract lengths)
  3. NETWORK EFFECTS — does the product get better as more people use it? Direct or indirect?
  4. INTANGIBLE ASSETS — patents, brands, licenses, regulatory approvals
  5. EFFICIENT SCALE — do they operate in a market where a second competitor can't earn returns?

Then: who is the most credible threat to each source of moat and what's their progress?

Moat rating: Wide / Narrow / None Time horizon of moat: 5yr / 10yr / 20yr+

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.