Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/yennanliu/investskill/competitor-analysisnpx skills add yennanliu/InvestSkill --skill competitor-analysisgit clone --depth 1 https://github.com/yennanliu/InvestSkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yennanliu/investskill/competitor-analysis)<a href="https://agentmods.dev/skills/yennanliu/investskill/competitor-analysis"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/competitor-analysis.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00012 | $0.05156 |
| Opus 5 | $0.00006 | $0.02578 |
| Sonnet 5 | $0.00002 | $0.01031 |
| Haiku 4.5 | $0.00001 | $0.00516 |
Grade A, and why
competitor-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Analysis
⚠️ Data Verification — Do This Before Any Analysis
Before running any analysis, always retrieve the latest market data for the ticker:
- Fetch current price — use web search or ask the user for the live price, 52-week range, and market cap. Never assume a price from training data.
- Confirm key figures — recent earnings, revenue, key ratios (P/E, P/S, etc.) as applicable to this skill.
- State your data source — note where the numbers came from (e.g., "Google Finance, June 19 2026") at the top of the output.
- Flag stale data explicitly — if live data is unavailable, display this warning before proceeding:
⚠️ Live data unavailable. The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.
Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.
Conduct deep competitive moat analysis to assess whether a company has a durable competitive advantage, how wide that moat is, and what the competitive dynamics of its industry mean for long-term investment returns.
Overview
Competitive analysis answers the fundamental question: "Does this company have a durable competitive advantage, and how wide is its moat?" This directly determines the appropriate valuation premium or discount vs. the sector.
A company with a wide, widening moat deserves a premium P/E and P/FCF multiple because its excess returns on capital are durable. A company with no moat, or a narrowing moat, should trade at or below sector multiples regardless of near-term earnings momentum. Understanding the moat is the single most important determinant of a stock's long-term investment return — more important than any individual quarterly earnings figure.
This skill provides a structured, repeatable framework for moat identification, industry attractiveness scoring, competitive benchmarking, and innovation positioning. Output feeds directly into /dcf-valuation (to set appropriate WACC and terminal growth rate) and /fundamental-analysis (to contextualize ROIC and margin trends).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 460 lines · 12 tokens per session scan A 626c8564cded
competitor-analysis is a skill published in the GitHub repository yennanliu/InvestSkill (199 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 5,156 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
dcf-model
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interactive-dashboard
Interactive web dashboards: stock trackers, sector heatmaps, portfolio monitors — served via preview URL.
initiating-coverage
Full equity research initiation: company research, financial model, valuation, charts, 30-50 page report.
comps-analysis
Comparable company analysis: operating metrics, valuation multiples, peer benchmarking.
html-report
Self-contained styled HTML reports written to the task directory: PDF-exportable research documents with inline data, charts, and theme-aware CSS.
inline-widget
Inline HTML widgets: charts, dashboards, data tables rendered directly in the chat via ShowWidget.