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 skills add yennanliu/InvestSkill --skill short-interestgit 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/short-interest)<a href="https://agentmods.dev/skills/yennanliu/investskill/short-interest"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/short-interest.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00012 | $0.04072 |
| Opus 5 | $0.00006 | $0.02036 |
| Sonnet 5 | $0.00002 | $0.00814 |
| Haiku 4.5 | $0.00001 | $0.00407 |
Grade A, and why
short-interest 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 8d 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 — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Short Interest 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.
Comprehensive analysis of short selling activity, squeeze potential, cost-of-borrow dynamics, and bearish positioning signals for US-listed stocks. Combines FINRA short interest data, options market signals, and technical context to assess directional risk.
Analysis Framework
1. Short Interest Overview
Measure the current level and trend of bearish positioning:
Core Short Interest Metrics
- Short interest (shares): Total number of shares sold short and not yet covered or closed
- Short float %: Short shares / Total float (shares available for public trading)
- Short interest as % of shares outstanding: Short shares / Total shares outstanding (includes locked-up insider shares)
- Float % is the more actionable metric — tighter supply amplifies squeeze dynamics
Short Float % Thresholds
Short Float % Interpretation
<2% Negligible — minimal bearish conviction
2-5% Low — modest skepticism or hedging
5-10% Moderate — meaningful bearish positioning
10-20% Elevated — significant short thesis in market
20-30% High — heavy bearish conviction, squeeze potential
>30% Extreme — high risk environment (squeeze OR fundamental collapse)
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.
- 8d ago First seen · 379 lines · 12 tokens per session scan A a4700efb34fe
short-interest is a skill published in the GitHub repository yennanliu/InvestSkill (204 stars, last pushed 2d ago), licensed MIT. It adds 12 tokens to every session and 4,072 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.
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