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 nimadorostkar/Claude-Skills-collection --skill stock-screeninggit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/stock-screening)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/stock-screening"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/stock-screening/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/stock-screening"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/stock-screening.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00046 | $0.01499 |
| Opus 5 | $0.00023 | $0.00749 |
| Sonnet 5 | $0.00009 | $0.00300 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
stock-screening 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 9d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock Screening
Purpose
Build a screen that narrows a universe to a reviewable shortlist. A screen is a filter, not a decision — its output is a list of things to look at, and treating it as a list of things to buy is the fastest way to lose money with a spreadsheet.
When to Use
- Building a systematic screen for candidates.
- A screen that returns hundreds of results, or none.
- Evaluating whether a screen's criteria actually discriminate.
- Reviewing a screen that historically performed well and now does not.
Capabilities
- Screen design: fundamental, technical, and combined criteria.
- Criteria selection and threshold setting.
- Overfitting detection.
- Survivorship and look-ahead bias avoidance.
- Ranking and shortlisting.
Inputs
- The universe, and how it is defined.
- The characteristics being sought, and why they should matter.
- Historical data, if the screen is to be validated.
Outputs
- A screen that returns a reviewable number of names — typically 10 to 40.
- Each criterion justified by a mechanism, not by backtest fit.
- A ranked shortlist for manual review.
Workflow
- Define the universe first — Liquidity and market-capitalization floors. A screen that returns illiquid microcaps you cannot actually trade is returning noise.
- Choose criteria with a mechanism — Each filter should have a reason it should work, stated before you test it. "It scored well in the backtest" is not a mechanism; it is a warning sign.
- Use few criteria — Three to six. Each additional filter cuts the result set and increases the chance you have fitted the screen to the past rather than to a real effect.
- Set thresholds at round, defensible numbers — A revenue growth threshold of 23.7% is fitted. 20% is a judgment. The former will not survive out of sample.
- Check the biases — Does the historical universe include companies that no longer exist? If not, the backtest is measuring the performance of survivors, which is not a strategy you could have run.
- Rank and review manually — The screen produces candidates. A human — or a careful, separate analysis — decides. The screen does not know why a company is cheap.
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.
- 9d ago First seen · 128 lines · 46 tokens per session scan A 7aa9a50913da
stock-screening is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 25d ago), licensed MIT. It adds 46 tokens to every session and 1,499 once invoked, about $0.0002 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 2026-09-03.
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