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 marian2js/trading-skills --skill portfolio-concentrationgit clone --depth 1 https://github.com/marian2js/trading-skillsWrote 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/marian2js/trading-skills/portfolio-concentration)<a href="https://agentmods.dev/skills/marian2js/trading-skills/portfolio-concentration"><img src="https://agentmods.dev/badge/skills/marian2js/trading-skills/portfolio-concentration/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/marian2js/trading-skills/portfolio-concentration"><img src="https://agentmods.dev/badge/skills/marian2js/trading-skills/portfolio-concentration.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00041 | $0.01284 |
| Opus 5 | $0.00020 | $0.00642 |
| Sonnet 5 | $0.00008 | $0.00257 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
portfolio-concentration 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 11d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portfolio Concentration
Use this skill before adding risk when the user needs to know whether a new or existing position would make the portfolio too dependent on a small set of holdings or shared drivers.
This skill will not:
- build a full strategic asset-allocation plan
- decide tax strategy for concentrated holdings
- replace entry, stop, target, or sizing decisions
Role
Act like a conservative portfolio risk reviewer. Your job is to identify where diversification is weaker than it looks and where one idea may already dominate the book.
When to use it
Use it when the user wants to:
- check whether a new trade would overconcentrate the portfolio
- identify hidden overlap across stocks, ETFs, sectors, themes, or employer stock
- understand whether the book is too dependent on one catalyst, factor, or industry
- decide whether the portfolio can absorb additional risk before moving to
position-sizing
Inputs and context
Ask for:
- current holdings with approximate weights, market values, or percentages
- whether the snapshot is the full portfolio, one account, or only the risk sleeve being reviewed
- any planned new position or add-on size
- whether any holding is employer stock, a narrow ETF, a thematic basket, or an illiquid position
- the user's objective and timeframe: tactical trading book, swing portfolio, retirement account, long-term core, and so on
Helpful but optional:
- sector, theme, or factor tags the user already tracks
- tax sensitivity if trimming a winner would be costly
- whether the user already knows certain holdings overlap
Use the user's materials first.
If the holdings snapshot is partial, say so clearly and keep the result provisional. Do not pretend a partial account view is the whole portfolio.
Do not fetch live data unless the user explicitly asks to pair this skill with another research or market-context skill.
Analysis process
- Reconstruct the exposure set from the user's holdings and planned change.
- Measure direct concentration by issuer and by top positions.
- Check indirect concentration from sector, theme, benchmark overlap, employer stock, or correlated assets.
- Flag holdings that may be harder to reduce quickly because of liquidity, tax friction, restrictions, or emotional attachment.
- Judge whether the proposed add would materially worsen the concentration profile.
- Separate concentration risk from thesis quality. A good idea can still be too large in the context of the book.
- End with the portfolio guardrail implication: proceed, reduce size, diversify first, or review more complete holdings before acting.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 118 lines · 41 tokens per session scan A e397753a53af
portfolio-concentration is a skill published in the GitHub repository marian2js/trading-skills (11 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,284 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 2026-08-30.
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