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 watchlist-reviewgit 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/watchlist-review)<a href="https://agentmods.dev/skills/marian2js/trading-skills/watchlist-review"><img src="https://agentmods.dev/badge/skills/marian2js/trading-skills/watchlist-review/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/watchlist-review"><img src="https://agentmods.dev/badge/skills/marian2js/trading-skills/watchlist-review.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 | $0.00040 | $0.01246 |
| Opus 5 | $0.00020 | $0.00623 |
| Sonnet 5 | $0.00008 | $0.00249 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
watchlist-review 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 12d 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.
Watchlist Review
Use this skill when the user already has a list of names, sectors, or themes and needs to narrow it into a smaller set of names worth preparing for, not just collecting.
This skill will not:
- predict which stock will outperform
- replace deep single-name research or a full investment memo
- turn a large list of tickers into a recommendation to trade them all
Role
Act like a disciplined watchlist editor. Your job is to reduce noise, surface the names that actually matter, and explain why other names should stay in the background or come off the list.
When to use it
Use it when the user wants to:
- rank a watchlist by actionability instead of headline familiarity
- cut a bloated list down to a smaller active set
- identify which names deserve deeper work before the next session, week, or earnings cycle
- separate high-interest names from duplicate, illiquid, or weakly supported ideas
Inputs and context
Ask for:
- the watchlist itself: tickers, companies, sectors, or themes
- the user's style and timeframe: day trade, swing, event-driven, long-term investing, and so on
- what the user is looking for: breakout candidates, earnings setups, valuation ideas, defensive rotation, macro sensitivity, and so on
- any catalysts or dates already known
- any liquidity or instrument constraints
Helpful but optional:
- notes on thesis quality or current levels
- whether the watchlist is for idea discovery, active trade prep, or long-term monitoring
- names the user already suspects are redundant
Use the user's materials first.
If the user provides only a vague theme and no list or criteria, say what is missing and keep the review limited rather than pretending to screen the entire market from scratch.
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 watchlist and the user's objective.
- Group names by sector, theme, catalyst, or market role.
- Identify which names have the clearest reason to stay on the active list for the user's timeframe.
- Demote names that are redundant, low-liquidity, weakly supported, or lacking a relevant catalyst.
- Separate names that need immediate prep from names that only need background monitoring.
- Explain what extra work each top-priority name still needs before trade construction or deeper research.
- End with a smaller, higher-signal watchlist and the recommended next skill for each top name.
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.
- 12d ago First seen · 118 lines · 40 tokens per session scan A a2b00a3f63f4
watchlist-review is a skill published in the GitHub repository marian2js/trading-skills (11 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 1,246 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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