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 Howard-Jerry/quant-agent-skills --skill stock-researchgit clone --depth 1 https://github.com/Howard-Jerry/quant-agent-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/howard-jerry/quant-agent-skills/stock-research)<a href="https://agentmods.dev/skills/howard-jerry/quant-agent-skills/stock-research"><img src="https://agentmods.dev/badge/skills/howard-jerry/quant-agent-skills/stock-research/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/howard-jerry/quant-agent-skills/stock-research"><img src="https://agentmods.dev/badge/skills/howard-jerry/quant-agent-skills/stock-research.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 | $0.00077 | $0.03683 |
| Opus 5 | $0.00039 | $0.01842 |
| Sonnet 5 | $0.00015 | $0.00737 |
| Haiku 4.5 | $0.00008 | $0.00368 |
Grade A, and why
stock-research 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 10d 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 — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
公开版适配说明（v1.0.0）：本 skill 来自作者个人量化系统，命令和文件路径是 作者技术栈的具体实现。使用前请先读术语表与适配指南： https://github.com/Howard-Jerry/quant-agent-skills/blob/main/docs/adaptation-guide.md
{{QUANT_ROOT}}是你的量化项目根目录；RRS/research_report/Vault/scripts/*.py等是作者配套组件，公开版不附带，请按指南替换为你自己的 数据层与知识库（最小骨架见仓库template/目录）。
stock-research
This is the canonical single-stock research skill. If you maintain platform copies (e.g., a Codex-side override), keep them structurally aligned with this canonical file, but do not blindly overwrite one with the other.
The old failure mode was one huge skill mixing common L3 rules, A-share rules, HK rules, partial U.S. rules, overlays, and historical patches. The new shape is: thin router first, market adapter second, shared closeout contract last.
First Decision
Before doing research, classify the request.
| Request | Action |
|---|---|
| "找票", "哪些值得 L3", "提高研究效率", broad watchlist triage | Use l3-candidate-queue; do not run full L3 first. |
| One concrete stock, L2+/L3, update, held-position refresh, or action question | Use this skill. |
| Trade ledger correction from screenshots or fills | Use brokerage-trade-screenshot-import or portfolio-cost-correction first. |
| Portfolio sizing across names | Use latest research_verdicts.json and portfolio skills after stock verdicts are current. |
For a single-stock L3, run the workflow's real six-agent evidence when the host
agent platform supports it. If real agents are unavailable or the user forbids
delegation, write fallback_reason; never claim local review is real agent
output.
Load References
Always read these first:
references/core-contract.mdreferences/market-router.mdreferences/output-contract.mdreferences/closeout-matrix.md
Then read exactly one primary market adapter:
references/markets/a-share.mdreferences/markets/h-share.mdreferences/markets/us-equity.md
Read an overlay only when the same company has another listed security:
references/overlays/ah-overlay.mdreferences/overlays/adr-hk-overlay.md
What ships with it
21 files 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.
- agents/catalyst-agent.skill.md 4.0 KB
- agents/financial-agent.skill.md 3.3 KB
- agents/growth-agent.skill.md 3.7 KB
- agents/industry-agent.skill.md 3.0 KB
- agents/report-agent.skill.md 4.4 KB
- agents/risk-agent.skill.md 2.9 KB
- references/closeout-matrix.md 3.5 KB
- references/core-contract.md 4.8 KB
- references/detailed-workflows.md 8.7 KB
- references/market-router.md 2.5 KB
- references/markets/a-share.md 5.8 KB
- references/markets/h-share.md 3.8 KB
- references/markets/us-equity.md 7.8 KB
- references/output-contract.md 14 KB
- references/overlays/adr-hk-overlay.md 1.3 KB
- references/overlays/ah-overlay.md 1.3 KB
- steps/01-check-first.skill.md 7.7 KB
- steps/02-data-collection.skill.md 39 KB
- steps/03-deep-analysis.skill.md 77 KB
- steps/04-output-delivery.skill.md 43 KB
- steps/05-quality-portfolio.skill.md 44 KB
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.
- 10d ago First seen · 280 lines · 77 tokens per session scan A 361cfe877716
stock-research is a skill published in the GitHub repository Howard-Jerry/quant-agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 3,683 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
grayscale-crypto-sectors
Use when evaluating crypto through a Grayscale-style Crypto Sectors lens: sector taxonomy, FTSE/Grayscale index eligibility, fee/usage fundamentals, sector-share valuation, ETP/trust wrappers, and Zcash-style privacy-as-money theses.
ansem-crypto
Use when evaluating crypto narratives, attention rotation, memecoin cycles, Solana-style ecosystem momentum, social distribution, and reflexive retail flows in an Ansem-style crypto market framework.
serenity
Use when evaluating US stocks through Serenity / @aleabitoreddit's AI and semiconductor supply-chain lens: upstream chokepoints, photonics/CPO bottlenecks, hyperscaler capex, institutional rotation, dilution risk, and thesis validation.
tqsdk-trading-and-data
Explain, implement, or debug TqSdk Python workflows for waitupdate or ischanging update loops, market data retrieval, historical download, account type selection, funds or positions or orders or trades, field meanings, order placement or cancellation, target-position tools, TqScenario margin trials, real-account…
buffett
Use when evaluating a business through Buffett-style ownership, owner earnings, durable moat, management quality, capital allocation, and margin-of-safety judgment.
darvas-box
Use when evaluating Nicolas Darvas-style box breakouts, new-high momentum, volume confirmation, and mechanical stop placement for liquid growth stocks.