CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

us-stock-prediction

美股个股次日走势预测 skill。当用户输入美股股票代码(如 AAPL、TSLA)或公司名称(如 "苹果"、"特斯拉"),并要求预测次日(T+1)走势、给出交易计划、评估盈亏概率时,触发本 skill。skill 会用 mcp__MiniMax__web_search 搜证盘前 90 分钟的关键信息(财报、盘后走势、隔夜期货、期权异动、内部人交易、宏观日历等),按"五维分析框架"(基本面/技术面/资金面/情绪面/宏观面)交叉验证,产出可量化的方向预测(涨/跌/横盘 + 概率百分比 + 区间),并写入当前项目 markdown 目录下的 `us-stock-{TICKER}-{YYYY-MM-DD}.md`。输出必须有完整的逻辑推导链(每一维的打分过程与数据来源)、术语通俗解释、mermaid/ascii 流程图或可视化图表。适用场景:盘前/盘中想做次日的方向判断与交易计划;不适用:长线基本面估值(应改用 finance 领域其他 skill)、日内高频交易、加密货币/港股/A股。

73

Quality

92%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, well-sequenced decision procedure with concrete scoring rules, formulas, degradation paths, and a delivery-gating checklist — excellent on actionability and workflow clarity. Its weakness is duplication: the inline output template and search-query lists restate content already in the bundle's reference files, inflating the body to ~350 lines when a leaner overview pointing to those files would serve better.

Suggestions

Replace the ~80-line inline markdown template in 步骤 6 with the required-section list plus a pointer to `references/output-template.md` (e.g. "章节顺序固定:核心结论/五维分析/价格区间/交易计划/风险/术语/来源;骨架直接复制 `references/output-template.md`"), since the reference file already bills itself as the copyable skeleton.

Trim the P0/P1/P2 query blocks to just the priority rule and 2-3 example queries, delegating the full situational keyword combinations to `references/search-queries.md`.

Move the worked example in 步骤 6 (the filled 五维加权得分 table and example values) into the output template reference, keeping only the empty schema in the body.

DimensionReasoningScore

Conciseness

The body is domain-specific rather than explaining known concepts, but ~80 lines of the full markdown output template are inlined in 步骤 6 even though `references/output-template.md` (325 lines, "可直接复制的 markdown 骨架") exists for exactly that, and the P0/P1/P2 query blocks duplicate `references/search-queries.md`. This is the 'mostly efficient but could be tightened' anchor; not 4 because the duplication of two existing reference files is a substantial, avoidable token cost, and not 2 because the material is skill-specific guidance rather than padded concept explanation.

3 / 5

Actionability

Everything is executable: literal search query strings ("{ticker} earnings after-hours", "ES NQ futures overnight"), a -2..+2 scoring scale with worked examples and weight thresholds (> +1.0 = 强看多), a probability formula with explicit clamp rules (35%-65%, hard clamp at 65%), a position-sizing formula ("仓位(股数) = (账户净值 × 风险预算%) ÷ (入场价 − 止损价)"), fixed file naming, and a concrete self-check checklist. Copy-paste-ready guidance covering common cases; nothing pseudocode.

5 / 5

Workflow Clarity

A 7-step strictly ordered flow (标的解析 → 搜证 → 打分 → 推演 → 交易计划 → 写文件 → 摘要) with explicit error-recovery paths: search failure downgrades confidence and mandates an "信息缺失" annotation, hedge signals force a confidence downgrade, and the 自检清单 gates delivery ("未通过任何一项 = 重新写,不能交付") — clear sequence with validation checkpoints and feedback loops.

5 / 5

Progressive Disclosure

Four real, one-level-deep references (five-dimension-framework.md, terminology.md, output-template.md, search-queries.md — all verified on disk with substantive content), each signposted inline ("详细规则见 references/five-dimension-framework.md") and summarized in the 资源引用 section with what each contains. Not 5: the body inlines the full output template and full query lists whose canonical home is those reference files, so the split between overview and detail isn't clean.

4 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is comprehensive and dense with concrete triggers, capabilities, output artifacts, and explicit scope boundaries (including an out-of-scope list redirecting adjacent use cases). It fully answers what, when, and what-not. Its only weakness is length — it is on the padded side for a description field, though nearly every clause is load-bearing.

DimensionReasoningScore

Specificity

Multiple concrete, specific actions: "用 mcp__MiniMax__web_search 搜证盘前 90 分钟的关键信息", "按'五维分析框架'(基本面/技术面/资金面/情绪面/宏观面)交叉验证", "产出可量化的方向预测(涨/跌/横盘 + 概率百分比 + 区间)", "写入当前项目 markdown 目录下的 us-stock-{TICKER}-{YYYY-MM-DD}.md" — comprehensive, no generic filler. Not 4: coverage is complete across search, analysis, output format, and deliverable, matching the 'multiple specific concrete actions; comprehensive coverage' anchor.

5 / 5

Completeness

Both what and when are explicit: what is a detailed capability list, and when is a concrete trigger clause ("当用户输入…并要求预测次日(T+1)走势、给出交易计划、评估盈亏概率时,触发本 skill") plus explicit "适用场景" and "不适用" boundaries. This is the anchor-5 pattern; not 4 because the 'when' is fully explicit, not merely present.

5 / 5

Trigger Term Quality

Natural user phrasings with synonyms and concrete examples: "美股股票代码(如 AAPL、TSLA)或公司名称(如 '苹果'、特斯拉')", "预测次日(T+1)走势", "给出交易计划", "评估盈亏概率". Both English tickers and Chinese company names are covered, matching the 'comprehensive coverage of natural terms including synonyms' anchor; not 4 because no common variation is obviously missing.

5 / 5

Distinctiveness Conflict Risk

A clear niche (US-stock next-day direction prediction) with explicit disambiguation: "不适用:长线基本面估值(应改用 finance 领域其他 skill)、日内高频交易、加密货币/港股/A股". The out-of-scope list steers adjacent finance queries to other skills, matching 'clear niche with distinct triggers; minimal conflict risk'.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

digoal/blog

Path

skills/us-stock-prediction/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.