Tushare Data
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求，转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
Generate a source-backed weekly financial trading outlook from daily-finance, finance-core-analysis, and finance-explosive-article outputs plus fresh authoritative market data.
Install Finance Weekly Outlook
$ npx skills add digoal/blog --skill finance-weekly-outlook -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install digoal/blog finance-weekly-outlook --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/finance-weekly-outlook .claude/skills/finance-weekly-outlook && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "finance-weekly-outlook" agent skill from https://github.com/digoal/blog/tree/master/skills/finance-weekly-outlook into .claude/skills/finance-weekly-outlook/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "finance-weekly-outlook", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/digoal/blog/tree/master/skills/finance-weekly-outlookType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add digoal/blog --skill finance-weekly-outlook -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install digoal/blog finance-weekly-outlook --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/finance-weekly-outlook .agents/skills/finance-weekly-outlook && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "finance-weekly-outlook" agent skill from https://github.com/digoal/blog/tree/master/skills/finance-weekly-outlook into .agents/skills/finance-weekly-outlook/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "finance-weekly-outlook", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add digoal/blog --skill finance-weekly-outlook -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install digoal/blog finance-weekly-outlook --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/finance-weekly-outlook .cursor/skills/finance-weekly-outlook && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "finance-weekly-outlook" agent skill from https://github.com/digoal/blog/tree/master/skills/finance-weekly-outlook into .cursor/skills/finance-weekly-outlook/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "finance-weekly-outlook", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/digoal/blog.git --path skills/finance-weekly-outlook--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add digoal/blog --skill finance-weekly-outlook -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install digoal/blog finance-weekly-outlook --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/finance-weekly-outlook .gemini/skills/finance-weekly-outlook && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "finance-weekly-outlook" agent skill from https://github.com/digoal/blog/tree/master/skills/finance-weekly-outlook into .gemini/skills/finance-weekly-outlook/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "finance-weekly-outlook", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install digoal/blog finance-weekly-outlookInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add digoal/blog --skill finance-weekly-outlook -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/finance-weekly-outlook .github/skills/finance-weekly-outlook && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "finance-weekly-outlook" agent skill from https://github.com/digoal/blog/tree/master/skills/finance-weekly-outlook into .github/skills/finance-weekly-outlook/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "finance-weekly-outlook", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add digoal/blog --skill finance-weekly-outlook -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install digoal/blog finance-weekly-outlook --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/finance-weekly-outlook .opencode/skills/finance-weekly-outlook && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "finance-weekly-outlook" agent skill from https://github.com/digoal/blog/tree/master/skills/finance-weekly-outlook into .opencode/skills/finance-weekly-outlook/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "finance-weekly-outlook", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
finance-weekly-outlook- GitHub stars
- 8.6k
- Token cost
- ~2.7k tokens
- SKILL.md length
- 1,231 words
- Files
- 6 (incl. references)
- Skills in repo
- 98
- Repo updated
- First seen
- Licence
- GPL-2.0
At a glance
Generate a source-backed weekly financial trading outlook from daily-finance, finance-core-analysis, and finance-explosive-article outputs plus fresh authoritative market data.
- Works in 6 steps: Build the Fact Base → Decide Macro Positioning → Select Bullish and Bearish Industries → …
- The user asks for future-one-week market outlook
- SKILL.md covers Overview, Required Inputs, Mandatory External Data Step and Workflow, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Finance Weekly Outlook is an agent skill from digoal/blog. Generate a source-backed weekly financial trading outlook from daily-finance, finance-core-analysis, and finance-explosive-article outputs plus fresh authoritative market data. Use when the user asks for future-one-week market outlook, weekly bullish/bearish sectors, stock picks, China A-share and US stock coverage, explicit buy/sell/hold trading plans, entry/exit levels, staged position sizing, sector rotation, or next-week investment opportunities; also use after the daily-finance → finance-core-analysis →…
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `agents/openai.yaml`, `references/data-checklist.md` and `references/tradingagents-method.md`).
It sits in Business, Finance & HR, covering Trading and backtesting and Stock and market analysis. The repository describes itself as: AI,Opensource,Database,Business,Finance,Minds. git clone --depth 1 https://github.com/digoal/blog. The licence is GPL-2.0.
When your agent uses it
- The user asks for future-one-week market outlook
- Weekly bullish/bearish sectors
- China A-share and US stock coverage
- Explicit buy/sell/hold trading plans
Example prompts
- “/finance-weekly-outlook”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit ad6fcb7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Finance Weekly Outlook loads about 2.7k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 156 tokens; SKILL.md has 1,231 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from digoal/blog at commit ad6fcb7, republished under its GPL-2.0 licence (© digoal). 1,231 words, ~2,697 tokens.
Download SKILL.mdSave it as
.claude/skills/finance-weekly-outlook/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- finance-weekly-outlook
- description
- Generate a source-backed weekly financial trading outlook from daily-finance, finance-core-analysis, and finance-explosive-article outputs plus fresh authoritative market data. Use when the user asks for future-one-week market outlook, weekly bullish/bearish sectors, stock picks, China A-share and US stock coverage, explicit buy/sell/hold trading plans, entry/exit levels, staged position sizing, sector rotation, or next-week investment opportunities; also use after the daily-finance → finance-core-analysis → finance-explosive-article pipeline when a concrete weekly action report is requested.
Finance Weekly Outlook
Overview
Generate a Markdown weekly outlook that identifies high-probability bullish and bearish industries and stocks for the next trading week, covering both US equities and China A-shares. The report must combine upstream finance articles, fresh authoritative data, market-behavior validation, expectation-gap reasoning, scenario falsification, and explicit trading action plans.
This skill is downstream of:
daily-finance -> finance-core-analysis -> finance-explosive-article -> finance-weekly-outlook
It can also run independently when upstream files are absent, but never invent missing data.
Required Inputs
Prefer the newest available files in the current project's markdown/ directory:
markdown/daily-finance-YYYY-MM-DD.mdmarkdown/finance-core-analysis-YYYY-MM-DD.mdmarkdown/finance-explosive-article-YYYY-MM-DD.md
If multiple dates exist, use the most recent trading-relevant date unless the user specifies a date. Preserve upstream facts and sources, but re-check any number used in the final thesis.
Resolve the forecast window explicitly:
- For US markets, use the next US trading week.
- For A-shares, use the next China trading week.
- If holidays make the US and China windows different, show both date ranges in the header.
- If the user asks for "next week" during an active session, use the next full trading week unless they ask for the remaining days of the current week.
If upstream files are absent, run independently with fresh data and state 上游文件：未使用 in the report metadata.
Mandatory External Data Step
Always access current external data before writing a current weekly outlook. Use primary/authoritative sources where possible:
- Official: central banks, statistics agencies, exchanges, regulators, listed-company filings, investor relations, earnings releases
- Market data: exchange pages, ETF/fund issuer data, CBOE/FRED/Treasury/Nasdaq/NYSE, HKEX, SSE/SZSE, CSRC/PBOC, Wind/Eastmoney/同花顺 only when official data is unavailable
- News: Reuters, Bloomberg, FT, WSJ, 财新, 第一财经, 财经, 21世纪经济报道, 经济观察报
Read references/data-checklist.md when planning the data pull. Read references/tradingagents-method.md when structuring the reasoning and risk review.
Do not rely on self-media, unverified social posts, headlines without source traceability, or a single isolated stock example to support an industry conclusion.
Current data must be no older than the latest completed trading session unless a source publishes less frequently. Label stale indicators and avoid using them as decisive evidence.
Workflow
1. Build the Fact Base
Extract upstream facts from daily/core/explosive articles, then add fresh data:
- Macro liquidity: Fed/PBOC signal, US 10Y, China 10Y, DXY, SHIBOR/DR007, Treasury/TGA or Fed balance-sheet context when relevant
- Risk appetite: VIX, put/call, market breadth, new highs/new lows, major index trend
- Capital flow: US sector ETF flow when available, A-share northbound/southbound if available, margin financing, main-board/STAR/ChiNext turnover structure
- Sector behavior: weekly relative strength vs S&P 500 and CSI 300, volume confirmation, support/resistance, breakout/failure
- Expectations and crowding: earnings revisions, revenue/EPS consensus direction, valuation percentile if available, fund positioning/crowdedness proxies
- Catalysts: earnings calendar, macro data calendar, policy events, product/order/legal/regulatory events
Tag data clearly:
【实】confirmed actual data【预】market expectation or consensus【估】model/analyst estimate【待】reported but not independently verified; avoid using as core evidence
Produce an internal evidence matrix before selecting sectors:
| Market | Constraint | Indicator | Latest value/status | Source | Decision impact |
|---|---|---|---|---|---|
| US | Rates/liquidity/risk | e.g. 10Y, DXY, VIX | value + date | source | supports/weakens/neutral |
| China A | Liquidity/policy/breadth | e.g. DR007, turnover, margin | value + date | source | supports/weakens/neutral |
2. Decide Macro Positioning
State whether next week should be 偏进攻, 中性偏进攻, 中性偏防守, or 偏防守.
Use this chain:
宏观约束 -> 资金行为 -> 风格偏好 -> 仓位上限 -> 证伪信号
Do not say "risk appetite improves" without naming the observable variable that improved.
Positioning must include:
- Gross exposure ceiling, expressed as a range, not a false precision point.
- Conditions for increasing exposure.
- Conditions for cutting exposure.
- One correlated-risk warning if several trades depend on the same macro factor.
3. Select Bullish and Bearish Industries
Cover both US equities and A-shares. Output at least:
- 2-4 bullish industries across the two markets
- 2-4 bearish industries across the two markets
- At least one bullish and one bearish view for each market unless data quality makes this unsafe; if so, explain why
For each industry, require:
- Industry-level evidence, not only a single stock move
- Market-behavior validation: price, volume, relative strength, breadth, or fund flow
- Catalyst and expectation-gap judgment
- Assumptions and what changes if assumptions fail
- Crowding/overpricing risk check
Avoid hindsight logic:
- Do not mark a current hot theme bullish merely because it is hot.
- Do not mark a falling industry bearish merely because it fell.
- Ask: "what is not yet priced for the coming week?"
Use confidence labels:
高: thesis has macro support, industry confirmation, catalyst, and clear invalidation.中: thesis has 2-3 supports but one important uncertainty.低: watchlist only; do not force a trading plan.
Show full SKILL.md (505 more words)Show less
4. Select Stocks and Trading Plans
For each market, provide a small number of stock calls. Prefer quality over quantity:
- US equities: 2-4 stocks total, including bullish and bearish/avoid candidates
- A-shares: 2-4 stocks total, including bullish and bearish/avoid candidates
Each stock must include:
- Name, ticker/code, market
- Direction: buy/add/hold/reduce/sell/avoid/watch
- Thesis with 2-4 evidence points
- Three-factor score: earnings expectation, valuation/sentiment, liquidity/risk premium
- Expectation-gap quadrant:
- Good news + high expectation = risk of "priced in"
- Good news + low expectation = bullish catalyst
- Bad news + high expectation = bearish shock
- Bad news + low expectation = possible exhaustion/reversal
- Concrete action plan:
- Entry trigger or buy zone
- Staged position sizing
- Stop-loss or invalidation level
- Take-profit or reduction plan
- Hold/sell/buy decision for existing positions
- Daily tracking indicators and action timetable
If precise price levels are unavailable from reliable data, use technical conditions instead of fabricated numbers, such as "daily close above prior 20-day high with volume above 20-day average".
Trading action rules:
- Do not give a
buy/add/sellaction unless entry, invalidation, sizing, and review date are all present. - Use
watch/avoid/holdwhen data quality is insufficient or the setup lacks confirmation. - Position sizing must be bounded by thesis confidence and correlated exposure; avoid all-in or certainty language.
- For existing positions, distinguish
持有观察,减仓, and止损退出conditions.
5. Run TradingAgents-Style Internal Review
Before finalizing, simulate these roles in writing or internally:
- Fundamental analyst: earnings, balance sheet, valuation, guidance
- News/policy analyst: catalysts, official data, regulatory and geopolitical events
- Technical/market analyst: trend, volume, breadth, support/resistance
- Bull researcher: best case and upside path
- Bear researcher: strongest counterargument and downside path
- Trader: translate thesis into action rules
- Risk manager: position sizing, invalidation, drawdown, correlated exposures
Use structured findings rather than long dialogue. The final article should show the result of the debate through assumptions, alternatives, and risk controls.
For each final stock call, include the review result in compact form:
多头证据 / 空头反驳 / 最终处理 / 证伪信号
6. Validate Before Saving
Check:
- Every major number has a source.
- Dates, units, direction, actual vs expected, intraday vs close are correct.
- US and A-share coverage is explicit.
- Bullish and bearish views are both present.
- Industry calls have industry-level evidence.
- Stock calls include clear trading actions, not vague "关注".
- Each key thesis has assumptions and falsification signals.
- The report includes charts/tables/Mermaid diagrams.
- The article ends with a disclaimer.
If a claim cannot be verified, weaken it, label it, or remove it.
Legal and suitability boundary:
- Write actions as scenario-based public-information plans, not personalized advice.
- Do not imply guaranteed returns, target certainty, or suitability for all investors.
- Keep the disclaimer, but do not use it to justify unsupported precision.
Output Structure
Save the final Markdown to:
markdown/finance-weekly-outlook-YYYY-MM-DD.md
Use the report date, create markdown/ if needed, and write UTF-8.
Required structure:
# 未来一周中美股多空展望｜YYYY-MM-DD 至 YYYY-MM-DD
> 执行摘要：...
## 一、结论先行：下周仓位与主线
Include: forecast window, report date, upstream files used, macro positioning, gross exposure ceiling, top bullish/bearish themes.
## 二、数据仪表盘
Include tables and at least one Mermaid diagram showing:
触发变量 -> 数据验证 -> 预期差 -> 交易计划 -> 证伪信号
## 三、行业多空矩阵
## 四、美股个股操盘计划
## 五、A股个股操盘计划
## 六、情景推演：前提、备选观点、证伪信号
## 七、下周关键日历与跟踪清单
## 八、数据来源
## 免责声明
本文仅供参考，不构成投资建议。市场有风险，投资需谨慎。文中观点基于公开信息、特定前提假设和当时可得数据，未来可能因宏观政策、流动性、财报、监管、地缘政治和市场情绪变化而失效。任何买入、卖出、持有或仓位安排都不应被视为个性化投资建议，投资者应结合自身风险承受能力独立决策。Writing Rules
- Write in Chinese unless the user requests another language.
- Be direct, structured, and source-backed.
- Use "不是A，而是B" only when it exposes a real mechanism mismatch.
- Do not overstate probability; use "基准情景/备选情景/尾部风险" instead of false certainty.
- Give explicit actions for individual stocks, but keep the disclaimer and assumption boundaries clear.
- Prefer concise tables over long prose where decisions need comparison.
© digoal, GPL-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (references) in skills/finance-weekly-outlook of digoal/blog.
- SKILL.md
- SKILL.md.bak.20260525-091704
- agents/openai.yaml
- references/data-checklist.md
- references/tradingagents-method.md
- test-prompts.json
Open the folder on GitHubat commit ad6fcb7
Compare with similar skills
Finance Weekly Outlook next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Finance Weekly Outlook this skilldigoal/blog | 8.6k | — | ~2.7k | Automated safety check: Pass | GPL-2.0 | |
| Tushare Datazillionare/zillionare | 322 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| Tradingview MCPatilaahmettaner/tradingview-mcp | 5k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Digital Oraclekomako-workshop/digital-oracle | 878 | — | ~5.9k | Automated safety check: Pass | MIT | |
| Worth Buy Stocksstarriv/worth-buy-stocks | 175 | — | ~4.5k | Automated safety check: Notes | None | |
| Regimejackson-video-resources/markov-hedge-fund-method | 484 | — | ~1.6k | Automated safety check: Pass | Custom licence |
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Questions about Finance Weekly Outlook
What does Finance Weekly Outlook do?
Generate a source-backed weekly financial trading outlook from daily-finance, finance-core-analysis, and finance-explosive-article outputs plus fresh authoritative market data. Finance Weekly Outlook is an agent skill from digoal/blog. Generate a source-backed weekly financial trading outlook from daily-finance, finance-core-analysis, and finance-explosive-article outputs plus fresh authoritative market data.
When should I use Finance Weekly Outlook?
Finance Weekly Outlook fits situations like: the user asks for future-one-week market outlook; weekly bullish/bearish sectors; china A-share and US stock coverage; explicit buy/sell/hold trading plans.
How do I install Finance Weekly Outlook in Claude Code?
Run `npx skills add digoal/blog --skill finance-weekly-outlook -a claude-code`. Or copy the skill folder (skills/finance-weekly-outlook in digoal/blog) into .claude/skills/finance-weekly-outlook in your project. Claude Code loads it when a task matches its description.
How do I install Finance Weekly Outlook in Codex?
Run `npx skills add digoal/blog --skill finance-weekly-outlook -a codex`. Or copy the skill folder (skills/finance-weekly-outlook in digoal/blog) into .agents/skills/finance-weekly-outlook in your project. Codex loads it when a task matches its description.
Can I use Finance Weekly Outlook in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add digoal/blog --skill finance-weekly-outlook -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/finance-weekly-outlook, .gemini/skills/finance-weekly-outlook, .github/skills/finance-weekly-outlook and .opencode/skills/finance-weekly-outlook in your project.
What does Finance Weekly Outlook need to run?
SKILL.md names no scripts, command-line tools or credentials: Finance Weekly Outlook is instructions for the agent only.
Does Finance Weekly Outlook access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Finance Weekly Outlook safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Finance Weekly Outlook use?
Finance Weekly Outlook is published under the GPL-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Finance Weekly Outlook use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.
What are the alternatives to Finance Weekly Outlook?
Skills that share tags, products or a category with Finance Weekly Outlook: Tushare Data (zillionare/zillionare, 322 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Digital Oracle (komako-workshop/digital-oracle, 878 stars) and Worth Buy Stocks (starriv/worth-buy-stocks, 175 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Finance Weekly Outlook?
digoal (a GitHub user) maintains it in digoal/blog, which has 8,588 GitHub stars. The repository holds 98 skills in this directory. The repository was last updated on October 9, 2026.
Source: digoal/blog on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.