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Gold News Sentiment

作者 cyecho-io · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install gold-news-sentiment
功能描述
Use this skill when users want to pull recent global gold-related news, assess short-term or medium-term market sentiment for gold, connect macro drivers lik...
使用说明 (SKILL.md)

gold-news-sentiment

Use this skill to turn recent gold-related news into a structured market sentiment read.

The default job is not to produce a trading call from vibes. The default job is to:

  • gather recent gold-relevant news
  • reuse a fresh cached snapshot when available
  • separate signal from repeated headlines and noise
  • connect the news to macro drivers that usually matter for gold
  • output a disciplined conclusion with uncertainty and risk notes

When To Use

Use this skill when the user wants to:

  • get a current gold market sentiment snapshot
  • understand whether recent news is net bullish or bearish for gold
  • classify the environment as 看涨, 看跌, or 观望
  • summarize the main drivers behind gold price moves
  • run a recurring workflow that tracks gold-related sentiment over time

Core Rules

  1. Treat the output as analysis support, not investment advice.
  2. Prefer high-credibility and recent sources over volume.
  3. Do not let repeated headlines count as multiple independent signals.
  4. Separate news sentiment from price trend. They often diverge.
  5. Always state uncertainty, key assumptions, and what could invalidate the conclusion.

Workflow

1. Check for a fresh cached snapshot

Before doing a fresh pull, check whether these files already exist:

  • data/latest_sentiment.md
  • data/latest_digest.md
  • data/latest_news.json

If data/latest_sentiment.md is fresh enough for the user's need, use it first so the user gets an immediate answer. Default freshness window:

  • interactive use: 6 hours
  • slower-moving medium-term questions: up to 24 hours if the user accepts it

If the cache is stale, missing, or the user explicitly asks for a refresh, continue with a fresh pull.

For the recurring workflow, read references/automation-template.md.

2. Pull recent news

Run the bundled script from the skill directory:

python3 scripts/fetch_news.py --hours 48 --limit 40

Use --query when the user wants a narrower theme such as:

  • central bank buying
  • Fed rate cuts
  • geopolitics
  • ETF flows
  • inflation

The script outputs normalized JSON with:

  • title
  • source
  • published time
  • link
  • query bucket
  • provider
  • detected tags

For source coverage and caveats, read references/source-list.md.

For cached fast-path updates, prefer:

python3 scripts/update_snapshot.py --hours 48 --limit 50

This refreshes:

  • data/latest_news.json
  • data/latest_digest.md

The automation can then write:

  • data/latest_sentiment.md

3. Remove weak signals

Before analysis:

  • deduplicate near-identical headlines
  • down-weight aggregator rewrites
  • discard clearly stale items if newer reports supersede them
  • flag articles that mention gold only in passing

If the fetch step returns zero usable items or includes request failures, do not fabricate a sentiment conclusion. Report that retrieval failed or that the evidence is insufficient, then stop or ask for a narrower rerun.

If more detail is needed, read references/scoring-rules.md.

4. Classify each item

Classify each article into one of:

  • bullish_for_gold
  • bearish_for_gold
  • mixed_or_unclear
  • background_only

Then note the driver category:

  • Fed / rates
  • US real yields
  • USD
  • inflation
  • labor / growth slowdown
  • geopolitics / risk-off
  • central bank demand
  • ETF / investor flows
  • physical demand / supply

Do not force a directional label when the causal chain is weak.

5. Aggregate into a market read

Build the conclusion in this order:

  1. short-term news balance
  2. macro driver alignment or conflict
  3. whether price-sensitive drivers are pointing the same way
  4. remaining uncertainty

Default horizon:

  • 短线: next few sessions to two weeks
  • 中线: next one to three months

If the user does not specify a horizon, provide both.

6. Produce the final output

Use this structure unless the user asks for another format:

结论:观望

情绪方向:
- 短线:偏多
- 中线:中性

置信度:中

核心驱动:
- \x3Cdriver 1>
- \x3Cdriver 2>
- \x3Cdriver 3>

主要新闻:
1. \x3Cheadline summary + why it matters>
2. \x3Cheadline summary + why it matters>
3. \x3Cheadline summary + why it matters>

为什么不是明确看涨/看跌:
- \x3Creason>

风险提示:
- \x3Crisk 1>
- \x3Crisk 2>

Output Discipline

  • A 看涨 conclusion requires multiple aligned bullish drivers, not one dramatic headline.
  • A 看跌 conclusion requires multiple aligned bearish drivers, not just temporary USD strength.
  • Use 观望 when the main drivers conflict or the incoming news is too noisy.
  • Prefer 偏多 or 偏空 in the explanation even when the final top-line conclusion is 观望.

Validation Checks

Before answering, verify:

  • at least one high-credibility source is represented
  • the timeframe is explicit
  • bullish and bearish evidence were both considered
  • the conclusion matches the balance of evidence
  • the fetch result was non-empty and did not fail silently

Automation Guidance

For recurring runs, the automation prompt should ask for:

  • one fresh gold sentiment update
  • explicit 看涨 / 看跌 / 观望 conclusion
  • supporting drivers and risks
  • links to the most relevant recent items

When data/latest_sentiment.md exists and is fresh, answer from it first unless the user asks for a real-time refresh.

Avoid claiming certainty. If the signal is mixed, say so.

安全使用建议
This skill appears coherent and limited to fetching public RSS-style news (Google/Bing), tagging and summarizing gold-related headlines, and writing snapshot files under data/. Before installing, confirm you are comfortable allowing network access to news.google.com and bing.com from the environment where the agent runs, and that the agent may create files under the skill's data/ directory. If you run in a locked-down environment, verify Python is available and that outgoing HTTP to those hosts is permitted. Optionally review the two scripts for any environment-specific path assumptions (they write to ./data) and test them in a sandbox if you need extra assurance.
功能分析
Type: OpenClaw Skill Name: gold-news-sentiment Version: 1.0.0 The gold-news-sentiment skill bundle is a well-structured tool designed to fetch and analyze financial news. The Python scripts (fetch_news.py and update_snapshot.py) use standard libraries to retrieve RSS feeds from legitimate providers (Google and Bing News) and cache them locally in a data directory. There is no evidence of data exfiltration, unauthorized network calls, or malicious code execution; all instructions in SKILL.md and the automation templates are strictly aligned with the stated purpose of market sentiment analysis.
能力评估
Purpose & Capability
The name and description match the included artifacts: two Python scripts that fetch RSS-style news from Google/Bing, deduplicate and tag items, and render cached snapshot files for downstream analysis. There are no unrelated environment variables, binaries, or external services requested.
Instruction Scope
SKILL.md explicitly instructs running the bundled scripts and reading/writing files under the skill's data/ directory (latest_news.json, latest_digest.md, latest_sentiment.md). The runtime instructions do not ask the agent to read arbitrary system files, access unrelated credentials, or post data to any endpoints other than the RSS query endpoints described. The workflow includes sensible validation (e.g., fail if retrieval empty).
Install Mechanism
There is no install spec (instruction-only plus shipped scripts). That minimizes install-time risk. The code is plain Python using standard library urllib and XML parsing; no third-party downloads or archive extraction are performed by the skill itself.
Credentials
The skill requires no environment variables, no credentials, and no config paths. Its only external interactions are network requests to public Google News and Bing RSS endpoints to collect headlines — proportional for a news-collection tool.
Persistence & Privilege
always:false and normal implicit invocation are used. The skill writes and reads only its own snapshot files under data/ and does not request system-wide configuration changes, other skills' credentials, or elevated privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install gold-news-sentiment
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /gold-news-sentiment 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
gold-news-sentiment v1.0.0 - Initial release providing structured sentiment analysis for gold based on recent global news and macro drivers. - Supports cached sentiment snapshots for fast responses, with configurable freshness windows. - Detailed workflow for deduplicating news, classifying headlines, and linking them to key macro factors. - Outputs disciplined conclusions as 看涨, 看跌, or 观望, with explicit uncertainty and risk notes. - Designed for recurring sentiment tracking and transparent, evidence-based analysis.
元数据
Slug gold-news-sentiment
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Gold News Sentiment 是什么?

Use this skill when users want to pull recent global gold-related news, assess short-term or medium-term market sentiment for gold, connect macro drivers lik... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 202 次。

如何安装 Gold News Sentiment?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install gold-news-sentiment」即可一键安装,无需额外配置。

Gold News Sentiment 是免费的吗?

是的,Gold News Sentiment 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Gold News Sentiment 支持哪些平台?

Gold News Sentiment 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Gold News Sentiment?

由 cyecho-io(@cyecho-io)开发并维护,当前版本 v1.0.0。

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