← Back to Skills Marketplace
alphaear-signal-tracker
by
zhouzhonglu8-png
· GitHub ↗
· v1.0.0
· MIT-0
103
Downloads
0
Stars
3
Active Installs
1
Versions
Install in OpenClaw
/install alphaear-signal-tracker
Description
Track finance investment signal evolution and update logic based on new finance market information. Use when monitoring finance signals and determining if th...
README (SKILL.md)
AlphaEar Signal Tracker Skill
Overview
This skill provides logic to track and update investment signals. It assesses how new market information impacts existing signals (Strengthened, Weakened, Falsified, or Unchanged).
Capabilities
1. Track Signal Evolution
1. Track Signal Evolution (Agentic Workflow)
YOU (the Agent) are the Tracker. Use the prompts in references/PROMPTS.md.
Workflow:
- Research: Use FinResearcher Prompt to gather facts/price for a signal.
- Analyze: Use FinAnalyst Prompt to generate the initial
InvestmentSignal. - Track: For existing signals, use Signal Tracking Prompt to assess evolution (Strengthened/Weakened/Falsified) based on new info.
Tools:
- Use
alphaear-searchandalphaear-stockskills to gather the necessary data. - Use
scripts/fin_agent.pyhelper_sanitize_signal_outputif needing to clean JSON.
Key Logic:
- Input: Existing Signal State + New Information (News/Price).
- Process:
- Compare new info with signal thesis.
- Determine impact direction (Positive/Negative/Neutral).
- Update confidence and intensity.
- Output: Updated Signal.
Example Usage (Conceptual):
# This skill is currently a pattern extracted from FinAgent.
# In a future refactor, it should be a standalone utility class.
# For now, refer to `scripts/fin_agent.py`'s `track_signal` method implementation.
Dependencies
agno(Agent framework)sqlite3(built-in)
Ensure DatabaseManager is initialized correctly.
Usage Guidance
This skill implements an agentic financial research and signal-tracking pipeline that will: fetch web pages and news, call external data/tool skills (alphaear-search, alphaear-stock), and read/write a local sqlite database. Before installing/running: 1) Review scripts/utils/database_manager.py and scripts/utils/news_tools.py to confirm where the DB files live and what data is written (the toolkits perform SQL UPDATEs). 2) Expect runtime network requests to arbitrary URLs (fetch_news_content) and ensure the execution environment is safe and sandboxed. 3) Note the skill can load JSON templates from a config directory (scripts/schema/isq_template.py -> load_templates_from_config) even though no config paths or env vars are declared—validate those file-read behaviors. 4) If you need to limit risk, run it in an isolated environment, disable autonomous invocation if you do not want the agent to call these tools automatically, and do not provide sensitive credentials or system-level file access. If you want a cleaner guarantee, request that the author explicitly declare DB paths, any external API keys the code expects, and document all persistent writes the skill performs.
Capability Analysis
Type: OpenClaw Skill
Name: alphaear-signal-tracker
Version: 1.0.0
The AlphaEar Signal Tracker is a comprehensive financial analysis skill bundle designed for market research, signal tracking, and automated reporting. It utilizes legitimate external services such as Jina Reader for web scraping, Akshare for financial data, and various LLM providers for analysis. While the code contains some technically risky patterns—such as the use of `torch.load` in `scripts/utils/predictor/evaluation.py` (which can be vulnerable to RCE via malicious pickle files) and `os.popen` in `scripts/utils/md_to_html.py` for system timestamps—these appear to be standard implementation choices for its functional domain rather than intentional malware. There is no evidence of data exfiltration, unauthorized persistence, or malicious prompt injection.
Capability Assessment
Purpose & Capability
Name/description match what the package does: the code and prompts implement research, signal parsing, tracking, and reporting for financial signals. Declared dependencies ('agno' and sqlite3) align with the toolkit and DB usage seen in scripts/tools/toolkits.py and scripts/fin_agent.py.
Instruction Scope
Prompts and SKILL.md instruct agents to fetch web content, call tool methods like search_ticker/get_stock_price, and run multi-step agentic workflows. The prompts require: (a) fetching arbitrary URLs and webpage content (scripts/tools/toolkits.py -> fetch_news_content), (b) updating a local database (enrich_news_content executes SQL UPDATE on daily_news), and (c) strict requirements to call tools 'for EVERY mentioned company'. These actions go beyond pure 'analysis' and involve network I/O and modifications to local storage; the SKILL.md does not explicitly call out these side-effects in a security-transparent way.
Install Mechanism
No external install/downloads are specified (no install spec). All code is packaged with the skill, so there is no high-risk remote fetch during installation. This reduces supply-chain concerns, though running the included code will perform network I/O at runtime.
Credentials
The skill declares no required environment variables or config paths, but several code paths access local files and config directories (e.g., scripts/schema/isq_template.py -> load_templates_from_config reads config/isq_templates or a given config path) and a DatabaseManager (scripts/fin_agent.py, scripts/tools/toolkits.py) is used for lookups and writes. The absence of declared config/DB paths or any credential requirements reduces transparency: the skill may attempt to read local config files or create/update a local sqlite DB without notifying the user or declaring where data will be stored.
Persistence & Privilege
The skill does not request 'always: true' and does not appear to modify other skills or system-wide agent settings. However it does perform persistent actions within its own domain (reading template JSON from config paths and writing to a local DB table daily_news). That behavior is expected for a tracker/reporting tool but should be reviewed before running with sensitive data or in privileged environments.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install alphaear-signal-tracker - After installation, invoke the skill by name or use
/alphaear-signal-tracker - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
AlphaEar Signal Tracker Skill v1.0.0
- Initial release of signal tracking logic for finance investment signals.
- Tracks and updates investment signal states (Strengthened, Weakened, Falsified, or Unchanged) based on new market information.
- Outlines an agentic workflow using research, analysis, and signal evolution tracking prompts.
- Integrates with `alphaear-search` and `alphaear-stock` for data gathering.
- Depends on `agno` agent framework and uses a `DatabaseManager` with `sqlite3`.
Metadata
Frequently Asked Questions
What is alphaear-signal-tracker?
Track finance investment signal evolution and update logic based on new finance market information. Use when monitoring finance signals and determining if th... It is an AI Agent Skill for Claude Code / OpenClaw, with 103 downloads so far.
How do I install alphaear-signal-tracker?
Run "/install alphaear-signal-tracker" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is alphaear-signal-tracker free?
Yes, alphaear-signal-tracker is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does alphaear-signal-tracker support?
alphaear-signal-tracker is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created alphaear-signal-tracker?
It is built and maintained by zhouzhonglu8-png (@zhouzhonglu8-png); the current version is v1.0.0.
More Skills