Agent skill

Generating Trading Signals

by aAAaqwq in aAAaqwq/AGI-Super-Team

Multi-indicator signal generation system that analyzes price action using 7 technical indicators and produces composite BUY/SELL signals with confiden Generate trading signals using technical…

MITAuto-check passedBusiness, Finance & HR

Install Generating Trading Signals

skills CLI
$ npx skills add aAAaqwq/AGI-Super-Team --skill generating-trading-signals -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aAAaqwq/AGI-Super-Team generating-trading-signals --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/crypto-signal-generator .claude/skills/generating-trading-signals && rm -rf skills-src

Use ~/.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/

Facts

Skill name
generating-trading-signals
GitHub stars
105
Token cost
~1.6k tokens
SKILL.md length
284 words
Files
8 (incl. scripts, references)
Skills in repo
167
Repo updated
First seen
Licence
MIT

At a glance

Multi-indicator signal generation system that analyzes price action using 7 technical indicators and produces composite BUY/SELL signals with confiden Generate trading signals using technical…

  • Works in 4 steps: Quick Signal Scan → Detailed Signal Analysis → Filter and Rank Signals → …
  • Analyzing assets for trading opportunities
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 6 more sections
  • Runs Python scripts from its folder; calls python and pip

What it does

Generating Trading Signals is an agent skill from aAAaqwq/AGI-Super-Team. Multi-indicator signal generation system that analyzes price action using 7 technical indicators and produces composite BUY/SELL signals with confiden Generate trading signals using technical indicators (RSI, MACD, Bollinger Bands, etc.). Combines multiple indicators into composite signals with confidence scores. Use when analyzing assets for trading opportunities or checking technical indicators. Trigger with phrases like "get trading signals", "check indicators", "analyze for entry", "scan for opportunities"…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `config/settings.yaml`, `references/errors.md` and `references/examples.md`).

It sits in Business, Finance & HR, covering Trading and backtesting. The repository describes itself as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters. The licence is MIT.

When your agent uses it

  • Analyzing assets for trading opportunities
  • Checking technical indicators
  • With phrases like get trading signals
  • Check indicators

Example prompts

  • “get trading signals”
  • “check indicators”
  • “analyze for entry”
  • “/generating-trading-signals”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(python:*)

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Quick Signal Scan
  2. Detailed Signal Analysis
  3. Filter and Rank Signals
  4. Use Custom Watchlists

What it can do on your machine

Read from SKILL.md and the folder at commit 7cefd81. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(python:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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

Generating Trading Signals loads about 1.6k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 149 tokens; SKILL.md has 284 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~149
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from aAAaqwq/AGI-Super-Team at commit 7cefd81, republished under its MIT licence (© aAAaqwq). 284 words, ~1,618 tokens.

Download SKILL.mdSave it as .claude/skills/generating-trading-signals/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
generating-trading-signals
description
Multi-indicator signal generation system that analyzes price action using 7 technical indicators and produces composite BUY/SELL signals with confiden Generate trading signals using technical indicators (RSI, MACD, Bollinger Bands, etc.). Combines multiple indicators into composite signals with confidence scores. Use when analyzing assets for trading opportunities or checking technical indicators. Trigger with phrases like "get trading signals", "check indicators", "analyze for entry", "scan for opportunities", "generate buy/sell signals", or "technical analysis".
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(python:*)
version
2.0.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT

Generating Trading Signals

Overview

Multi-indicator signal generation system that analyzes price action using 7 technical indicators and produces composite BUY/SELL signals with confidence scores and risk management levels.

Indicators Used:

  • RSI (Relative Strength Index) - Overbought/oversold
  • MACD (Moving Average Convergence Divergence) - Trend and momentum
  • Bollinger Bands - Mean reversion and volatility
  • Trend (SMA 20/50/200 crossovers) - Trend direction
  • Volume - Confirmation of moves
  • Stochastic Oscillator - Short-term momentum
  • ADX (Average Directional Index) - Trend strength

Prerequisites

Install required dependencies:

bash
pip install yfinance pandas numpy

Optional for visualization:

bash
pip install matplotlib

Instructions

Step 1: Quick Signal Scan

Scan multiple assets for trading opportunities:

bash
python {baseDir}/scripts/scanner.py --watchlist crypto_top10 --period 6m

Output shows signal type (STRONG_BUY/BUY/NEUTRAL/SELL/STRONG_SELL) and confidence for each asset.

Step 2: Detailed Signal Analysis

Get full indicator breakdown for a specific symbol:

bash
python {baseDir}/scripts/scanner.py --symbols BTC-USD --detail

Shows each indicator's contribution:

  • Individual signal (BUY/SELL/NEUTRAL)
  • Indicator value
  • Reasoning (e.g., "RSI oversold at 28.5")
Step 3: Filter and Rank Signals

Find the best opportunities:

bash
# Only buy signals with 70%+ confidence
python {baseDir}/scripts/scanner.py --filter buy --min-confidence 70 --rank confidence

# Rank by most bullish
python {baseDir}/scripts/scanner.py --rank bullish

# Save results to JSON
python {baseDir}/scripts/scanner.py --output signals.json
Step 4: Use Custom Watchlists

Available predefined watchlists:

bash
python {baseDir}/scripts/scanner.py --list-watchlists
python {baseDir}/scripts/scanner.py --watchlist crypto_defi

Watchlists: crypto_top10, crypto_defi, crypto_layer2, stocks_tech, etfs_major

Output

Signal Summary Table
================================================================================
  SIGNAL SCANNER RESULTS
================================================================================

  Symbol       Signal         Confidence          Price    Stop Loss
--------------------------------------------------------------------------------
  BTC-USD      STRONG_BUY          78.5%     $67,234.00  $64,890.00
  ETH-USD      BUY                 62.3%      $3,456.00   $3,312.00
  SOL-USD      NEUTRAL             45.0%        $142.50         N/A
--------------------------------------------------------------------------------

  Summary: 2 Buy | 1 Neutral | 0 Sell
  Scanned: 3 assets | [timestamp]
================================================================================
Detailed Signal Output
======================================================================
  BTC-USD - STRONG_BUY
  Confidence: 78.5% | Price: $67,234.00
======================================================================

  Risk Management:
    Stop Loss:   $64,890.00
    Take Profit: $71,922.00
    Risk/Reward: 1:2.0

  Signal Components:
----------------------------------------------------------------------
    RSI              | STRONG_BUY   | Oversold at 28.5 (< 30)
    MACD             | BUY          | MACD above signal, positive momentum
    Bollinger Bands  | BUY          | Price near lower band (%B = 0.15)
    Trend            | BUY          | Uptrend: price above key MAs
    Volume           | STRONG_BUY   | High volume (2.3x) on up move
    Stochastic       | STRONG_BUY   | Oversold (%K=18.2, %D=21.5)
    ADX              | BUY          | Strong uptrend (ADX=32.1)
----------------------------------------------------------------------
Signal Types
SignalScoreMeaning
STRONG_BUY+2Multiple strong buy signals aligned
BUY+1Moderate buy signals
NEUTRAL0No clear direction
SELL-1Moderate sell signals
STRONG_SELL-2Multiple strong sell signals aligned
Confidence Interpretation
ConfidenceInterpretation
70-100%High conviction, strong signal
50-70%Moderate conviction
30-50%Weak signal, mixed indicators
0-30%No clear direction, avoid trading

Configuration

Edit {baseDir}/config/settings.yaml:

yaml
indicators:
  rsi:
    period: 14
    overbought: 70
    oversold: 30

signals:
  weights:
    rsi: 1.0
    macd: 1.0
    bollinger: 1.0
    trend: 1.0
    volume: 0.5

Error Handling

See {baseDir}/references/errors.md for common issues:

  • API rate limits
  • Insufficient data handling
  • Network errors

Examples

See {baseDir}/references/examples.md for detailed examples:

  • Multi-timeframe analysis
  • Custom indicator parameters
  • Combining with backtester
  • Automated scanning schedules

Integration with Backtester

Test signals historically:

bash
# Generate signal
python {baseDir}/scripts/scanner.py --symbols BTC-USD --detail

# Backtest the strategy that generated the signal
python {baseDir}/../trading-strategy-backtester/skills/backtesting-trading-strategies/scripts/backtest.py \
  --strategy rsi_reversal --symbol BTC-USD --period 1y

Files

FilePurpose
scripts/scanner.pyMain signal scanner
scripts/signals.pySignal generation logic
scripts/indicators.pyTechnical indicator calculations
config/settings.yamlConfiguration

Resources

  • yfinance for price data
  • pandas/numpy for calculations
  • Compatible with trading-strategy-backtester plugin

© aAAaqwq, MIT. 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 7 other files (scripts, references) in skills/crypto-signal-generator of aAAaqwq/AGI-Super-Team.

  • SKILL.md
  • config/settings.yaml
  • references/errors.md
  • references/examples.md
  • references/implementation.md
  • scripts/indicators.py
  • scripts/scanner.py
  • scripts/signals.py

Open the folder on GitHubat commit 7cefd81

Compare with similar skills

Generating Trading Signals 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.

Generating Trading Signals compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Generating Trading Signals this skillaAAaqwq/AGI-Super-Team105—~1.6kAutomated safety check: PassMIT
Tushare Datazillionare/zillionare3222 repos~2.3kAutomated safety check: PassNone
Tradingview MCPatilaahmettaner/tradingview-mcp5k—~1.3kAutomated safety check: PassMIT
Digital Oraclekomako-workshop/digital-oracle878—~5.9kAutomated safety check: PassMIT
Polyclawchainstacklabs/polyclaw3591 repos~2kAutomated safety check: PassApache-2.0
Markdownfacioquo/stock-indicators-dotnet1.2k—~812Automated safety check: PassApache-2.0

Similar skills

  • 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。

    322 GitHub starsUsed in 2 repos~2.3k tokens
    Business, Finance & HRAuto-check passed
  • Tradingview MCP

    atilaahmettaner/tradingview-mcp

    AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, licensed news sentiment (Marketaux), and…

    5k GitHub stars~1.3k tokensUpdated yesterday
    Business, Finance & HRAuto-check passed
  • Digital Oracle

    komako-workshop/digital-oracle

    Answer prediction questions using market trading data, not opinions.

    878 GitHub stars~5.9k tokensUpdated 2 mo ago
    Business, Finance & HRAuto-check passed
  • Polyclaw

    chainstacklabs/polyclaw

    Trade on Polymarket via split + CLOB execution. An agent skill from chainstacklabs/polyclaw.

    359 GitHub starsUsed in 1 repo~2k tokens
    Business, Finance & HRAuto-check passed
  • Markdown

    facioquo/stock-indicators-dotnet

    Format and lint Markdown in this repository against GitHub Flavored Markdown and its markdownlint-cli2 configuration — headers, lists, code fences, callouts (VitePress containers on docs-site pages…

    1.2k GitHub stars~812 tokensUpdated yesterday
    Business, Finance & HRAuto-check passed
  • Openmobius Skill

    MobiusQuant/OpenMobius-skill

    Provides multi-school trading Q&A, chart/OHLCV analysis, annotation, and fresh-market workflows covering ICT/SMC, ChanLun, Wyckoff, Price Action, Order Flow, VSA, and Elliott Wave.

    697 GitHub stars~7.2k tokensUpdated 1 mo ago
    Business, Finance & HRAuto-check passed

More from aAAaqwq/AGI-Super-Team

All 167 skills in this repo
  • Content Creator

    aAAaqwq/AGI-Super-Team

    Create SEO-optimized marketing content with consistent brand voice.

    105 GitHub starsUsed in 3 repos~1.9k tokens
    Auto-check passed
  • Financial Calculator

    aAAaqwq/AGI-Super-Team

    Advanced financial calculator with future value tables, present value, discount calculations, markup pricing, and compound interest.

    105 GitHub starsUsed in 1 repo~1.5k tokens
    Auto-check passed
  • Bankr Signals

    aAAaqwq/AGI-Super-Team

    Transaction-verified trading signals on Base blockchain. An agent skill from aAAaqwq/AGI-Super-Team.

    105 GitHub starsUsed in 2 repos~3.3k tokens
    Auto-check passed
  • Erc 8004

    aAAaqwq/AGI-Super-Team

    Register AI agents on Ethereum mainnet using ERC-8004 (Trustless Agents).

    105 GitHub starsUsed in 2 repos~1.2k tokens
    Auto-check passed
  • Frontend Design Ultimate

    aAAaqwq/AGI-Super-Team

    Create distinctive, production-grade static sites with React, Tailwind CSS, and shadcn/ui — no mockups needed.

    105 GitHub starsUsed in 2 repos~2.7k tokens
    Auto-check passed
  • Zsxq Smart Publish

    aAAaqwq/AGI-Super-Team

    Publish and manage content on 知识星球 (zsxq.com). An agent skill from aAAaqwq/AGI-Super-Team.

    105 GitHub stars~1.5k tokensUpdated 2 days ago
    Auto-check passed

Questions about Generating Trading Signals

What does Generating Trading Signals do?

Multi-indicator signal generation system that analyzes price action using 7 technical indicators and produces composite BUY/SELL signals with confiden Generate trading signals using technical…. Generating Trading Signals is an agent skill from aAAaqwq/AGI-Super-Team.).

When should I use Generating Trading Signals?

Generating Trading Signals fits situations like: analyzing assets for trading opportunities; checking technical indicators; with phrases like get trading signals; check indicators.

How do I install Generating Trading Signals in Claude Code?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill generating-trading-signals -a claude-code`. Or copy the skill folder (skills/crypto-signal-generator in aAAaqwq/AGI-Super-Team) into .claude/skills/generating-trading-signals in your project. Claude Code loads it when a task matches its description.

How do I install Generating Trading Signals in Codex?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill generating-trading-signals -a codex`. Or copy the skill folder (skills/crypto-signal-generator in aAAaqwq/AGI-Super-Team) into .agents/skills/generating-trading-signals in your project. Codex loads it when a task matches its description.

Can I use Generating Trading Signals 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 aAAaqwq/AGI-Super-Team --skill generating-trading-signals -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/generating-trading-signals, .gemini/skills/generating-trading-signals, .github/skills/generating-trading-signals and .opencode/skills/generating-trading-signals in your project.

What does Generating Trading Signals need to run?

Going by SKILL.md and its folder, Generating Trading Signals needs Python for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(python:*).

Does Generating Trading Signals access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Generating Trading Signals 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Generating Trading Signals use?

Generating Trading Signals is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Generating Trading Signals use?

About 1.6k tokens (SKILL.md is roughly 6.5k 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 4.3k tokens, read only when the agent opens those files.

What are the alternatives to Generating Trading Signals?

Skills that share tags, products or a category with Generating Trading Signals: Tushare Data (zillionare/zillionare, 322 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Digital Oracle (komako-workshop/digital-oracle, 878 stars) and Polyclaw (chainstacklabs/polyclaw, 359 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Generating Trading Signals?

aAAaqwq (a GitHub user) maintains it in aAAaqwq/AGI-Super-Team, which has 105 GitHub stars. The repository holds 167 skills in this directory. The repository was last updated on October 8, 2026.

Source: aAAaqwq/AGI-Super-Team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.