theme-detector

theme-detector is a skill for Claude Code, Codex from BaggaT236/AI-Trading-Skills. It costs 51 tokens per session (3,663 once invoked), scanned A, original, MIT.

A market-theme analysis tool that looks for broad trends across investment sectors. It combines price movement, trading activity, and how widely a trend appears to judge its strength and maturity.

In plain words
What is it for?
Finding sectors with shared momentum, ranking bullish and bearish themes, assessing whether a theme is emerging or exhausted, and reviewing supporting breadth, volume, and ETF signals.
Why use it?
A rising sector may reflect a short-lived move or a broader market theme. This tool helps distinguish emerging, established, crowded, and weakening themes while considering both upward and downward trends.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Finding sectors with shared momentum, ranking bullish and bearish themes, assessing whether a theme is emerging or exhausted, and reviewing supporting breadth, volume, and ETF signals.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/baggat236/ai-trading-skills/theme-detector
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add BaggaT236/AI-Trading-Skills --skill theme-detector
Clone the repo
git clone --depth 1 https://github.com/BaggaT236/AI-Trading-Skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for theme-detector

README.md
[![agentmods](https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/theme-detector/github.svg)](https://agentmods.dev/skills/baggat236/ai-trading-skills/theme-detector)
Your own site
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/theme-detector"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/theme-detector/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for theme-detector

Your own site · 80×15
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/theme-detector"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/theme-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,663 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.03663
Opus 5 $0.00026 $0.01832
Sonnet 5 $0.00010 $0.00733
Haiku 4.5 $0.00005 $0.00366

Measured 11d ago against content hash 7f6b2b316702, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

theme-detector scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 31 executable files (scripts/calculators/__init__.py, scripts/calculators/heat_calculator.py, scripts/calculators/industry_ranker.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/theme-detector/SKILL.md · 429 lines

How it starts

The opening of the file, as written. The whole thing — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Theme Detector

Overview

This skill detects and ranks trending market themes by analyzing cross-sector momentum, volume, and breadth signals. It identifies both bullish (upward momentum) and bearish (downward pressure) themes, assesses lifecycle maturity (Emerging/Accelerating/Trending/Mature/Exhausting), and provides a confidence score combining quantitative data with narrative analysis.

3-Dimensional Scoring Model:

  1. Theme Heat (0-100): Direction-neutral strength of the theme (momentum, volume, uptrend ratio, breadth)
  2. Lifecycle Maturity: Stage classification (Emerging / Accelerating / Trending / Mature / Exhausting) based on duration, extremity clustering, valuation, and ETF proliferation
  3. Confidence (Low / Medium / High): Reliability of the detection, combining quantitative breadth with narrative confirmation. Script output is capped at Medium; Claude's WebSearch narrative confirmation step can elevate to High.

Key Features:

  • Cross-sector theme detection using FINVIZ industry data
  • Direction-aware scoring (bullish and bearish themes)
  • Lifecycle maturity assessment to identify crowded vs. emerging trades
  • ETF proliferation scoring (more ETFs = more mature/crowded theme)
  • Integration with uptrend-dashboard for 3-point evaluation
  • Dual-mode operation: FINVIZ Elite (fast) or public scraping (slower, limited)
  • WebSearch-based narrative confirmation for top themes

When to Use This Skill

Explicit Triggers:

  • "What market themes are trending right now?"
  • "Which sectors are hot/cold?"
  • "Detect current market themes"
  • "What are the strongest bullish/bearish narratives?"
  • "Is AI/clean energy/defense still a strong theme?"
  • "Where is sector rotation heading?"
  • "Show me thematic investing opportunities"

Implicit Triggers:

  • User wants to understand broad market narrative shifts
  • User is looking for thematic ETF or sector allocation ideas
  • User asks about crowded trades or late-cycle themes
  • User wants to know which themes are emerging vs. exhausted

Read the full file on GitHub · 429 lines

Files

What ships with it

38 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 429 lines · 51 tokens per session scan A 7f6b2b316702

Subscribe to this mod's changes

theme-detector is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 7d ago), licensed MIT. It adds 51 tokens to every session and 3,663 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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