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.
npx skills add mphinance/alpha-skills --skill theme-detectorgit clone --depth 1 https://github.com/mphinance/alpha-skillsWrote 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.
[](https://agentmods.dev/skills/mphinance/alpha-skills/theme-detector)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/theme-detector"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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.
<a href="https://agentmods.dev/skills/mphinance/alpha-skills/theme-detector"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/theme-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.03443 |
| Opus 5.5 | $0.00020 | $0.01377 |
| Sonnet 5.5 | $0.00010 | $0.00689 |
| Haiku 4.5 | $0.00005 | $0.00344 |
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 1mo ago.
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.
This is a copy
92% identical to theme-detector — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 416 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:
- Theme Heat (0-100): Direction-neutral strength of the theme (momentum, volume, uptrend ratio, breadth)
- Lifecycle Maturity: Stage classification (Emerging / Accelerating / Trending / Mature / Exhausting) based on duration, extremity clustering, valuation, and ETF proliferation
- 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
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.
- assets/report_template.md 4.5 KB
- README.md 14 KB
- references/cross_sector_themes.md 11 KB
- references/finviz_industry_codes.md 17 KB
- references/thematic_etf_catalog.md 12 KB
- references/theme_detection_methodology.md 13 KB
- scripts/calculators/__init__.py 37 B runs code
- scripts/calculators/heat_calculator.py 4.4 KB runs code
- scripts/calculators/industry_ranker.py 2.5 KB runs code
- scripts/calculators/lifecycle_calculator.py 5.1 KB runs code
- scripts/calculators/theme_classifier.py 12 KB runs code
- scripts/calculators/theme_discoverer.py 9.4 KB runs code
- scripts/config_loader.py 4.3 KB runs code
- scripts/default_theme_config.py 10 KB runs code
- scripts/etf_scanner.py 25 KB runs code
- scripts/finviz_performance_client.py 6.7 KB runs code
- scripts/report_generator.py 22 KB runs code
- scripts/representative_stock_selector.py 22 KB runs code
- scripts/scorer.py 2.6 KB runs code
- scripts/tests/conftest.py 298 B runs code
- scripts/tests/README.md 653 B
- scripts/tests/test_config_loader.py 7.7 KB runs code
- scripts/tests/test_detect_divergence.py 3.6 KB runs code
- scripts/tests/test_etf_scanner.py 31 KB runs code
- scripts/tests/test_finviz_performance_client.py 5.2 KB runs code
- scripts/tests/test_heat_calculator.py 9.2 KB runs code
- scripts/tests/test_industry_ranker.py 9.8 KB runs code
- scripts/tests/test_lifecycle_calculator.py 12 KB runs code
- scripts/tests/test_report_generator.py 24 KB runs code
- scripts/tests/test_representative_stock_selector.py 40 KB runs code
- scripts/tests/test_scorer.py 7.2 KB runs code
- scripts/tests/test_theme_classifier.py 25 KB runs code
- scripts/tests/test_theme_detector_e2e.py 19 KB runs code
- scripts/tests/test_theme_discoverer.py 19 KB runs code
- scripts/tests/test_uptrend_client.py 3.5 KB runs code
- scripts/theme_detector.py 34 KB runs code
- scripts/themes.yaml 5.1 KB
- scripts/uptrend_client.py 7.5 KB runs code
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.
- 1mo ago First seen · 416 lines · 51 tokens per session scan A b0ecfc299faa
theme-detector is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 51 tokens to every session and 3,443 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to theme-detector, differing in 15 lines, and is treated as a copy.
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