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 sector-analystgit 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/sector-analyst)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/sector-analyst"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/sector-analyst/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/sector-analyst"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/sector-analyst.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 | $0.00084 | $0.01819 |
| Opus 5.5 | $0.00034 | $0.00728 |
| Sonnet 5.5 | $0.00017 | $0.00364 |
| Haiku 4.5 | $0.00008 | $0.00182 |
Grade A, and why
sector-analyst 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.
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sector Analyst
Overview
This skill enables comprehensive analysis of sector rotation and market cycle positioning by fetching uptrend ratio data from TraderMonty's public CSV dataset. It ranks sectors, calculates cyclical vs defensive risk regime scores, identifies overbought/oversold conditions, and estimates the current market cycle phase. Chart images can optionally supplement the data-driven analysis with industry-level detail.
When to Use This Skill
Use this skill when:
- User requests sector rotation analysis (no chart images required)
- User asks about cyclical vs defensive positioning
- User wants to know which sectors are overbought or oversold
- User requests market cycle phase estimation
- User provides sector performance charts for supplementary analysis
- User asks for sector-based scenario analysis or predictions
Example user requests:
- "Run a sector rotation analysis"
- "Which sectors are leading — cyclical or defensive?"
- "Are any sectors overbought right now?"
- "What phase of the market cycle are we in?"
- "Analyze these sector performance charts and tell me where we are in the market cycle"
Prerequisites
- Python 3.8+ with
requestslibrary (for CSV fetching) - No API keys required — data is fetched from a public GitHub repository
- Optional: Sector performance chart images for supplementary analysis
Data Source
Sector uptrend ratios are fetched from TraderMonty's public GitHub repository (no API key required):
- Sector Summary:
sector_summary.csv— uptrend ratio, trend, slope, and status per sector - Freshness Check:
uptrend_ratio_timeseries.csv— max(date) used to verify data recency
Running the Script
# Default: fetch CSV, print human-readable analysis
python3 scripts/analyze_sector_rotation.py
# JSON output
python3 scripts/analyze_sector_rotation.py --json
# Save to file
python3 scripts/analyze_sector_rotation.py --save --output-dir reports/
Analysis Workflow
Follow this structured workflow:
What ships with it
9 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/industory_performance_1.jpeg 456 KB
- assets/industory_performance_2.jpeg 776 KB
- assets/sector_performance.jpeg 356 KB
- README.md 8.8 KB
- references/sector_rotation.md 6.4 KB
- scripts/analyze_sector_rotation.py 25 KB runs code
- scripts/tests/conftest.py 358 B runs code
- scripts/tests/helpers.py 2.5 KB runs code
- scripts/tests/test_analyze_sector_rotation.py 19 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 · 235 lines · 84 tokens per session scan A ba929e65c161
sector-analyst is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 84 tokens to every session and 1,819 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-14.
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