xalpha is a Python toolkit for managing and analyzing fund investments, including market-data retrieval, portfolio records, visualization, and strategy backtesting. It is for investors and quantitative researchers who track recurring or grid-based investments across financial markets, including through supported quantitative platforms. The catalogue add-ons provide agent workflows for using xalpha.
Borrowing it
Nothing to install: this file belongs to refraction-ray/xalpha. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/refraction-ray/xalpha/master/.agents/skills/question2report/SKILL.mdgit clone --depth 1 https://github.com/refraction-ray/xalphaWrote 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/refraction-ray/xalpha/question2report)<a href="https://agentmods.dev/skills/refraction-ray/xalpha/question2report"><img src="https://agentmods.dev/badge/skills/refraction-ray/xalpha/question2report/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/refraction-ray/xalpha/question2report"><img src="https://agentmods.dev/badge/skills/refraction-ray/xalpha/question2report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00075 | $0.03002 |
| Opus 5.5 | $0.00030 | $0.01201 |
| Sonnet 5.5 | $0.00015 | $0.00600 |
| Haiku 4.5 | $0.00007 | $0.00300 |
Grade A, and why
question2report 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 2mo 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Question → Report Skill
Transform a user's free-form financial question into a production-quality, self-contained HTML report with embedded charts and tables.
Pipeline
User Question
│
▼
┌──────────────────┐
│ 1. ANALYZE │ Parse intent, identify assets, metrics, time range
└────────┬─────────┘
│
▼
┌──────────────────┐
│ 2. CONFIRM │ Present analysis plan to user; agree on scope
└────────┬─────────┘
│
▼
┌──────────────────┐
│ 3. DISCOVER API │ Explore the xalpha codebase to find suitable APIs
└────────┬─────────┘
│
▼
┌──────────────────┐
│ 4. FETCH & CLEAN │ Write & run a Python script; handle errors & NaN
└────────┬─────────┘
│
▼
┌──────────────────┐
│ 5. ANALYZE DATA │ Compute metrics appropriate to the question
└────────┬─────────┘
│
▼
┌──────────────────┐
│ 6. GENERATE HTML │ Build a beautiful, self-contained HTML report
└──────────────────┘
Step 1 — Analyze the Question
Parse the user's natural-language question and extract:
- Subject: What assets, funds, indices, or strategies are being discussed?
- Comparison / benchmark: Is there a reference to compare against?
- Time range: Explicit dates, or implied ("last 3 years", "since inception"). Default to the most recent 3 full calendar years if unspecified.
- Desired output: What kind of insights does the user want? (rankings, trend comparison, risk analysis, prediction accuracy, etc.)
If fund codes or asset identifiers are not given, research them via web search or by exploring the xalpha codebase for relevant list/search APIs.
Step 2 — Confirm Scope
Before any data work, present a concise plan to the user:
📋 Analysis Plan
─────────────────────────────────
Subject : <what is being analyzed>
Assets : <list of codes / tickers>
Period : <start> → <end>
Analysis : <what metrics / comparisons will be computed>
Charts : <what visualizations will be included>
─────────────────────────────────
Shall I proceed, or would you like to adjust?
What ships with it
2 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.
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.
- 2mo ago First seen · 248 lines · 75 tokens per session scan A 8644d4e6ed65
question2report is a skill published in the GitHub repository refraction-ray/xalpha (2,746 stars, last pushed 3mo ago), licensed MIT. It adds 75 tokens to every session and 3,002 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 2026-08-30.
question2report compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| question2reportrefraction-ray/xalpha | Skill | 2.7k | 3mo ago | MIT | A | 75 | Claude Code |
| dcf-modelginlix-ai/LangAlpha | Skill | 2.2k | today | Apache-2.0 | A | 56 | Claude Code, Codex |
| comps-analysisginlix-ai/LangAlpha | Skill | 2.2k | today | Apache-2.0 | A | 48 | Claude Code, Codex |
| company-profileginlix-ai/LangAlpha | Skill | 2.2k | today | Apache-2.0 | A | 68 | Claude Code, Codex |
| check-modelginlix-ai/LangAlpha | Skill | 2.2k | today | Apache-2.0 | A | 72 | Claude Code, Codex |
| ui-designginlix-ai/LangAlpha | Skill | 2.2k | today | Apache-2.0 | A | 24 | Claude Code, Codex |
| check-deckginlix-ai/LangAlpha | Skill | 2.2k | today | Apache-2.0 | A | 63 | Claude Code, Codex |