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 elonmaskhair-prog/dsh-plugin-diepi --skill diepi-quant-researchgit clone --depth 1 https://github.com/elonmaskhair-prog/dsh-plugin-diepiWrote 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/elonmaskhair-prog/dsh-plugin-diepi/diepi-quant-research)<a href="https://agentmods.dev/skills/elonmaskhair-prog/dsh-plugin-diepi/diepi-quant-research"><img src="https://agentmods.dev/badge/skills/elonmaskhair-prog/dsh-plugin-diepi/diepi-quant-research/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/elonmaskhair-prog/dsh-plugin-diepi/diepi-quant-research"><img src="https://agentmods.dev/badge/skills/elonmaskhair-prog/dsh-plugin-diepi/diepi-quant-research.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 | $0.00059 | $0.01520 |
| Opus 5 | $0.00030 | $0.00760 |
| Sonnet 5 | $0.00012 | $0.00304 |
| Haiku 4.5 | $0.00006 | $0.00152 |
Grade A, and why
diepi-quant-research 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.
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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
diePi quantitative research
Use this workflow when the user wants to express a stock or ETF strategy in natural language and backtest it with diePi.
Hard boundary
- This integration is for research and backtesting only. It has no live-order tool.
- Never pass or execute arbitrary Python through diePi tools. Any optional external Skill runs separately under the host's own tool and permission policy.
- StrategySpec v1 currently supports one daily, long-only
ma_crossoverstrategy with an optional amount-expansion entry filter. - Do not claim support for futures, leverage, short selling, intraday or minute data, portfolio optimization, or a built-in data connector.
- Treat Tushare acquisition as an optional handoff to the independently installed official Tushare Skill. Never request, receive, echo, or log a Tushare token in chat or in diePi tool arguments.
Workflow
- Call
mcp__diepi__capabilitiesand select an opaquedataset_id. Read itsdata_contractandexecution_model; these are part of the strategy interpretation, not optional boilerplate. If no configured dataset can cover the requested daily instrument and interval, do not invent an ID: read the Tushare handoff and follow its missing-data branch. - Translate the user's words into
StrategySpec v1. Do not invent a ticker, date interval, or economically material trading assumption. Ask if one is missing. - Call
mcp__diepi__preview_strategy. Show the strategy card when its exact entry, exit, position, or information boundary could surprise the user. - Call
mcp__diepi__validate_datafor the exact symbol, interval, and price mode. If the configured data is ready, skip all acquisition. If required daily data is missing, read the Tushare handoff and follow it. Validate the staged and host-registeredmarket_data_v1dataset again. Treat validation as a hard gate: never start a backtest after a failed or incomplete validation. A warning is evidence to disclose, not text to silently discard. - Generate and record one stable
submission_idfor this exact execution request. Usereq_followed by 8 to 64 ASCII letters, digits,_, or-; a fresh 32-character hexadecimal nonce is a good suffix. Never reuse the ID for different dataset, strategy, backtest, or execution-assumption values. - Call
mcp__diepi__start_backtestwith thatsubmission_id. It validates again and returns quickly with ajob_id; it never waits for the whole run. If the call times out or its transport fails, retry the exact same arguments with the samesubmission_id. Never generate a replacement ID for a retry. - Poll
mcp__diepi__job_statusat a reasonable cadence. Usemcp__diepi__cancel_jobwhen the user asks to stop. - Call
mcp__diepi__get_result. Treat a run as comparable only when all are true:artifact_verified == true,adapter_attribution_verified == true,result_committed == true,result_status == SUCCESS, andrankable == true.
What ships with it
1 file 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.
- 11d ago First seen · 119 lines · 59 tokens per session scan A b686c7ded20a
diepi-quant-research is a skill published in the GitHub repository elonmaskhair-prog/dsh-plugin-diepi (2 stars, last pushed 9d ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,520 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 2026-08-31.
Other skills, from other repositories
tushare
A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.
correlation-analysis
Correlation and cointegration analysis — co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
ashare-pre-st-filter
An A-share China stock risk checker that forecasts whether a company may receive an ST or *ST warning in the next financial year. ST labels are Chinese exchange warnings for companies facing specified financial or regulatory problems.
credit-analysis
A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
etf-analysis
A framework for comparing exchange-traded funds (ETFs), which are funds bought and sold on a stock exchange and usually track an index, industry, asset, or strategy. It covers fees, how closely an ETF follows its target, trading activity, and portfolio use.