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 agentmods add skills/camusgit/evoquant/quant-experiment-runtimenpx skills add CamusGIT/EvoQuant --skill quant-experiment-runtimegit clone --depth 1 https://github.com/CamusGIT/EvoQuantWhat 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 | $0.00179 | $0.02321 |
| Opus 5 | $0.00089 | $0.01161 |
| Sonnet 5 | $0.00036 | $0.00464 |
| Haiku 4.5 | $0.00018 | $0.00232 |
Grade A, and why
quant-experiment-runtime 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 2d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quant Research Experiment Runtime
An Experiment Executor for quant auto-research: take a Research Artifact
(LLM-generated, exposing a callable entry point), run it against a real
offline source database to compute research-object values, and evaluate
quantitative metrics. It does not design experiments (that is
experiment-pipeline) — it executes one.
Mental model: Runtime = Experiment Executor
Workflow (experiment-pipeline) ── owns when/whether to run
│
▼
Experiment Runtime ── owns how to run one experiment
│
▼
Research Artifact ── a runnable research product (py file / package / future workspace|docker|notebook)
│
▼
Entry Point ── Python-native callable, e.g. "path/to/code.py::run" or "pkg.mod:run"
│
▼
Results ── Runtime does NOT interpret; Metric does
│
▼
Metric (registry, extensible) ── evaluates; does NOT realign
│
▼
ExperimentResult (+ artifacts/) ── Reflection / downstream Workflow depend only on this
The Runtime only knows "I run a Research Artifact via its Entry Point." It is
agnostic to: research-object type (factor / generation method / portfolio),
expression form (DSL / python / generator), and the internal structure of
results. Those belong to the Research Artifact / Workflow / Metric.
This skill is self-contained: panel building (scripts/_panel.py) and IC
metrics (scripts/_metrics.py) are implemented inside the skill and need only
pandas / pyarrow / numpy. There is no dependency on any external
factor-research project.
When to use
- You need to actually run the proposed object on real data.
- Experiment needs concrete IC-style metrics.
- You need to evaluate a batch of candidates.
When NOT to use
- Designing which experiments to run / stage budgets →
experiment-pipeline. - Debugging a single failed experiment →
experiment-craft. - Searching/reading papers →
local-paper-navigator.
What ships with it
15 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/candidate-template.json 558 B
- assets/discover-output-example.json 1.2 KB
- assets/experiment-result-schema.json 1.6 KB
- assets/research-artifact-example/factor.py 3.0 KB runs code
- assets/split-config.example.json 422 B
- references/metrics-extension.md 2.5 KB
- references/research-code-convention.md 3.8 KB
- references/runtime-interface.md 4.5 KB
- references/split-policy.md 2.6 KB
- scripts/_metrics.py 17 KB runs code
- scripts/_panel.py 9.1 KB runs code
- scripts/build_panel.py 2.7 KB runs code
- scripts/discover_data.py 5.5 KB runs code
- scripts/experiment_runtime.py 19 KB runs code
- scripts/run_experiment.py 3.7 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.
- 2d ago First seen · 190 lines · 179 tokens per session scan A 46689fb4b583
quant-experiment-runtime is a skill published in the GitHub repository CamusGIT/EvoQuant (215 stars, last pushed 15d ago), licensed Apache-2.0. It adds 179 tokens to every session and 2,321 once invoked, about $0.0009 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.
Other skills, from other repositories
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.
ashare-pre-st-filter
A 股 ST/ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。.
credit-analysis
固收与信用分析：信用债评级、利差分析、违约风险评估、城投债研究、可转债定价与策略。.
etf-analysis
ETF分析：产品筛选、费率对比、跟踪误差、流动性评估、策略应用与中国市场ETF量化配置框架。.
geopolitical-risk
Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.
vibe-trading
Professional finance research toolkit — backtesting (10 engines + benchmark comparison panel), factor analysis, Alpha Zoo (462 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract →…