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 QuantJourneyOrg/quantjourney-bt --skill qj-report-analystgit clone --depth 1 https://github.com/QuantJourneyOrg/quantjourney-btWrote 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/quantjourneyorg/quantjourney-bt/qj-report-analyst)<a href="https://agentmods.dev/skills/quantjourneyorg/quantjourney-bt/qj-report-analyst"><img src="https://agentmods.dev/badge/skills/quantjourneyorg/quantjourney-bt/qj-report-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/quantjourneyorg/quantjourney-bt/qj-report-analyst"><img src="https://agentmods.dev/badge/skills/quantjourneyorg/quantjourney-bt/qj-report-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 | $0.00000 | $0.00533 |
| Opus 5 | $0.00000 | $0.00267 |
| Sonnet 5 | $0.00000 | $0.00107 |
| Haiku 4.5 | $0.00000 | $0.00053 |
Grade A, and why
qj-report-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 10d 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QuantJourney Report Analyst
Use this skill to read a QuantJourney backtest report — the metrics, the plots, and the trade blotter — and judge whether the result is trustworthy.
Read the headline metrics first
- CAGR — annualized return. Context, not verdict.
- Sharpe / Sortino — risk-adjusted return; Sortino only penalizes downside. A Sharpe > 2 on a simple strategy is a red flag to investigate, not celebrate.
- Max drawdown / Calmar — worst peak-to-trough and return-per-unit-drawdown. Ask: could you hold through that drawdown?
- VaR / CVaR — tail loss at a confidence level (loss-positive convention).
Read the plots
- Cumulative returns (± regime overlay) — the shape. Is the edge steady or one lucky period? Is it just long-beta?
- Drawdown / underwater — depth and time to recover. Long underwater periods break real allocators.
- Monthly returns heatmap — consistency vs a few dominating months.
- Rolling Sharpe / volatility / beta — when it worked. A rolling Sharpe that collapses in recent years is a warning.
- Crisis analysis — per-crisis return, vol, drawdown, beta (GFC, COVID, 2022). How does it behave when the market breaks?
- Blotter — trade PnL distribution (a few big winners vs broad edge), holding-period distribution, and transaction-cost analysis (did costs eat it?).
Read the walk-forward diagnostics
- OOS equity vs in-sample — does the edge survive out of sample?
- Sharpe decay IS→OOS — a large drop means fit to noise.
- Overfit ratio / efficiency traffic lights — heed the red verdicts.
- A single good out-of-sample result is not proof of robustness.
Red flags to call out
- Edge concentrated in one regime or a handful of trades.
- Sharpe that vanishes once realistic costs are added.
- High turnover with thin per-trade PnL.
- Big in-sample / out-of-sample Sharpe decay.
- Market-neutral book that is actually net long (check exposure).
- Short strategy whose return is mostly the un-modeled borrow carry.
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.
- 10d ago First seen · 47 lines · 0 tokens per session scan A dbb187465423
qj-report-analyst is a skill published in the GitHub repository QuantJourneyOrg/quantjourney-bt (53 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 533 tokens. 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.
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