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 austin-starks/Public-Portfolio-Challenge --skill lockbox-holdoutgit clone --depth 1 https://github.com/austin-starks/Public-Portfolio-ChallengeWrote 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/austin-starks/public-portfolio-challenge/lockbox-holdout)<a href="https://agentmods.dev/skills/austin-starks/public-portfolio-challenge/lockbox-holdout"><img src="https://agentmods.dev/badge/skills/austin-starks/public-portfolio-challenge/lockbox-holdout/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/austin-starks/public-portfolio-challenge/lockbox-holdout"><img src="https://agentmods.dev/badge/skills/austin-starks/public-portfolio-challenge/lockbox-holdout.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 | $0.00115 | $0.00975 |
| Opus 5 | $0.00057 | $0.00487 |
| Sonnet 5 | $0.00023 | $0.00195 |
| Haiku 4.5 | $0.00012 | $0.00097 |
Grade A, and why
lockbox-holdout 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 12d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Single-Touch Lockbox & Baselines
The lockbox is a final holdout that no fold, sweep, or search ever touches — the last line of defense against calendar-fit selection pressure. It is distinct from the walk-forward OOS folds (walk-forward-oos), which are used repeatedly during search; the lockbox is touched exactly once.
The lockbox
- What: the walk-forward span stops
lockbox_width_days = 126(~6 months) before the run date. That final window(run date − 126d) → run dateis the lockbox — held out from ALL folds, ALL sweeps, ALL search. - How: only the assembled deploy-shape book that already passed every gate on the walk-forward aggregate runs over the lockbox, exactly once, after design freeze (Stage S2).
- Pass conditions (frozen at S0, do not move them after seeing results):
- lockbox OOS return ≥ worst single-fold walk-forward OOS return;
- lockbox maxDD ≤ 55%;
- posture clean (
audit_backtest_posture, all four conditions); - breadth ≥ 9 eligible names (
audit_backtest_breadth— see breadth-audit).
Single-touch is absolute
If you look at the lockbox and then iterate the design, the lockbox is burned — move the walk-forward span back and hold out a fresh tail before any deploy. A lockbox failure is treated as calendar-fit selection pressure and means no deploy, except by a logged owner override that explicitly names the lockbox.
The three baselines (A/B/C) as OOS bars (Stage S1)
Loaded from committed snapshots/*.json via create_portfolio. Score A and B on the current engine;
copy C's frozen published numbers as the bar to beat.
- A — equity buy&hold (the full universe). A return bar only, never a Sortino bar — its Sortino reflects an equity-rally smoothness a leveraged long-premium book can't match.
- B — naive LEAP ladder (canonical spec: all names, no rank/filter, 14-day cadence, 8%-of-portfolio
per name under 95% budget, structure = first affordable rung of
[Δ0.50 call → Δ0.55/0.25 → Δ0.55/0.40 → Δ0.50/0.42 vertical], all 180–365 DTE, single exit at DTE ≤ 45). A Sortino bar only in folds with ≥ 9/20 breadth. - C — frozen incumbent bar with published per-fold OOS numbers, copied verbatim (do not re-run to "improve" them).
What ships with it
3 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.
- 12d ago First seen · 64 lines · 115 tokens per session scan A 9e9d91eee6e1
lockbox-holdout is a skill published in the GitHub repository austin-starks/Public-Portfolio-Challenge (44 stars, last pushed 4d ago), licensed MIT. It adds 115 tokens to every session and 975 once invoked, about $0.0006 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
strategy-generate
Create, modify, and optimize quantitative trading strategies, then backtest and evaluate them.
backtesting-sim
Backtesting and simulation: vectorized backtesting, paper trading simulation, strategy A/B testing, automated strategy building, natural language to strategy, and trading plan generation. USE FOR: backtest, backtesting, paper trading, simulation, strategy builder, A/B test strategies, natural language strategy…
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.