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 sweep-reoptimizationgit 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/sweep-reoptimization)<a href="https://agentmods.dev/skills/austin-starks/public-portfolio-challenge/sweep-reoptimization"><img src="https://agentmods.dev/badge/skills/austin-starks/public-portfolio-challenge/sweep-reoptimization/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/sweep-reoptimization"><img src="https://agentmods.dev/badge/skills/austin-starks/public-portfolio-challenge/sweep-reoptimization.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 | $0.00109 | $0.01286 |
| Opus 5 | $0.00055 | $0.00643 |
| Sonnet 5 | $0.00022 | $0.00257 |
| Haiku 4.5 | $0.00011 | $0.00129 |
Grade A, and why
sweep-reoptimization 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sweep Re-Optimization & Provenance
The three standing methodology rules that exist because a deploy candidate was once certified on knobs inherited from a sweep on a different structure and presented as "optimal."
The three standing rules (binding on all future episodes)
- Re-optimize on ANY structural change — never inherit a genome across structures. Knobs (TP,
total budget, per-name size, rank window, VIX gate, entry cooldown, DTE bracket) are
structure-dependent. Whenever the structure changes — affordability rungs, strike depths,
universe membership (e.g. adding a name), DTE family, entry/exit shape, or adding an alt-data rank
signal — re-run the
engine_kind:"sweep"walk-forward on the NEW book before certifying. Abacktest_onlycert on inherited knobs only confirms the book generalizes; it does not establish the knobs are good for that structure. - Separate "explore designs" from "optimize the chosen design." Screen candidate designs → pick
one → then sweep that exact design's knobs → then
backtest_only-certify. If the screen picks design X, the sweep base must be X itself, not its neighbours. Never sweep the losers' variants while leaving the chosen book hand-tuned. - Label every deployed parameter's provenance in the deliverable —
swept-on-this-book/inherited-from <study-id>/hand-set. "Inherited" is amber, not green, until re-swept on the current structure.
Self-check trigger words: "carry over the knobs," "reuse the winner's params," "same config on the new ladder," "already certified, no need to re-sweep," "just raise the allocation." Each must prompt: did the structure change since those knobs were searched? If yes → re-sweep before certifying.
Running the sweep
get_sweep_surfaceon the chosen book (or a clean clone) FIRST to get the real sweepable field names. Re-sweep from a CLEAN seed — re-sweeping an already-swept book can stack/duplicate genes; field-audit for duplicated conditions.run_walk_forward_studywithengine_kind:"sweep",inner_mode:"optimize",certification:true,mode:"validation", anchored, 5 folds (4 acceptable), 2022→today,oos_width_days:252. See walk-forward-oos for the full param block. GA overfits and is rejected for deploy certification — sweep is the certified path.preview_only:truefirst to compile genes + cost-check.- Each sweep must be non-trivial: ≥3
gene_intentsaxes × ≥3 values each (e.g.BuyingPowerPct,TakeProfitPct,RollTriggerDte, rank depth, delta band, DTE). A 1-axis or 2-value sweep does not qualify.
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.
- 11d ago First seen · 82 lines · 109 tokens per session scan A ff0f33490f13
sweep-reoptimization is a skill published in the GitHub repository austin-starks/Public-Portfolio-Challenge (44 stars, last pushed 3d ago), licensed MIT. It adds 109 tokens to every session and 1,286 once invoked, about $0.0005 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
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.