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 run-episodegit 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/run-episode)<a href="https://agentmods.dev/skills/austin-starks/public-portfolio-challenge/run-episode"><img src="https://agentmods.dev/badge/skills/austin-starks/public-portfolio-challenge/run-episode/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/run-episode"><img src="https://agentmods.dev/badge/skills/austin-starks/public-portfolio-challenge/run-episode.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 | $0.00100 | $0.01262 |
| Opus 5 | $0.00050 | $0.00631 |
| Sonnet 5 | $0.00020 | $0.00252 |
| Haiku 4.5 | $0.00010 | $0.00126 |
Grade A, and why
run-episode 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Episode
The orchestrator entry point for the Public Portfolio Challenge. Every episode is a specific job over specific real artifacts, but the discipline is the same and lives in the functional skills. This skill picks the right runbook, pins its artifacts, and runs the stages in order, delegating each to the skill that owns it. It does not re-implement the discipline — it sequences it.
Invocation
/run-episode <episode>[ <attempt>]
Examples: /run-episode 10 · /run-episode 10 addendum · "run the episode-10 bakeoff".
First: load the actual runbook (it is the source of truth for THIS run)
Read the target runbook and treat it as authoritative for the job + artifacts (this skill only encodes the ordering and delegation):
| Episode | Runbook | Shape |
|---|---|---|
| 10 | episode-10/BAKEOFF_RUNBOOK.md |
Multi-family bakeoff (search→certify→lockbox→deploy) |
| 10 / addendum | episode-10/addendum/RUNBOOK.md |
Redesign entries + exits, prove sell behavior and retain the incumbent edge |
From the runbook, pin before doing anything: the SUBJECT (live book / build) IDs, the incumbent bar (cert study id + per-fold numbers), the fixed universe + capital, and any carried-over artifacts. The runbook's own Stage list wins if it disagrees with the generic ordering below.
The bookend rules (fixed for every episode)
- Stage A first — confirm the SUBJECT by FIELD (
get_portfolio→conditionFieldAudit), re-verify the incumbent bar, spot-check one fresh backtest, and (if live) check pending orders +automaticOrderApproval. Deliver a one-paragraph before state. - Deploy last, and GATED — no clone, no reconcile, no orders until the human says the episode's explicit go phrase ("deploy + clean up"). See deploy-gate.
- Bugs are a deliverable at every stage — bug-protocol on any failure; quarantine tainted results.
- Verify, don't assert — every engine-behavior claim checked against events/fields/repro (via portfolio-certification's first principles) before it lands in the log.
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 · 92 lines · 100 tokens per session scan A 97d3dbe888a9
run-episode is a skill published in the GitHub repository austin-starks/Public-Portfolio-Challenge (44 stars, last pushed 4d ago), licensed MIT. It adds 100 tokens to every session and 1,262 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.
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.
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.
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.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
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 →…