Orkas is a desktop application for commanding a team of AI agents through one chat, with a commander model assigning work to specialist agents in parallel or in sequence. People use it to coordinate research, writing, presentations, and software tasks while keeping files on their computer. The catalogue includes skills for extending the agents available to Orkas.
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 Orkas-AI/Orkas --skill quant-labgit clone --depth 1 https://github.com/Orkas-AI/OrkasWrote 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/orkas-ai/orkas/quant-lab)<a href="https://agentmods.dev/skills/orkas-ai/orkas/quant-lab"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/quant-lab/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/orkas-ai/orkas/quant-lab"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/quant-lab.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 | $0.00003 | $0.00558 |
| Opus 5.5 | $0.00001 | $0.00223 |
| Sonnet 5.5 | $0.00001 | $0.00112 |
| Haiku 4.5 | $0.00000 | $0.00056 |
Grade A, and why
quant-lab scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 24d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quant Lab
Read with market-data for strategy design, backtesting, or experiment review.
Experiment card
Before calculation, state universe, data/adjustment, signal, rebalance timing, execution price assumption, sizing, cash treatment, costs, benchmark, sample, and invalidation rule. A close-derived signal may affect only the next return.
The compact core supports sma_cross, momentum, and rsi_reversion as
transparent baselines:
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" market-data stock -- backtest --input history.json --strategy sma_cross --fast 20 --slow 60 --cost-bps 8 --slippage-bps 5 --include-series --output result.json
Use the baselines to test a claim, not to stretch the engine into an unsupported strategy. For a custom strategy, produce a precise experiment specification and use a user-authorized project framework when available.
Review gate
- Confirm point-in-time membership and fundamentals; disclose survivorship or restatement exposure when they cannot be reconstructed.
- Include transaction costs and a relevant passive benchmark. State whether dividends, borrow, market impact, taxes, and FX are absent.
- Prefer chronological train/validation/test or walk-forward evidence. Keep the final test untouched while tuning.
- Inspect sensitivity to neighboring parameters, later entry, higher cost, and subperiods. A fragile result is a finding, not something to optimize away.
- Report CAGR/return, volatility, Sharpe convention, max drawdown, turnover, exposure, trade count, and the exact signal-delay guard.
Never describe a backtest as expected future performance. Preserve the core's
result JSON; put supplementary interpretation in a separate dossier or file.
--include-series supplies signal date, applied position, turnover and gross,
cost and net returns from the same calculation as the summary. Use that ledger
for trade explanations rather than rebuilding it with another timing model.
--cutoff YYYY-MM-DD selects a requested historical price window in memory;
the result records exclusions without changing the input.
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.
- 24d ago First seen · 49 lines · 3 tokens per session scan A bd9d4b813bc8
quant-lab is a skill published in the GitHub repository Orkas-AI/Orkas (2,192 stars, last pushed today), licensed MIT. It adds 3 tokens to every session and 558 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-23.
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