FEDERATED - the claims inside are not verified by this repo. Nine skills that wrap the `neural-trader` npm engine inside a 71k-star agent harness: walk-forward backtests, Monte-Carlo and parameter sweeps run on the vendor's cloud, LSTM/Transformer/N-BEATS training, PageRank feature attribution, mean-variance optimisation by conjugate gradient, regime detection and VaR/CVaR. These are CLI wrappers, not methodology - the engine is a separate install, cloud jobs need an API key and receipts are signed with an Ed25519 key. Nothing executes market orders. Installs disabled.
1 on GitHubby ruvnetv0.3.6MITupdated todayfederated
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Plugin ruflo-neural-trader from marketplace fin-skills in howard-lynn-ye/Fin-RSI. Commands read from the repo's manifest — today.
What it installs
Skills (9)
trader-backtest
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation; Ed25519-sign the result for paper→live tamper evidence (ADR-126 Phase 4)
trader-cloud-backtest
Run a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally
trader-explain
Regulator-grade feature attribution for any LSTM/Transformer signal — single-entry PageRank ranks the top-K features that drove the prediction (ADR-126 Phase 6, ADR-123 single-entry PR)
trader-portfolio-cg
Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
trader-portfolio
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
trader-regime
Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy. Use when the user asks about market conditions, wants to pick a strategy for current conditions, or before running a backtest/signal that should be regime-aware.
trader-risk
Assess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status
trader-signal
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
trader-train
Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
Backtesting specialist using npx neural-trader Rust/NAPI engine — walk-forward validation, Monte Carlo simulation, parameter optimization. Orthogonal research lane (ADR-126 Phase 5) — produces signed promotion candidates, NOT a hot-path participant in live execution
market-analyst
Market regime detection and technical analysis using npx neural-trader — RSI, MACD, Bollinger Bands, volume profile, regime classification. Pipeline entry point — sends RegimeVerdict to trading-strategist (ADR-126 Phase 5)
risk-analyst
Portfolio risk assessment and position sizing using npx neural-trader — VaR/CVaR, Kelly criterion, circuit breakers, correlation monitoring. Pipeline BLOCKING GATE — receives SignalProposal from trading-strategist, returns RiskDecision (ADR-126 Phase 5)
trading-strategist
Designs and optimizes neural trading strategies using npx neural-trader — LSTM/Transformer models, Rust/NAPI backtesting, Z-score anomaly detection. Pipeline middle stage — receives RegimeVerdict from market-analyst, sends SignalProposal[] to risk-analyst, gated on RiskDecision approval (ADR-126 Pha
FEDERATED - the claims inside are not verified by this repo. 501 engineering-grade trading skills - point-in-time backtest schemas, universe look-ahead, alt-data availability lag, ML against transaction costs, adjusted-vs-unadjusted prices, backtest determinism and audit trails, broker idempotency and failover, exchange FIX specs (ASX, B3, Bursa, BIST, Cboe), regulatory gates and a key-scope audit skill. Its README claims 458 of 501 cite a primary source; it is clearly LLM-generated at scale and the depth is unverified. Upstream splits these into per-domain plugins, but its root manifest exposes all 501 at once - roughly 25x the discovery budget, which is yours to spend since it installs disabled. No credentials in the skills; nothing executes. Installs disabled.
FEDERATED - the claims inside are not verified by this repo. FTShare's official one-endpoint-per-skill data catalogue for the Chinese market: A/HK/US quotes and K-lines, 可转债 including SZSE declaration snapshots, ETF PCF and IOPV, 龙虎榜, block trades, margin balances, executive holdings, the ChinaBond yield curve, the CPI/PPI/PMI/LPR/M2/credit macro series and news-sentiment factors. FTSHARE_API_KEY in the environment (a free tier exists per the launch article); read-only. Be deliberate: 215 sub-skills is roughly 10x the ~20-skill discovery budget, so treat it as a catalogue to enable for a session, not a standing install - it arrives disabled and the budget is yours to spend. Installs disabled.
FEDERATED - the claims inside are not verified by this repo. The deepest IFRS/GAAP pack in this sweep that also validates cleanly: IFRS 9 expected credit loss, IFRS 16 leases, IAS 12 deferred tax, revenue recognition, consolidation, SOX, forensic accounting, insurance reserving, WACC, LBO, treasury and Basel banking compliance, with an eval harness in the repo. Outside this library's quant core and overlapping the banking-regulatory entry on Basel and IFRS 9. Optional Stripe/Plaid connectors; nothing executes. Installs disabled.
FEDERATED - the claims inside are not verified by this repo. HKUDS' 33k-star trading agent, taken for its skill tree alone - the richest algorithm content this sweep found: the Kakushadze 101, GTJA 191 and Qlib 158 alpha zoos, factor IC/IR research, execution models (square-root impact, VWAP/TWAP), microstructure (VPIN, Kyle's lambda, Amihud), SABR and local-vol options, convertible bonds, credit, Brinson attribution, GARCH/ADF and a Pine/vnpy exporter. The skills are coupled to the app's data router and Python toolkit, so test them standalone before trusting them; data keys go in a .env, and live orders are reachable only through a separate gated connector with a kill switch, not from these skills. 90 skills is about 4.5x the discovery budget. Installs disabled.
FEDERATED - the claims inside are not verified by this repo. The closest structural peer to fin-libraries found in the wild - 82 one-library skills in Chinese - and the only pack anywhere in this sweep with actuarial and structured-credit content: MlFinLab information bars and fractional differencing, arch/GARCH, alphalens, bt/backtrader/nautilus/qlib/LEAN, QuantLib and FinancePy day counts and schedules, XVA (CVA/DVA/FVA, SIMM), chainladder loss reserving, IFRS 9 ECL, credit scorecards and transition matrices, ABS waterfalls, Smith-Wilson EIOPA curves. Depth is unverified and the author has one star; the easytrader and nautilus skills document live-broker paths but nothing executes by default. 82 skills is roughly 4x the discovery budget. Installs disabled.
FEDERATED - the claims inside are not verified by this repo. The maintained superset of geeksfino-finskills, 107 skills across China, Hong Kong and US directories: A+H premium, block deals, 龙虎榜, pledge risk, goodwill, 解禁 unlocks, margin risk, factor crowding, event studies, valuation regimes, and HK southbound, currency and liquidity monitors. Free data sources; nothing executes. Two caveats - it has been idle since February 2026, and 107 skills is roughly 5x the discovery budget, which is yours to spend since it installs disabled. Installs disabled.
Apollo GraphQL agent skills for Claude Code — Apollo Client, Server, Federation, Connectors, Router, Rover CLI, iOS, Kotlin, and the Apollo MCP server. Covers schema design, query optimization, and GraphQL best practices.
Data lake, analytics, search, streaming, ETL, and orchestration workflows with S3 Tables, AWS Glue, Athena, Amazon Redshift, Amazon OpenSearch Service, Amazon MSK, Amazon Managed Service for Apache Flink, Amazon MWAA, and Amazon SageMaker Catalog. Covers managed Iceberg tables on S3 Tables, ingestion from JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Amazon Redshift, Snowflake, BigQuery, and DynamoDB, AWS Glue Data Catalog inventory and asset discovery, federated Athena queries, vector storage and semantic search on Amazon S3 Vectors, Amazon OpenSearch Service and Serverless (migration from Solr/Elasticsearch, provisioning, vector/semantic/hybrid search, log analytics, trace analytics), Amazon MSK Provisioned with Standard and Express brokers (cluster sizing, performance and consumer lag troubleshooting, topic management, MSK Replicator for disaster recovery and active-active replication, and Streaming Tables delivery to S3 Tables), migrating self-managed Apache Kafka to MSK Express, Apache Flink application development on Amazon Managed Service for Apache Flink, and Amazon MWAA Provisioned and MWAA Serverless (authoring, testing, and debugging DAGs and workflows, and Airflow version upgrades for provisioned environments), and Amazon SageMaker Catalog and AWS Glue Data Catalog curation (business descriptions, glossaries, metadata forms, publishing, and safe asset retirement) with subscription-based access requests, approvals, audits, and revocations.
Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper)
1 skill
apify/agent-skills 2,497
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