Neural trading strategies — self-learning LSTM/Transformer/N-BEATS models with Rust/NAPI backtesting
Plugin ruflo-neural-trader from marketplace moaa in ahmadhammoud56/MOAA. Commands read from the repo's manifest — 5 days ago.
trader-backtestRun 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-backtestRun 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-explainRegulator-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-cgMean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
trader-portfolioOptimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
trader-regimeDetect 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-riskAssess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status
trader-signalGenerate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
trader-trainTrain neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
/ruflo-neural-trader:traderNeural trading via npx neural-trader — strategies, backtesting, signals, risk, portfolio optimization
backtest-engineerBacktesting 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-analystMarket 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-analystPortfolio 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-strategistDesigns 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
Harness-native ECC operator layer - 68 agents, 286 skills, 94 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
200 skills · 94 commands · 68 agents · 1 MCP server · hooks
Expose the plugin-safe Claude Code subset of Agentic Awesome Skills through a single marketplace entry.
200 skills
Specialist Council: 60 role agents with per-role model/effort and arena-forged members; assembles teams and delivers one verified result.
2 skills · 60 agents · hooks
Matt Pocock's agent skills for real engineering (grilling, spec/ticket flows, TDD, code review, domain modelling and more).
37 skills
Token usage tracking, model cost attribution per agent, budget alerts, and optimization recommendations
24 skills · 1 command · 1 agent · hooks
Talk like caveman. Cut 65% output tokens vs unprompted baseline (measured). Keep all technical accuracy.
20 skills · 1 command · 3 agents · hooks
A four-tier memory ladder for Claude Code where promotion is earned by recurrence, not asserted by confidence. L0 raw transcripts are never injected; L1 candidate atoms are gitignored and recalled on lexical relevance per prompt; L2 project context loads each session start; L3 stable persona is always loaded. L1 to L2 needs three distinct sessions spanning two distinct calendar days (a stated claim needs two; a verified claim is the only single-observation path); L2 to L3 needs two projects and thirty days. Two gates refuse rather than guess: a claim altered by the redaction pass never promotes on evidence alone, and a contradicted claim freezes until a human resolves it. Three hooks run the loop unattended and every one fails open. Nothing reaches a committed CLAUDE.md without an explicit human adopt. Ships 5 stdlib scripts, 3 hooks, a cs-memory-curator agent, /cs-memory, a JSON schema, and a 69-check validator. Concept from TencentCloud/TencentDB-Agent-Memory (MIT); no upstream code included.
1 skill · 1 command · 1 agent · hooks
Audit a project's npm, PyPI, and Go dependencies for supply-chain risk: version-matched advisories for direct dependencies and the full lockfile tree, abandoned upstreams, npm publisher concentration, and install scripts
1 skill
Ships pull requests to open-source projects you don't maintain — CONTRIBUTING compliance, PR-size/scope checks, minimal-diff implementation, AI-disclosure PR descriptions, and rebase or bot-review replies. Use for 提 PR / 上游 PR / 贡献代码, "open a PR", "fix this upstream", "rebase against main", "respond to the bot review". Not for reviewing PRs as maintainer (use github-review-pr) or general GitHub CRUD (use github-ops).
1 skill
Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689), Articles 51-56. Covers GPAI model identification (Art. 3(63)), systemic risk model designation (Art. 3(65), Art. 51, 10^25 FLOP training compute threshold), upstream provider obligations under Art. 53 (transparency, technical documentation, copyright policy, training data summary), additional systemic risk obligations under Art. 55 (model evaluation, adversarial testing, AI Office incident notification, cybersecurity), systemic-risk notification and Commission designation under Art. 52, downstream Art. 25 quasi-provider duties, and Art. 53(2) open-source exemption. Built for in-house counsel and AI governance teams reviewing LLM or foundation model deployments, drafting GPAI compliance plans, or assessing systemic risk thresholds. DACH: BaFin, BSI, BNetzA. Pairs with the EU AI Act System Classifier, High-Risk Readiness, FRIA, and Serious Incident Reporting skills.
200 skills
BDD-first engineering skills forked from mattpocock/skills (synced 2026-11-04, upstream v1.3.2 + 3 commits)
50 skills
Research-augmented fork of obra/superpowers. Ships 13 workflow skills (brainstorming, planning, code review, debugging, verification, worktrees, parallel agents) standalone, with no dependency on the upstream superpowers plugin.
13 skills
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