All Agent Skills
41,263 results
- Rufloruvnetruflo — an installable skill for AI agents.
- Ruflo DoctorruvnetRun health checks on the Ruflo installation and fix common issues
- Rvf ManageruvnetManage RVF (Ruflo Vector Format) files for portable agent memory and cross-platform transfer
- Safety ScanruvnetScan inputs for prompt injection, unsafe content, and adversarial attacks using AIDefence. Use when processing untrusted input (user submissions, API payloads, webhook data, tool outputs) before passing it to a model or executing it.
- Security AuditruvnetComprehensive security scanning and vulnerability detection. Includes input validation, path traversal prevention, CVE detection, and secure coding pattern enforcement. Use when: authentication implementation, authorization logic, payment processing, user data handling, API endpoint creation, file upload handling, database queries, external API integration.
- Security ScanruvnetRun full security scans on the codebase using Ruflo security tools. Use when reviewing PRs for security regressions, auditing auth/input-handling code, before production deploys, or when the user asks for a security check at quick/standard/deep depth.
- Session PersistruvnetPersist and restore agent sessions across conversations with state snapshots
- Skill BuilderruvnetCreate new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.
- Sparc ImplementruvnetRun the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement
- Sparc MethodologyruvnetSPARC development workflow: Specification, Pseudocode, Architecture, Refinement, Completion. A structured approach for complex implementations that ensures thorough planning before coding. Use when: new feature implementation, complex implementations, architectural changes, system redesign, integration work, unclear requirements.
- Sparc RefineruvnetRun the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation
- Sparc SpecruvnetRun the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory
- Stream ChainruvnetStream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
- Swarm AdvancedruvnetAdvanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
- Swarm InitruvnetInitialize a multi-agent swarm with anti-drift configuration. Use when starting a complex multi-file task that needs 3+ coordinated agents (feature implementation, refactor across modules, security audit). Skip for single-file edits or quick questions.
- Swarm OrchestrationruvnetMulti-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules. Use when: 3+ files need changes, new feature implementation, cross-module refactoring, API changes with tests, security-related changes, performance optimization across codebase, database schema changes.
- Tdd WorkflowruvnetTDD London School workflow -- mock-first, outside-in test development
- Test GapsruvnetDetect missing test coverage and generate test suggestions. Use when the user asks about coverage gaps, untested code, or what tests to write next; also after adding a feature to find what still needs tests.
- Trader BacktestruvnetRun 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 BacktestruvnetRun 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 ExplainruvnetRegulator-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 PortfolioruvnetOptimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
- Trader Portfolio CgruvnetMean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
- Trader RegimeruvnetDetect 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.