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| Author | SHA1 | Date | |
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 funman300
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ac36c73d40 |
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# Claude Flow runtime files
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data/
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logs/
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sessions/
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neural/
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*.log
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*.tmp
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# RuFlo V3 - Complete Capabilities Reference
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> Generated: 2026-05-19T00:18:20.864Z
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> Full documentation: https://github.com/ruvnet/claude-flow
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## 📋 Table of Contents
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1. [Overview](#overview)
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2. [Swarm Orchestration](#swarm-orchestration)
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3. [Available Agents (60+)](#available-agents)
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4. [CLI Commands (26 Commands, 140+ Subcommands)](#cli-commands)
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5. [Hooks System (27 Hooks + 12 Workers)](#hooks-system)
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6. [Memory & Intelligence (RuVector)](#memory--intelligence)
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7. [Hive-Mind Consensus](#hive-mind-consensus)
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8. [Performance Targets](#performance-targets)
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9. [Integration Ecosystem](#integration-ecosystem)
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---
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## Overview
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RuFlo V3 is a domain-driven design architecture for multi-agent AI coordination with:
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- **15-Agent Swarm Coordination** with hierarchical and mesh topologies
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- **HNSW Vector Search** - 150x-12,500x faster pattern retrieval
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- **SONA Neural Learning** - Self-optimizing with <0.05ms adaptation
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- **Byzantine Fault Tolerance** - Queen-led consensus mechanisms
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- **MCP Server Integration** - Model Context Protocol support
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### Current Configuration
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| Setting | Value |
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|---------|-------|
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| Topology | hierarchical-mesh |
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| Max Agents | 15 |
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| Memory Backend | hybrid |
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| HNSW Indexing | Enabled |
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| Neural Learning | Enabled |
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| LearningBridge | Enabled (SONA + ReasoningBank) |
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| Knowledge Graph | Enabled (PageRank + Communities) |
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| Agent Scopes | Enabled (project/local/user) |
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---
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## Swarm Orchestration
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### Topologies
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| Topology | Description | Best For |
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|----------|-------------|----------|
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| `hierarchical` | Queen controls workers directly | Anti-drift, tight control |
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| `mesh` | Fully connected peer network | Distributed tasks |
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| `hierarchical-mesh` | V3 hybrid (recommended) | 10+ agents |
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| `ring` | Circular communication | Sequential workflows |
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| `star` | Central coordinator | Simple coordination |
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| `adaptive` | Dynamic based on load | Variable workloads |
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### Strategies
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- `balanced` - Even distribution across agents
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- `specialized` - Clear roles, no overlap (anti-drift)
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- `adaptive` - Dynamic task routing
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### Quick Commands
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```bash
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# Initialize swarm
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npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized
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# Check status
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npx @claude-flow/cli@latest swarm status
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# Monitor activity
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npx @claude-flow/cli@latest swarm monitor
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```
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---
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## Available Agents
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### Core Development (5)
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`coder`, `reviewer`, `tester`, `planner`, `researcher`
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### V3 Specialized (4)
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`security-architect`, `security-auditor`, `memory-specialist`, `performance-engineer`
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### Swarm Coordination (5)
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`hierarchical-coordinator`, `mesh-coordinator`, `adaptive-coordinator`, `collective-intelligence-coordinator`, `swarm-memory-manager`
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### Consensus & Distributed (7)
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`byzantine-coordinator`, `raft-manager`, `gossip-coordinator`, `consensus-builder`, `crdt-synchronizer`, `quorum-manager`, `security-manager`
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### Performance & Optimization (5)
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`perf-analyzer`, `performance-benchmarker`, `task-orchestrator`, `memory-coordinator`, `smart-agent`
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### GitHub & Repository (9)
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`github-modes`, `pr-manager`, `code-review-swarm`, `issue-tracker`, `release-manager`, `workflow-automation`, `project-board-sync`, `repo-architect`, `multi-repo-swarm`
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### SPARC Methodology (6)
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`sparc-coord`, `sparc-coder`, `specification`, `pseudocode`, `architecture`, `refinement`
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### Specialized Development (8)
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`backend-dev`, `mobile-dev`, `ml-developer`, `cicd-engineer`, `api-docs`, `system-architect`, `code-analyzer`, `base-template-generator`
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### Testing & Validation (2)
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`tdd-london-swarm`, `production-validator`
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### Agent Routing by Task
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| Task Type | Recommended Agents | Topology |
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|-----------|-------------------|----------|
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| Bug Fix | researcher, coder, tester | mesh |
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| New Feature | coordinator, architect, coder, tester, reviewer | hierarchical |
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| Refactoring | architect, coder, reviewer | mesh |
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| Performance | researcher, perf-engineer, coder | hierarchical |
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| Security | security-architect, auditor, reviewer | hierarchical |
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| Docs | researcher, api-docs | mesh |
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---
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## CLI Commands
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### Core Commands (12)
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| Command | Subcommands | Description |
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|---------|-------------|-------------|
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| `init` | 4 | Project initialization |
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| `agent` | 8 | Agent lifecycle management |
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| `swarm` | 6 | Multi-agent coordination |
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| `memory` | 11 | AgentDB with HNSW search |
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| `mcp` | 9 | MCP server management |
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| `task` | 6 | Task assignment |
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| `session` | 7 | Session persistence |
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| `config` | 7 | Configuration |
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| `status` | 3 | System monitoring |
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| `workflow` | 6 | Workflow templates |
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| `hooks` | 17 | Self-learning hooks |
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| `hive-mind` | 6 | Consensus coordination |
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### Advanced Commands (14)
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| Command | Subcommands | Description |
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|---------|-------------|-------------|
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| `daemon` | 5 | Background workers |
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| `neural` | 5 | Pattern training |
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| `security` | 6 | Security scanning |
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| `performance` | 5 | Profiling & benchmarks |
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| `providers` | 5 | AI provider config |
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| `plugins` | 5 | Plugin management |
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| `deployment` | 5 | Deploy management |
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| `embeddings` | 4 | Vector embeddings |
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| `claims` | 4 | Authorization |
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| `migrate` | 5 | V2→V3 migration |
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| `process` | 4 | Process management |
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| `doctor` | 1 | Health diagnostics |
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| `completions` | 4 | Shell completions |
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### Example Commands
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```bash
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# Initialize
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npx @claude-flow/cli@latest init --wizard
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# Spawn agent
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npx @claude-flow/cli@latest agent spawn -t coder --name my-coder
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# Memory operations
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npx @claude-flow/cli@latest memory store --key "pattern" --value "data" --namespace patterns
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npx @claude-flow/cli@latest memory search --query "authentication"
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# Diagnostics
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npx @claude-flow/cli@latest doctor --fix
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```
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---
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## Hooks System
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### 27 Available Hooks
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#### Core Hooks (6)
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| Hook | Description |
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|------|-------------|
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| `pre-edit` | Context before file edits |
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| `post-edit` | Record edit outcomes |
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| `pre-command` | Risk assessment |
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| `post-command` | Command metrics |
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| `pre-task` | Task start + agent suggestions |
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| `post-task` | Task completion learning |
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#### Session Hooks (4)
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| Hook | Description |
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|------|-------------|
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| `session-start` | Start/restore session |
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| `session-end` | Persist state |
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| `session-restore` | Restore previous |
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| `notify` | Cross-agent notifications |
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#### Intelligence Hooks (5)
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| Hook | Description |
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|------|-------------|
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| `route` | Optimal agent routing |
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| `explain` | Routing decisions |
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| `pretrain` | Bootstrap intelligence |
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| `build-agents` | Generate configs |
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| `transfer` | Pattern transfer |
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#### Coverage Hooks (3)
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| Hook | Description |
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|------|-------------|
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| `coverage-route` | Coverage-based routing |
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| `coverage-suggest` | Improvement suggestions |
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| `coverage-gaps` | Gap analysis |
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### 12 Background Workers
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| Worker | Priority | Purpose |
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|--------|----------|---------|
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| `ultralearn` | normal | Deep knowledge |
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| `optimize` | high | Performance |
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| `consolidate` | low | Memory consolidation |
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| `predict` | normal | Predictive preload |
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| `audit` | critical | Security |
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| `map` | normal | Codebase mapping |
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| `preload` | low | Resource preload |
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| `deepdive` | normal | Deep analysis |
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| `document` | normal | Auto-docs |
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| `refactor` | normal | Suggestions |
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| `benchmark` | normal | Benchmarking |
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| `testgaps` | normal | Coverage gaps |
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---
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## Memory & Intelligence
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### RuVector Intelligence System
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- **SONA**: Self-Optimizing Neural Architecture (<0.05ms)
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- **MoE**: Mixture of Experts routing
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- **HNSW**: 150x-12,500x faster search
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- **EWC++**: Prevents catastrophic forgetting
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- **Flash Attention**: 2.49x-7.47x speedup
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- **Int8 Quantization**: 3.92x memory reduction
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### 4-Step Intelligence Pipeline
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1. **RETRIEVE** - HNSW pattern search
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2. **JUDGE** - Success/failure verdicts
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3. **DISTILL** - LoRA learning extraction
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4. **CONSOLIDATE** - EWC++ preservation
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### Self-Learning Memory (ADR-049)
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| Component | Status | Description |
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|-----------|--------|-------------|
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| **LearningBridge** | ✅ Enabled | Connects insights to SONA/ReasoningBank neural pipeline |
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| **MemoryGraph** | ✅ Enabled | PageRank knowledge graph + community detection |
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| **AgentMemoryScope** | ✅ Enabled | 3-scope agent memory (project/local/user) |
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**LearningBridge** - Insights trigger learning trajectories. Confidence evolves: +0.03 on access, -0.005/hour decay. Consolidation runs the JUDGE/DISTILL/CONSOLIDATE pipeline.
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**MemoryGraph** - Builds a knowledge graph from entry references. PageRank identifies influential insights. Communities group related knowledge. Graph-aware ranking blends vector + structural scores.
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**AgentMemoryScope** - Maps Claude Code 3-scope directories:
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- `project`: `<gitRoot>/.claude/agent-memory/<agent>/`
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- `local`: `<gitRoot>/.claude/agent-memory-local/<agent>/`
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- `user`: `~/.claude/agent-memory/<agent>/`
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High-confidence insights (>0.8) can transfer between agents.
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### Memory Commands
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```bash
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# Store pattern
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npx @claude-flow/cli@latest memory store --key "name" --value "data" --namespace patterns
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# Semantic search
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npx @claude-flow/cli@latest memory search --query "authentication"
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# List entries
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npx @claude-flow/cli@latest memory list --namespace patterns
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# Initialize database
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npx @claude-flow/cli@latest memory init --force
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```
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---
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## Hive-Mind Consensus
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### Queen Types
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| Type | Role |
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|------|------|
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| Strategic Queen | Long-term planning |
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| Tactical Queen | Execution coordination |
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| Adaptive Queen | Dynamic optimization |
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### Worker Types (8)
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`researcher`, `coder`, `analyst`, `tester`, `architect`, `reviewer`, `optimizer`, `documenter`
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### Consensus Mechanisms
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| Mechanism | Fault Tolerance | Use Case |
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|-----------|-----------------|----------|
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| `byzantine` | f < n/3 faulty | Adversarial |
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| `raft` | f < n/2 failed | Leader-based |
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| `gossip` | Eventually consistent | Large scale |
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| `crdt` | Conflict-free | Distributed |
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| `quorum` | Configurable | Flexible |
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|
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### Hive-Mind Commands
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```bash
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# Initialize
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npx @claude-flow/cli@latest hive-mind init --queen-type strategic
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# Status
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npx @claude-flow/cli@latest hive-mind status
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# Spawn workers
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npx @claude-flow/cli@latest hive-mind spawn --count 5 --type worker
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# Consensus
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npx @claude-flow/cli@latest hive-mind consensus --propose "task"
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```
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---
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## Performance Targets
|
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|
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| Metric | Target | Status |
|
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|--------|--------|--------|
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| HNSW Search | 150x-12,500x faster | ✅ Implemented |
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| Memory Reduction | 50-75% | ✅ Implemented (3.92x) |
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| SONA Integration | Pattern learning | ✅ Implemented |
|
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| Flash Attention | 2.49x-7.47x | 🔄 In Progress |
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| MCP Response | <100ms | ✅ Achieved |
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| CLI Startup | <500ms | ✅ Achieved |
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| SONA Adaptation | <0.05ms | 🔄 In Progress |
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| Graph Build (1k) | <200ms | ✅ 2.78ms (71.9x headroom) |
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| PageRank (1k) | <100ms | ✅ 12.21ms (8.2x headroom) |
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| Insight Recording | <5ms/each | ✅ 0.12ms (41x headroom) |
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| Consolidation | <500ms | ✅ 0.26ms (1,955x headroom) |
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| Knowledge Transfer | <100ms | ✅ 1.25ms (80x headroom) |
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---
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## Integration Ecosystem
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### Integrated Packages
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| Package | Version | Purpose |
|
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|---------|---------|---------|
|
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| agentic-flow | 3.0.0-alpha.1 | Core coordination + ReasoningBank + Router |
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| agentdb | 3.0.0-alpha.10 | Vector database + 8 controllers |
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| @ruvector/attention | 0.1.3 | Flash attention |
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| @ruvector/sona | 0.1.5 | Neural learning |
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### Optional Integrations
|
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| Package | Command |
|
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|---------|---------|
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| ruv-swarm | `npx ruv-swarm mcp start` |
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| flow-nexus | `npx flow-nexus@latest mcp start` |
|
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| agentic-jujutsu | `npx agentic-jujutsu@latest` |
|
||||
|
||||
### MCP Server Setup
|
||||
```bash
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# Add Ruflo MCP
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claude mcp add ruflo -- npx -y ruflo@latest
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|
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# Optional servers
|
||||
claude mcp add ruv-swarm -- npx -y ruv-swarm mcp start
|
||||
claude mcp add flow-nexus -- npx -y flow-nexus@latest mcp start
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Quick Reference
|
||||
|
||||
### Essential Commands
|
||||
```bash
|
||||
# Setup
|
||||
npx ruflo@latest init --wizard
|
||||
npx ruflo@latest daemon start
|
||||
npx ruflo@latest doctor --fix
|
||||
|
||||
# Swarm
|
||||
npx ruflo@latest swarm init --topology hierarchical --max-agents 8
|
||||
npx ruflo@latest swarm status
|
||||
|
||||
# Agents
|
||||
npx ruflo@latest agent spawn -t coder
|
||||
npx ruflo@latest agent list
|
||||
|
||||
# Memory
|
||||
npx ruflo@latest memory search --query "patterns"
|
||||
|
||||
# Hooks
|
||||
npx ruflo@latest hooks pre-task --description "task"
|
||||
npx ruflo@latest hooks worker dispatch --trigger optimize
|
||||
```
|
||||
|
||||
### File Structure
|
||||
```
|
||||
.claude-flow/
|
||||
├── config.yaml # Runtime configuration
|
||||
├── CAPABILITIES.md # This file
|
||||
├── data/ # Memory storage
|
||||
├── logs/ # Operation logs
|
||||
├── sessions/ # Session state
|
||||
├── hooks/ # Custom hooks
|
||||
├── agents/ # Agent configs
|
||||
└── workflows/ # Workflow templates
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
**Full Documentation**: https://github.com/ruvnet/claude-flow
|
||||
**Issues**: https://github.com/ruvnet/claude-flow/issues
|
||||
@@ -1,43 +0,0 @@
|
||||
# RuFlo V3 Runtime Configuration
|
||||
# Generated: 2026-05-19T00:18:20.863Z
|
||||
|
||||
version: "3.0.0"
|
||||
|
||||
swarm:
|
||||
topology: hierarchical-mesh
|
||||
maxAgents: 15
|
||||
autoScale: true
|
||||
coordinationStrategy: consensus
|
||||
|
||||
memory:
|
||||
backend: hybrid
|
||||
enableHNSW: true
|
||||
persistPath: .claude-flow/data
|
||||
cacheSize: 100
|
||||
# ADR-049: Self-Learning Memory
|
||||
learningBridge:
|
||||
enabled: true
|
||||
sonaMode: balanced
|
||||
confidenceDecayRate: 0.005
|
||||
accessBoostAmount: 0.03
|
||||
consolidationThreshold: 10
|
||||
memoryGraph:
|
||||
enabled: true
|
||||
pageRankDamping: 0.85
|
||||
maxNodes: 5000
|
||||
similarityThreshold: 0.8
|
||||
agentScopes:
|
||||
enabled: true
|
||||
defaultScope: project
|
||||
|
||||
neural:
|
||||
enabled: true
|
||||
modelPath: .claude-flow/neural
|
||||
|
||||
hooks:
|
||||
enabled: true
|
||||
autoExecute: true
|
||||
|
||||
mcp:
|
||||
autoStart: false
|
||||
port: 3000
|
||||
@@ -1,17 +0,0 @@
|
||||
{
|
||||
"initialized": "2026-05-19T00:18:20.864Z",
|
||||
"routing": {
|
||||
"accuracy": 0,
|
||||
"decisions": 0
|
||||
},
|
||||
"patterns": {
|
||||
"shortTerm": 0,
|
||||
"longTerm": 0,
|
||||
"quality": 0
|
||||
},
|
||||
"sessions": {
|
||||
"total": 0,
|
||||
"current": null
|
||||
},
|
||||
"_note": "Intelligence grows as you use Ruflo"
|
||||
}
|
||||
@@ -1,18 +0,0 @@
|
||||
{
|
||||
"timestamp": "2026-05-19T00:18:20.864Z",
|
||||
"processes": {
|
||||
"agentic_flow": 0,
|
||||
"mcp_server": 0,
|
||||
"estimated_agents": 0
|
||||
},
|
||||
"swarm": {
|
||||
"active": false,
|
||||
"agent_count": 0,
|
||||
"coordination_active": false
|
||||
},
|
||||
"integration": {
|
||||
"agentic_flow_active": false,
|
||||
"mcp_active": false
|
||||
},
|
||||
"_initialized": true
|
||||
}
|
||||
@@ -1,26 +0,0 @@
|
||||
{
|
||||
"version": "3.0.0",
|
||||
"initialized": "2026-05-19T00:18:20.864Z",
|
||||
"domains": {
|
||||
"completed": 0,
|
||||
"total": 5,
|
||||
"status": "INITIALIZING"
|
||||
},
|
||||
"ddd": {
|
||||
"progress": 0,
|
||||
"modules": 0,
|
||||
"totalFiles": 0,
|
||||
"totalLines": 0
|
||||
},
|
||||
"swarm": {
|
||||
"activeAgents": 0,
|
||||
"maxAgents": 15,
|
||||
"topology": "hierarchical-mesh"
|
||||
},
|
||||
"learning": {
|
||||
"status": "READY",
|
||||
"patternsLearned": 0,
|
||||
"sessionsCompleted": 0
|
||||
},
|
||||
"_note": "Metrics will update as you use Ruflo. Run: npx ruflo@latest daemon start"
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
{
|
||||
"initialized": "2026-05-19T00:18:20.864Z",
|
||||
"status": "PENDING",
|
||||
"cvesFixed": 0,
|
||||
"totalCves": 3,
|
||||
"lastScan": null,
|
||||
"_note": "Run: npx @claude-flow/cli@latest security scan"
|
||||
}
|
||||
@@ -8,10 +8,6 @@
|
||||
data/
|
||||
.claude/
|
||||
|
||||
# ruflo runtime state
|
||||
agentdb.rvf
|
||||
agentdb.rvf.lock
|
||||
|
||||
# IDE project files
|
||||
.idea/
|
||||
|
||||
|
||||
@@ -1,22 +0,0 @@
|
||||
{
|
||||
"mcpServers": {
|
||||
"ruflo": {
|
||||
"command": "npx",
|
||||
"args": [
|
||||
"-y",
|
||||
"ruflo@latest",
|
||||
"mcp",
|
||||
"start"
|
||||
],
|
||||
"env": {
|
||||
"npm_config_update_notifier": "false",
|
||||
"CLAUDE_FLOW_MODE": "v3",
|
||||
"CLAUDE_FLOW_HOOKS_ENABLED": "true",
|
||||
"CLAUDE_FLOW_TOPOLOGY": "hierarchical-mesh",
|
||||
"CLAUDE_FLOW_MAX_AGENTS": "15",
|
||||
"CLAUDE_FLOW_MEMORY_BACKEND": "hybrid"
|
||||
},
|
||||
"autoStart": false
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -20,4 +20,4 @@ resources:
|
||||
images:
|
||||
- name: solitaire-server
|
||||
newName: git.aleshym.co/funman300/solitaire-server
|
||||
newTag: 83c40116
|
||||
newTag: eb6c93fb
|
||||
|
||||
@@ -72,17 +72,6 @@ const TIME_ATTACK_TOAST_SECS: f32 = 5.0;
|
||||
const CHALLENGE_TOAST_SECS: f32 = 3.0;
|
||||
const VOLUME_TOAST_SECS: f32 = 1.4;
|
||||
|
||||
/// Z added to a card's render depth while its `CardAnim` is in-flight.
|
||||
///
|
||||
/// Foundation and tableau cards share x,y during the slide (destination equals
|
||||
/// a slot that already holds a card). Without this lift the incoming card's
|
||||
/// bottom-right corner overlaps the stationary card's top-left, which the
|
||||
/// player perceives as a single card with mismatched rank/suit indices.
|
||||
///
|
||||
/// 50.0 sits comfortably above the highest pile depth (~1.04) and well below
|
||||
/// `DRAG_Z` (500), so a dragged card always renders above an animated one.
|
||||
const CARD_ANIM_Z_LIFT: f32 = 50.0;
|
||||
|
||||
/// Per-card stagger interval for the win cascade at Normal speed (seconds).
|
||||
///
|
||||
/// Sourced from `ui_theme::MOTION_CASCADE_STAGGER_SECS` so all motion timing
|
||||
@@ -265,11 +254,7 @@ fn advance_card_anims(
|
||||
// shared `CardAnim` struct stays a simple linear-tween container — the
|
||||
// upgrade is one extra `sample_curve` call per advancing animation.
|
||||
let s = sample_curve(MotionCurve::SmoothSnap, t);
|
||||
let mut pos = anim.start.lerp(anim.target, s);
|
||||
// Elevate z during transit so the moving card always renders in front
|
||||
// of any card already resting at the destination position.
|
||||
pos.z = anim.target.z + CARD_ANIM_Z_LIFT;
|
||||
transform.translation = pos;
|
||||
transform.translation = anim.start.lerp(anim.target, s);
|
||||
if t >= 1.0 {
|
||||
transform.translation = anim.target;
|
||||
commands.entity(entity).remove::<CardAnim>();
|
||||
|
||||
@@ -178,8 +178,8 @@ pub struct CardLabel;
|
||||
/// readable at phone scale. Only exists when `CardImageSet` is present
|
||||
/// (the fallback solid-colour path uses a plain `CardLabel` instead).
|
||||
#[cfg(target_os = "android")]
|
||||
#[derive(Component, Debug, Clone)]
|
||||
struct AndroidCornerLabel(pub String);
|
||||
#[derive(Component, Debug, Clone, Copy)]
|
||||
struct AndroidCornerLabel;
|
||||
|
||||
/// Solid-colour background sprite behind [`AndroidCornerLabel`].
|
||||
///
|
||||
@@ -707,20 +707,15 @@ fn sync_cards(
|
||||
.map(|c| c.id)
|
||||
};
|
||||
|
||||
// Map card_id -> (Entity, current_translation, anim_end) for in-place
|
||||
// updates. `anim_end` is `Some(end_xy)` when a curve-based `CardAnimation`
|
||||
// is currently driving the card (e.g. a drag-rejection return tween).
|
||||
//
|
||||
// In the position loop below we compare `anim_end` against the new game-
|
||||
// state target position to decide whether to honour or cancel the tween:
|
||||
// • end ≈ target → animation is still heading to the right place; let
|
||||
// it finish (skip the snap/slide path).
|
||||
// • end ≠ target → the game state has changed (e.g. a new game started
|
||||
// while the win-cascade was mid-flight); cancel the
|
||||
// stale `CardAnimation` and apply the new position.
|
||||
let mut existing: HashMap<u32, (Entity, Vec3, Option<Vec2>)> = HashMap::new();
|
||||
// Map card_id -> (Entity, current_translation, has_card_animation) for
|
||||
// in-place updates. The `has_card_animation` flag lets `update_card_entity`
|
||||
// skip the snap/slide path on cards that are already being driven by a
|
||||
// curve-based `CardAnimation` tween (e.g. the drag-rejection return tween
|
||||
// — see `input_plugin::end_drag`). Otherwise the StateChangedEvent that
|
||||
// accompanies a rejection would race the tween and the card would jump.
|
||||
let mut existing: HashMap<u32, (Entity, Vec3, bool)> = HashMap::new();
|
||||
for (entity, marker, transform, anim) in entities.iter() {
|
||||
existing.insert(marker.card_id, (entity, transform.translation, anim.map(|a| a.end)));
|
||||
existing.insert(marker.card_id, (entity, transform.translation, anim.is_some()));
|
||||
}
|
||||
|
||||
let live_ids: HashSet<u32> = positions.iter().map(|(c, _, _)| c.id).collect();
|
||||
@@ -737,19 +732,7 @@ fn sync_cards(
|
||||
// behind the incoming top card during the draw slide animation.
|
||||
for (card, position, z) in positions {
|
||||
let entity = match existing.get(&card.id) {
|
||||
Some(&(entity, cur, anim_end)) => {
|
||||
// If a CardAnimation is in flight, check whether its destination
|
||||
// still matches the game-state target. If the game moved the card
|
||||
// elsewhere (e.g. new game started during a win-cascade scatter),
|
||||
// cancel the stale tween so the card snaps/slides to its new home.
|
||||
let has_anim = match anim_end {
|
||||
Some(end_xy) if (end_xy - position).length() > 2.0 => {
|
||||
commands.entity(entity).remove::<CardAnimation>();
|
||||
false
|
||||
}
|
||||
Some(_) => true,
|
||||
None => false,
|
||||
};
|
||||
Some(&(entity, cur, has_anim)) => {
|
||||
update_card_entity(
|
||||
&mut commands, entity, card, position, z, layout,
|
||||
slide_secs, back_colour, color_blind, high_contrast, cur, has_anim, card_images, selected_back, font_handle,
|
||||
@@ -1159,11 +1142,10 @@ fn add_android_corner_label(
|
||||
// Large rank+suit text drawn on top of the background. FiraMono must be
|
||||
// wired here explicitly — the suit glyphs (U+2660–U+2666) are not in
|
||||
// Bevy's built-in font and render as a coloured rectangle without it.
|
||||
let label_text = mobile_label_for(card);
|
||||
parent.spawn((
|
||||
AndroidCornerLabel(label_text.clone()),
|
||||
AndroidCornerLabel,
|
||||
CardLabel,
|
||||
Text2d::new(label_text),
|
||||
Text2d::new(mobile_label_for(card)),
|
||||
TextFont {
|
||||
font: font_handle.cloned().unwrap_or_default(),
|
||||
font_size,
|
||||
@@ -2107,7 +2089,7 @@ fn resize_cards_in_place(
|
||||
fn resize_android_corner_labels(
|
||||
layout: Res<LayoutResource>,
|
||||
card_images: Option<Res<CardImageSet>>,
|
||||
mut text_query: Query<(&AndroidCornerLabel, &mut Text2d, &mut TextFont, &mut Transform)>,
|
||||
mut text_query: Query<(&mut TextFont, &mut Transform), With<AndroidCornerLabel>>,
|
||||
mut bg_query: Query<
|
||||
(&mut Sprite, &mut Transform),
|
||||
(With<AndroidCornerBg>, Without<AndroidCornerLabel>),
|
||||
@@ -2123,8 +2105,7 @@ fn resize_android_corner_labels(
|
||||
let text_x = -layout.0.card_size.x / 2.0 + inset;
|
||||
let text_y = layout.0.card_size.y / 2.0 - inset;
|
||||
|
||||
for (label, mut text2d, mut font, mut transform) in text_query.iter_mut() {
|
||||
text2d.0 = label.0.clone();
|
||||
for (mut font, mut transform) in text_query.iter_mut() {
|
||||
font.font_size = font_size;
|
||||
transform.translation.x = text_x;
|
||||
transform.translation.y = text_y;
|
||||
|
||||
@@ -1159,23 +1159,9 @@ mod tests {
|
||||
.spawn(async { Err::<(), String>("network error".to_string()) });
|
||||
app.world_mut().resource_mut::<OptInTask>().0 = Some(failed_task);
|
||||
|
||||
// Pump until the task is polled or a deadline elapses. A fixed
|
||||
// update count is unreliable under parallel `cargo test --workspace`
|
||||
// load — the AsyncComputeTaskPool background threads can be starved
|
||||
// long enough that 5 updates finish before the task completes.
|
||||
// Mirrors the deadline-loop pattern used in sync_plugin tests.
|
||||
let deadline = std::time::Instant::now() + std::time::Duration::from_secs(5);
|
||||
loop {
|
||||
// Allow the task to complete and be polled.
|
||||
for _ in 0..5 {
|
||||
app.update();
|
||||
let msgs = app.world().resource::<Messages<WarningToastEvent>>();
|
||||
let mut cursor = msgs.get_cursor();
|
||||
if cursor.read(msgs).next().is_some() {
|
||||
break;
|
||||
}
|
||||
if std::time::Instant::now() >= deadline {
|
||||
break;
|
||||
}
|
||||
std::thread::yield_now();
|
||||
}
|
||||
|
||||
let msgs = app.world().resource::<Messages<WarningToastEvent>>();
|
||||
@@ -1197,19 +1183,8 @@ mod tests {
|
||||
.spawn(async { Err::<(), String>("network error".to_string()) });
|
||||
app.world_mut().resource_mut::<OptOutTask>().0 = Some(failed_task);
|
||||
|
||||
// Deadline-bounded pump — see opt_in_error_fires_warning_toast for rationale.
|
||||
let deadline = std::time::Instant::now() + std::time::Duration::from_secs(5);
|
||||
loop {
|
||||
for _ in 0..5 {
|
||||
app.update();
|
||||
let msgs = app.world().resource::<Messages<WarningToastEvent>>();
|
||||
let mut cursor = msgs.get_cursor();
|
||||
if cursor.read(msgs).next().is_some() {
|
||||
break;
|
||||
}
|
||||
if std::time::Instant::now() >= deadline {
|
||||
break;
|
||||
}
|
||||
std::thread::yield_now();
|
||||
}
|
||||
|
||||
let msgs = app.world().resource::<Messages<WarningToastEvent>>();
|
||||
@@ -1235,22 +1210,8 @@ mod tests {
|
||||
let ok_task = AsyncComputeTaskPool::get().spawn(async { Ok::<(), String>(()) });
|
||||
app.world_mut().resource_mut::<OptInTask>().0 = Some(ok_task);
|
||||
|
||||
// Deadline-bounded pump — see opt_in_error_fires_warning_toast for rationale.
|
||||
let deadline = std::time::Instant::now() + std::time::Duration::from_secs(5);
|
||||
loop {
|
||||
for _ in 0..5 {
|
||||
app.update();
|
||||
if app
|
||||
.world()
|
||||
.resource::<SettingsResource>()
|
||||
.0
|
||||
.leaderboard_opted_in
|
||||
{
|
||||
break;
|
||||
}
|
||||
if std::time::Instant::now() >= deadline {
|
||||
break;
|
||||
}
|
||||
std::thread::yield_now();
|
||||
}
|
||||
|
||||
assert!(
|
||||
@@ -1276,22 +1237,8 @@ mod tests {
|
||||
let ok_task = AsyncComputeTaskPool::get().spawn(async { Ok::<(), String>(()) });
|
||||
app.world_mut().resource_mut::<OptOutTask>().0 = Some(ok_task);
|
||||
|
||||
// Deadline-bounded pump — see opt_in_error_fires_warning_toast for rationale.
|
||||
let deadline = std::time::Instant::now() + std::time::Duration::from_secs(5);
|
||||
loop {
|
||||
for _ in 0..5 {
|
||||
app.update();
|
||||
if !app
|
||||
.world()
|
||||
.resource::<SettingsResource>()
|
||||
.0
|
||||
.leaderboard_opted_in
|
||||
{
|
||||
break;
|
||||
}
|
||||
if std::time::Instant::now() >= deadline {
|
||||
break;
|
||||
}
|
||||
std::thread::yield_now();
|
||||
}
|
||||
|
||||
assert!(
|
||||
|
||||
@@ -300,7 +300,6 @@ function render(s) {
|
||||
board.querySelectorAll(".card.drop-target").forEach(e => e.classList.remove("drop-target"));
|
||||
|
||||
if (s.is_auto_completable && !s.is_won && !acTimer) {
|
||||
stopTimer(); // freeze elapsed time at the moment the player's last move completes
|
||||
acTimer = setInterval(doAutoCompleteStep, 380);
|
||||
}
|
||||
if (s.is_won) {
|
||||
|
||||
@@ -40,32 +40,20 @@ export class ReplayPlayer {
|
||||
}
|
||||
/**
|
||||
* Snapshot the current `GameState` as a JS object (see `StateSnapshot`).
|
||||
*
|
||||
* Throws a JS string exception on serialisation failure (should never
|
||||
* occur in practice — `StateSnapshot` contains only primitive types).
|
||||
* @returns {any}
|
||||
*/
|
||||
state() {
|
||||
const ret = wasm.replayplayer_state(this.__wbg_ptr);
|
||||
if (ret[2]) {
|
||||
throw takeFromExternrefTable0(ret[1]);
|
||||
}
|
||||
return takeFromExternrefTable0(ret[0]);
|
||||
return ret;
|
||||
}
|
||||
/**
|
||||
* Apply the next move; returns the post-step snapshot, or `null`
|
||||
* once the move list is exhausted.
|
||||
*
|
||||
* Returns `null` (not an exception) when the replay is finished.
|
||||
* Throws a JS string exception on serialisation failure.
|
||||
* @returns {any}
|
||||
*/
|
||||
step() {
|
||||
const ret = wasm.replayplayer_step(this.__wbg_ptr);
|
||||
if (ret[2]) {
|
||||
throw takeFromExternrefTable0(ret[1]);
|
||||
}
|
||||
return takeFromExternrefTable0(ret[0]);
|
||||
return ret;
|
||||
}
|
||||
/**
|
||||
* 0-indexed position of the next move to apply.
|
||||
@@ -169,16 +157,11 @@ export class SolitaireGame {
|
||||
}
|
||||
/**
|
||||
* Full pile snapshot as a JS object.
|
||||
*
|
||||
* Throws a JS string exception on serialisation failure.
|
||||
* @returns {any}
|
||||
*/
|
||||
state() {
|
||||
const ret = wasm.solitairegame_state(this.__wbg_ptr);
|
||||
if (ret[2]) {
|
||||
throw takeFromExternrefTable0(ret[1]);
|
||||
}
|
||||
return takeFromExternrefTable0(ret[0]);
|
||||
return ret;
|
||||
}
|
||||
/**
|
||||
* Undo the last move. Returns `{ok, error?, snapshot?}`.
|
||||
@@ -197,13 +180,6 @@ function __wbg_get_imports() {
|
||||
const ret = Error(getStringFromWasm0(arg0, arg1));
|
||||
return ret;
|
||||
},
|
||||
__wbg_String_8564e559799eccda: function(arg0, arg1) {
|
||||
const ret = String(arg1);
|
||||
const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_malloc, wasm.__wbindgen_realloc);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true);
|
||||
getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true);
|
||||
},
|
||||
__wbg___wbindgen_throw_9c75d47bf9e7731e: function(arg0, arg1) {
|
||||
throw new Error(getStringFromWasm0(arg0, arg1));
|
||||
},
|
||||
|
||||
Binary file not shown.
Reference in New Issue
Block a user