agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Run the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement
$ npx -y skills add ruvnet/claude-flow --skill sparc-implement --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/sparc-implementContext preview
The summary Claude sees to decide when to auto-load this skill.
Run the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement
name: sparc-implement description: Run the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement argument-hint: "" allowed-tools: mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__task_create mcp__plugin_ruflo-core_ruflo__task_update mcp__plugin_ruflo-core_ruflo__task_complete mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step mcp__plugin_ruflo-core_ruflo__neural_predict mcp__plugin_ruflo-core_ruflo__workflow_create Bash Read Write Edit
Run Phases 2 and 3 of the SPARC methodology: design algorithms with pseudocode, then establish architecture with module boundaries and API contracts.
After the Specification phase is complete and its gate has been passed. This skill covers both the Pseudocode and Architecture phases as they are tightly coupled — algorithm design informs module boundaries and vice versa.
1. **Retrieve specification** — call `mcp__plugin_ruflo-core_ruflo__memory_search` with namespace `sparc-phases` and query for the feature's spec. Extract requirements, acceptance criteria, constraints, and edge cases.
2. **Retrieve phase state** — call `mcp__plugin_ruflo-core_ruflo__memory_search` with namespace `sparc-state` and query for the feature to confirm we are in Phase 2 or 3.
3. **Search for architectural patterns** — call `mcp__plugin_ruflo-core_ruflo__neural_predict` with the feature description to find relevant architectural decisions from past projects
4. **Phase 2 — Pseudocode Design**: a. For each acceptance criterion, write language-agnostic pseudocode that satisfies it b. Define core data structures with type annotations c. Map control flow including:
d. Annotate algorithmic complexity (time and space) for critical paths e. Store pseudocode artifact:
5. **Phase 3 — Architecture Design**: a. Define bounded contexts and aggregate roots following DDD patterns:
b. Design API contracts:
c. Plan module boundaries:
d. Specify infrastructure concerns:
e. Store architecture artifact:
6. **Update phase state** — call `mcp__plugin_ruflo-core_ruflo__memory_store` with namespace `sparc-state`, updating current phase to 3 (Architecture) with both artifacts recorded
7. **Record trajectory step** — call `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step` with architecture summary
8. **Begin implementation** — if the user confirms, proceed to write production code: a. Create files following the defined module boundaries b. Implement interfaces and types first c. Implement core logic following the pseudocode d. Write unit tests alongside implementation (TDD when possible) e. Run tests to verify acceptance criteria
9. **Present architecture** — display the architecture decision record and suggest running `/sparc advance` to pass the Phase 3 gate
# Pseudocode: {Feature Name}
## Core Algorithms
### Algorithm 1: {name}
```pseudocode
FUNCTION processRequest(input):
VALIDATE input against schema
IF invalid THEN THROW ValidationError
result <- TRANSFORM input
STORE result
RETURN resultComplexity: O(n) time, O(1) space
---
src/{feature}/
{feature}.types.ts # Interfaces and types
{feature}.service.ts # Business logic
{feature}.controller.ts # HTTP handling
{feature}.repository.ts # Data access
{feature}.test.ts # Tests--- Phases 2-3 complete. Run `/sparc advance` to pass the gate check.
An agent meta-harness for Claude Code and Codex. 📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.
Repo: ruvnet/claude-flow
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and…
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use…
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing…
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG…
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination