# Slash Command Encoder > Slash Command Encoder - Skill: `dnyoussef/slash-command-encoder` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/slash-command-encoder` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/slash-command-encoder/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/slash-command-encoder --- /*============================================================================*/ /* SLASH-COMMAND-ENCODER SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: slash-command-encoder version: 2.0.0 description: | [assert|neutral] Creates ergonomic slash commands (/command) that provide fast, unambiguous access to micro-skills, cascades, and agents. Enhanced with auto-discovery, intelligent routing, parameter validation, and co [ground:given] [conf:0.95] [state:confirmed] category: orchestration tags: - commands - interface - ergonomics - auto-discovery - composition author: ruv cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute slash-command-encoder workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic orchestration processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "slash-command-encoder", category: "orchestration", version: "2.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Aspectual", source: "Russian", force: "Complete or ongoing?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["slash-command-encoder", "orchestration", "workflow"], context: "user needs slash-command-encoder capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ ## Orchestration Skill Guidelines ### When to Use This Skill - **Multi-stage workflows** requiring sequential, parallel, or conditional execution - **Complex pipelines** coordinating multiple micro-skills or agents - **Iterative processes** with Codex sandbox testing and auto-fix loops - **Multi-model routing** requiring intelligent AI selection per stage - **Production workflows** needing GitHub integration and memory persistence ### When NOT to Use This Skill - **Single-agent tasks** with no coordination requirements - **Simple sequential work** that doesn't need stage management - **Trivial operations** completing in <5 minutes - **Pure research** without implementation stages ### Success Criteria - [assert|neutral] *All stages complete** with 100% success rate [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] *Dependency resolution** with no circular dependencies [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] *Model routing optimal** for each stage (Gemini/Codex/Claude) [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] *Memory persistence** maintained across all stages [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] *No orphaned stages** - all stages tracked and completed [ground:acceptance-criteria] [conf:0.90] [state:provisional] ### Edge Cases to Handle - **Stage failure mid-cascade** - Implement retry with exponential backoff - **Circular dependencies** - Validate DAG structure before execution - **Model unavailability** - Have fallback model selection per stage - **Memory overflow** - Implement stage result compression - **Timeout on long stages** - Configure per-stage timeout limits ### Guardrails (NEVER Violate) - [assert|emphatic] NEVER: lose stage state** - Persist after each stage completion [ground:policy] [conf:0.98] [state:confirmed] - [assert|neutral] ALWAYS: validate dependencies** - Check DAG acyclic before execution [ground:policy] [conf:0.98] [state:confirmed] - [assert|neutral] ALWAYS: track cascade progress** - Update memory with real-time status [ground:policy] [conf:0.98] [state:confirmed] - [assert|emphatic] NEVER: skip error handling** - Every stage needs try/catch with fallback [ground:policy] [conf:0.98] [state:confirmed] - [assert|neutral] ALWAYS: cleanup on failure** - Release resources, clear temp state [ground:policy] [conf:0.98] [state:confirmed] ### Evidence-Based Validation - **Verify stage outputs** - Check actual results vs expected schema - **Validate data flow** - Confirm outputs passed correctly to next stage - **Check model routing** - Verify correct AI used per stage requirements - **Measure cascade performance** - Track execution time vs estimates - **Audit memory usage** - Ensure no memory leaks across stages # Slash Command Encoder (Enhanced) ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## Overview Creates fast, scriptable `/command` interfaces for micro-skills, cascades, and agents. This enhanced version includes automatic skill discovery, intelligent command generation, parameter validation, multi-model routing, and command chaining patterns. ## Philosophy: Expert Efficiency **Command Line UX for AI**: Expert users benefit from fast, precise, scriptable interfaces over natural language when performing repeated operations. **Enhanced Capabilities**: - **Auto-Discovery**: Scans and catalogs all installed skills automatically - **Intelligent Routing**: Commands invoke optimal AI/agent for task - **Parameter Validation**: Type-checked, auto-completed parameters - **Command Chaining**: Compose commands into pipelines - **Multi-Model Integration**: Direct access to Gemini/Codex via commands **Key Principles**: 1. Fast and unambiguous invocation 2. Self-documenting through naming 3. Composable and scriptable 4. Type-safe parameter handling 5. Muscle memory for power users ## When to Create Slash Commands ✅ **Per /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/orchestration/slash-command-encoder/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "slash-command-encoder-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] SLASH_COMMAND_ENCODER_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]