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slash-command-encoder

@DNYoussef/context-cascade
10
0

Design and route slash-command workflows with clear schemas, safety rails, and validated handoffs.

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1Download skill
2Enable skills in Claude

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3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

/============================================================================/ /* 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]