Agent skill

slash-command-encoder

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 command chaining. Generates comprehensive command catalogs for all installed skills with multi-model integration.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/dnyoussef/slash-command-encoder

SKILL.md

Slash Command Encoder (Enhanced)

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

✅ Perfect For:

  • Operations performed repeatedly (daily/weekly)
  • Workflows that need exact parameters
  • Tasks requiring scriptable automation
  • Commands that compose into pipelines
  • Expert user shortcuts

❌ Don't Use For:

  • One-off exploratory tasks
  • Operations needing natural language nuance
  • Tasks better suited to interactive dialogue

Enhanced Creation Workflow

Step 1: Auto-Discovery Phase

Scan Installed Skills:

bash
# Discovery algorithm
scan_directories:
  - ~/.claude/skills/*/SKILL.md
  - .claude/skills/*/SKILL.md

extract_metadata:
  - name (command base)
  - description (help text)
  - inputs (parameters)
  - outputs (return types)
  - integration_points (routing)

Catalog Generation:

yaml
discovered_skills:
  micro_skills: [extract-data, validate-api, refactor-code, ...]
  cascades: [audit-pipeline, code-quality-swarm, ...]
  agents: [root-cause-analyzer, code-reviewer, ...]
  multi_model: [gemini-megacontext, codex-auto, ...]

Step 2: Command Design (Enhanced)

A. Naming Conventions

Category Prefixes:

bash
# Data operations
/extract-json, /validate-csv, /transform-xml

# Code operations
/lint-python, /test-coverage, /refactor-imports

# Agent invocation
/agent-rca, /agent-reviewer, /agent-architect

# Multi-model
/gemini-search, /codex-auto, /claude-reason

# Workflows
/audit-pipeline, /deploy-prod, /quality-check

Naming Rules:

  • Verb-noun pattern: /validate-api, /extract-data
  • Agent prefix: /agent-<specialty>
  • Model prefix: /gemini-*, /codex-*
  • Workflow descriptive: /audit-pipeline
  • Max 3 words, hyphenated

B. Parameter Design

Parameter Types:

yaml
positional:
  - file_path (required, validated)
  - target (required, validated)

flags:
  --strict: boolean
  --format: enum[json, csv, xml]
  --output: file_path

options:
  --config: json_object
  --schema: file_path
  --model: enum[claude, gemini, codex]

Validation Schema:

typescript
interface CommandParameter {
  name: string
  type: 'string' | 'number' | 'boolean' | 'file_path' | 'enum'
  required: boolean
  default?: any
  validation?: RegExp | ((value: any) => boolean)
  description: string
  completion?: () => string[]  // Auto-complete options
}

C. Multi-Model Routing

Model Selection Flags:

bash
# Explicit model selection
/analyze src/ --model gemini-megacontext  # Large context
/prototype feature.spec --model codex-auto  # Rapid prototyping
/reason bug-report.md --model codex-reasoning  # Alternative view
/review code.js --model claude  # Best reasoning (default)

# Auto-select based on task
/analyze-large-codebase  # Auto-routes to gemini-megacontext
/rapid-prototype  # Auto-routes to codex-auto
/search-current-info  # Auto-routes to gemini-search

Step 3: Command Implementation Structure

Command Definition Template:

yaml
command:
  name: /command-name
  version: 1.0.0

  description: |
    Brief description of what this command does

  category: data | code | agent | workflow | multi-model

  parameters:
    - name: input
      type: file_path
      required: true
      validation: file_exists
      description: Input file to process

    - name: --strict
      type: boolean
      default: false
      description: Enable strict validation

    - name: --model
      type: enum
      options: [claude, gemini-megacontext, gemini-search, codex-auto]
      default: auto-select
      description: AI model to use

  routing:
    type: micro-skill | cascade | agent | multi-model
    target: skill-name | cascade-name | agent-name
    model_selection: auto | explicit

  binding:
    parameter_mapping:
      file: ${input}
      strictness: ${--strict}
      model: ${--model}

  output:
    format: json | text | file
    validation: schema | none

  examples:
    - command: /command-name input.json --strict
      description: Process input.json with strict validation

  composition:
    chainable: true
    pipe_output: stdout
    pipe_input: stdin

Step 4: Command Chaining & Composition

Pipeline Patterns:

bash
# Sequential pipeline
/extract data.json | /validate --strict | /transform --format csv > output.csv

# Parallel fan-out
/analyze src/ --parallel [/lint + /security-scan + /test-coverage] | /merge-reports

# Conditional branching
/validate input.json && /deploy-prod || /generate-error-report

# Multi-stage workflow
/audit-pipeline src/ \
  --phase theater-detection \
  --phase functionality-audit --model codex-auto \
  --phase style-audit \
  --output report.json

Composition Interface:

typescript
interface ChainableCommand {
  execute: (input: any) => Promise<CommandResult>
  pipe: (next: Command) => ChainableCommand
  parallel: (commands: Command[]) => ParallelCommand
  conditional: (condition: boolean, ifTrue: Command, ifFalse: Command) => ConditionalCommand
}

Step 5: Auto-Completion & Help

Completion System:

bash
# File path completion
/validate <TAB>  # Shows files matching pattern

# Parameter completion
/analyze --<TAB>  # Shows available flags

# Model completion
/analyze --model <TAB>  # Shows [claude, gemini-megacontext, codex-auto, ...]

# Command discovery
/<TAB>  # Shows all available commands by category

Help Generation:

markdown
/help command-name

Command: /validate-api
Version: 1.0.0
Category: Data Operations

Description:
  Validates API responses against OpenAPI schemas using specialist validation agent

Usage:
  /validate-api <file> [--schema <schema_file>] [--strict] [--model <model>]

Parameters:
  file              Path to API response file (required)
  --schema FILE     OpenAPI schema file (default: auto-detect)
  --strict          Enable strict validation mode
  --model MODEL     AI model [claude|gemini|codex] (default: auto)

Examples:
  /validate-api response.json
  /validate-api response.json --schema openapi.yaml --strict
  /validate-api response.json --model gemini-megacontext

Chains with:
  /extract-data → /validate-api → /transform-data

See also:
  /validate-csv, /validate-json, /agent-validator

Enhanced Command Templates

1. Data Processing Commands

Template:

yaml
command: /process-<datatype>
category: data
routing:
  type: micro-skill
  target: process-<datatype>

parameters:
  - input: file_path (required)
  - --format: enum[json, csv, xml]
  - --schema: file_path
  - --output: file_path
  - --model: enum[claude, gemini, codex]

examples:
  /extract-json data.json --schema schema.json
  /validate-csv data.csv --strict --output report.json
  /transform-xml data.xml --format json

Generated Commands:

  • /extract-json, /extract-csv, /extract-xml
  • /validate-json, /validate-csv, /validate-api
  • /transform-json, /transform-csv, /transform-xml

2. Code Operation Commands

Template:

yaml
command: /code-<operation>
category: code
routing:
  type: micro-skill | cascade
  target: code-<operation>

parameters:
  - path: file_path | directory (required)
  - --language: enum[python, javascript, typescript, ...]
  - --config: file_path
  - --fix: boolean (auto-fix issues)
  - --model: enum[claude, codex-auto]

examples:
  /lint-code src/ --language python --fix
  /test-coverage src/ --output coverage-report.json
  /refactor-imports src/ --model codex-auto

Generated Commands:

  • /lint-code, /lint-python, /lint-javascript
  • /test-coverage, /test-suite, /test-watch
  • /refactor-imports, /refactor-di, /refactor-patterns
  • /analyze-complexity, /analyze-security, /analyze-performance

3. Agent Invocation Commands

Template:

yaml
command: /agent-<specialty>
category: agent
routing:
  type: agent
  target: <specialty>-agent
  model_selection: auto

parameters:
  - task: string (required, detailed task description)
  - --context: file_path | directory
  - --depth: enum[shallow, normal, deep]
  - --model: enum[claude, gemini, codex]

examples:
  /agent-rca "Debug intermittent timeout in API" --context src/api/
  /agent-reviewer src/feature.js --depth deep
  /agent-architect "Design user authentication system" --context docs/

Generated Commands:

  • /agent-rca → Root Cause Analyzer
  • /agent-reviewer → Code Reviewer
  • /agent-architect → System Architect
  • /agent-security → Security Auditor
  • /agent-performance → Performance Optimizer

4. Multi-Model Commands

Template:

yaml
command: /<model>-<capability>
category: multi-model
routing:
  type: multi-model
  target: <model>-cli
  model: <model>

parameters:
  - task: string (required)
  - --context: file_path | directory
  - --output: file_path

examples:
  /gemini-megacontext "Analyze entire 30K line codebase" --context src/
  /gemini-search "What are React 19 breaking changes?"
  /gemini-media "Generate architecture diagram" --output diagram.png
  /codex-auto "Prototype user auth feature" --context spec.md
  /codex-reasoning "Alternative algorithm for sorting" --context src/sort.js

Generated Commands:

  • /gemini-megacontext → 1M token context analysis
  • /gemini-search → Real-time web information
  • /gemini-media → Image/video generation
  • /gemini-extensions → Figma, Stripe, Postman integration
  • /codex-auto → Full Auto sandboxed prototyping
  • /codex-reasoning → GPT-5-Codex alternative reasoning
  • /claude-reason → Best overall reasoning (default)

5. Workflow/Cascade Commands

Template:

yaml
command: /<workflow-name>
category: workflow
routing:
  type: cascade
  target: <workflow-name>-cascade

parameters:
  - target: file_path | directory (required)
  - --phase: enum[all, phase1, phase2, phase3]
  - --parallel: boolean (enable parallel execution)
  - --model: enum[auto, claude, gemini, codex]
  - --output: file_path

examples:
  /audit-pipeline src/ --output audit-report.json
  /quality-check src/ --parallel --model auto
  /deploy-prod --phase all --output deployment-log.txt

Generated Commands:

  • /audit-pipeline → theater → functionality → style
  • /quality-check → [lint + security + coverage] → report
  • /deploy-prod → validate → test → build → deploy
  • /modernize-legacy → analyze → refactor → test → document

Integration with Existing Skills

Command Catalog for Current Skills (14 Total)

Audit Skills (4 commands):

bash
/theater-detect src/          # Theater detection audit
/functionality-audit src/     # Functionality audit with Codex iteration
/style-audit src/             # Style and quality audit
/audit-pipeline src/          # All 3 phases sequentially

Multi-Model Skills (7 commands):

bash
/gemini-megacontext "task"    # 1M token context
/gemini-search "query"        # Real-time web info
/gemini-media "description"   # Generate images/videos
/gemini-extensions "task"     # Figma, Stripe, etc.
/codex-auto "task"            # Full Auto prototyping
/codex-reasoning "problem"    # GPT-5-Codex alternative view
/multi-model "task"           # Intelligent orchestrator

Root Cause Analysis (1 command):

bash
/agent-rca "problem"          # Root cause analysis agent

Three-Tier Architecture (2 commands):

bash
/create-micro-skill "task"    # Create new micro-skill
/create-cascade "workflow"    # Create new cascade

Command Composition Examples

Example 1: Complete Quality Pipeline:

bash
# Sequential quality checks with multi-model routing
/audit-pipeline src/ \
  --phase theater-detection \
  --phase functionality-audit --model codex-auto \
  --phase style-audit --model claude \
  --output quality-report.json

Example 2: Root Cause + Fix Workflow:

bash
# Analyze problem, then auto-fix with Codex
/agent-rca "Intermittent timeout in API" --context src/api/ | \
/codex-auto "Fix identified root cause" --sandbox true

Example 3: Research + Prototype + Test:

bash
# Multi-model cascade
/gemini-search "Best practices for React 19" | \
/codex-auto "Prototype React 19 feature using best practices" | \
/functionality-audit --model codex-auto

Example 4: Parallel Quality Checks:

bash
# Fan-out to multiple tools
/quality-check src/ --parallel [
  /theater-detect,
  /lint-code,
  /test-coverage,
  /analyze-security
] | /merge-reports --output comprehensive-report.json

Integration with Claude Code Command System

Command Registration

Auto-Registration Pattern:

bash
# On skill installation, auto-register commands
.claude/skills/*/SKILL.md → parse → generate → .claude/commands/<command>.md

# Command file format
.claude/commands/validate-api.md:
---
name: validate-api
binding: micro-skill:validate-api
---
Validate API responses against OpenAPI schemas.
Usage: /validate-api <file> [--schema <schema>] [--strict]

Command Discovery

Discovery Mechanism:

yaml
on_startup:
  - scan ~/.claude/skills/*/SKILL.md
  - scan .claude/skills/*/SKILL.md
  - parse metadata (name, inputs, category)
  - generate command definitions
  - register with Claude Code CLI
  - build auto-completion index

on_update:
  - watch for skill changes
  - regenerate affected commands
  - update completion index

Parameter Validation

Validation Pipeline:

typescript
interface ValidationPipeline {
  // Type checking
  validateTypes: (params: any) => ValidationResult

  // File existence
  validatePaths: (paths: string[]) => ValidationResult

  // Enum constraints
  validateEnums: (values: any) => ValidationResult

  // Custom validators
  validateCustom: (value: any, validator: Function) => ValidationResult

  // Aggregate results
  aggregate: () => ValidationResult
}

// Before command execution
const result = validate(command, parameters)
if (!result.valid) {
  throw new ValidationError(result.errors)
}

Command Chaining Patterns

Pattern 1: Sequential Pipeline

bash
# Data processing pipeline
/extract-json data.json | \
/validate-api --schema openapi.yaml | \
/transform-json --format csv | \
/generate-report --template summary

Pattern 2: Parallel Fan-Out

bash
# Parallel quality checks
/analyze src/ --parallel [
  /lint-code,
  /security-scan --deep,
  /test-coverage,
  /complexity-analysis
] | /merge-reports --format html

Pattern 3: Conditional Branching

bash
# Deploy based on quality
/validate-quality src/ && \
  /deploy-prod --environment production || \
  /generate-quality-report --notify team

Pattern 4: Iterative Refinement

bash
# Refactor until quality threshold met
while [[ $(quality-score) -lt 85 ]]; do
  /refactor-code src/ --model codex-auto
  /test-coverage src/
done

Pattern 5: Multi-Model Cascade

bash
# Research → Design → Implement → Test
/gemini-search "Best practices for feature X" | \
/agent-architect "Design feature X with best practices" | \
/codex-auto "Implement designed feature" | \
/functionality-audit --model codex-auto | \
/style-audit

Best Practices (Enhanced)

Command Design

  1. ✅ Use clear, consistent naming (verb-noun)
  2. ✅ Limit positional parameters (max 2-3)
  3. ✅ Provide sensible defaults
  4. ✅ Enable command chaining
  5. ✅ Include comprehensive help
  6. ✅ Support model selection for flexibility

Parameter Design

  1. ✅ Type-safe with validation
  2. ✅ Auto-completion enabled
  3. ✅ Required vs optional clearly marked
  4. ✅ Enum constraints for options
  5. ✅ File path validation

Integration Design

  1. ✅ Clean routing to skills/agents
  2. ✅ Standardized output formats
  3. ✅ Composable interfaces
  4. ✅ Error handling with clear messages
  5. ✅ Progress reporting for long operations

Working with Slash Command Encoder

Invocation: "Create slash commands for [skill/cascade/agent] with [parameters] that [composition pattern]"

The encoder will:

  1. Auto-discover all installed skills
  2. Design command naming and parameters
  3. Create validation schemas
  4. Generate command definitions
  5. Register with Claude Code CLI
  6. Build auto-completion index
  7. Produce comprehensive command catalog

Advanced Features:

  • Automatic skill discovery and catalog generation
  • Intelligent multi-model routing
  • Type-safe parameter validation
  • Command chaining and composition
  • Auto-completion for parameters
  • Comprehensive help generation
  • Integration with Claude Code CLI

Integration:

  • Works with micro-skill-creator for skill-to-command generation
  • Works with cascade-orchestrator for workflow commands
  • Works with multi-model system for AI routing
  • Works with audit-pipeline for quality commands
  • Works with root-cause-analyzer for debugging commands

Version 2.0 Enhancements:

  • Auto-discovery of all installed skills
  • Multi-model intelligent routing
  • Command chaining and composition patterns
  • Type-safe parameter validation
  • Auto-completion system
  • Comprehensive command catalog generation
  • Integration with Gemini/Codex CLIs
  • Enhanced help and documentation generation

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