Context Cascade - Nested Plugin Architecture for Claude Code Official Claude Code Plugin | Version 3.1.0 | Last updated: 2026-01-09 (see docs/COMPONENT-COUNTS.json for source counts) Context-saving nested architecture: Playbooks -> Skills -> Agents -> Commands. Load only what you need, saving 90%+ context space.
Resources
5Install
npx skillscat add dnyoussef/context-cascade/skills-operations-github-project-management Install via the SkillsCat registry.
/============================================================================/
/* SKILL SKILL :: VERILINGUA x VERIX EDITION /
/============================================================================*/
name: SKILL
version: 1.0.0
description: |
[assert|neutral] SKILL skill for operations workflows [ground:given] [conf:0.95] [state:confirmed]
category: operations
tags:
- general
author: system
cognitive_frame:
primary: aspectual
goal_analysis:
first_order: "Execute SKILL workflow"
second_order: "Ensure quality and consistency"
third_order: "Enable systematic operations processes"
/----------------------------------------------------------------------------/
/* S0 META-IDENTITY /
/----------------------------------------------------------------------------*/
[define|neutral] SKILL := {
name: "SKILL",
category: "operations",
version: "1.0.0",
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: ["SKILL", "operations", "workflow"],
context: "user needs SKILL capability"
} [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/
/* S3 CORE CONTENT /
/----------------------------------------------------------------------------*/
GitHub Project Management
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
Overview
A comprehensive skill for managing GitHub projects using AI swarm coordination. This skill combines intelligent issue management, automated project board synchronization, and swarm-based coordination for efficient project delivery.
Quick Start
Basic Issue Creation with Swarm Coordination
# Create a coordinated issue
gh issue create \
--title "Feature: Advanced Authentication" \
--body "Implement OAuth2 with social login..." \
--label "enhancement,swarm-ready"
# Initialize swarm for issue
npx claude-flow@alpha hooks pre-task --description "Feature implementation"Project Board Quick Setup
# Get project ID
PROJECT_ID=$(gh project list --owner @me --format json | \
jq -r '.projects[0].id')
# Initialize board sync
npx ruv-swarm github board-init \
--project-id "$PROJECT_ID" \
--sync-mode "bidirectional"Core Capabilities
1. Issue Management & Triage
Automated Issue Creation
Single Issue with Swarm Coordination
// Initialize issue management swarm
mcp__claude-flow__swarm_init { topology: "star", maxAgents: 3 }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "Issue Coordinator" }
mcp__claude-flow__agent_spawn { type: "researcher", name: "Requirements Analyst" }
mcp__claude-flow__agent_spawn { type: "coder", name: "Implementation Planner" }
// Create comprehensive issue
mcp__github__create_issue {
owner: "org",
repo: "repository",
title: "Integration Review: Complete system integration",
body: `## 🔄 Integration Review
### Overview
Comprehensive review and integration between components.
### Objectives
- [ ] Verify dependencies and imports
- [ ] Ensure API integration
- [ ] Check hook system integration
- [ ] Validate data systems alignment
### Swarm Coordination
This issue will be managed by coordinated swarm agents for optimal progress tracking.`,
labels: ["integration", "review", "enhancement"],
assignees: ["username"]
}
// Set up automated tracking
mcp__claude-flow__task_orchestrate {
task: "Monitor and coordinate issue progress with automated updates",
strategy: "adaptive",
priority: "medium"
}Batch Issue Creation
# Create multiple related issues using gh CLI
gh issue create \
--title "Feature: Advanced GitHub Integration" \
--body "Implement comprehensive GitHub workflow automation..." \
--label "feature,github,high-priority"
gh issue create \
--title "Bug: Merge conflicts in integration branch" \
--body "Resolve merge conflicts..." \
--label "bug,integration,urgent"
gh issue create \
--title "Documentation: Update integration guides" \
--body "Update all documentation..." \
--label "documentation,integration"Issue-to-Swarm Conversion
Transform Issues into Swarm Tasks
# Get issue details
ISSUE_DATA=$(gh issue view 456 --json title,body,labels,assignees,comments)
# Create swarm from issue
npx ruv-swarm github issue-to-swarm 456 \
--issue-data "$ISSUE_DATA" \
--auto-decompose \
--assign-agents
# Batch process multiple issues
ISSUES=$(gh issue list --label "swarm-ready" --json number,title,body,labels)
npx ruv-swarm github issues-batch \
--issues "$ISSUES" \
--parallel
# Update issues with swarm status
echo "$ISSUES" | jq -r '.[].number' | while read -r num; do
gh issue edit $num --add-label "swarm-processing"
doneIssue Comment Commands
Execute swarm operations via issue comments:
<!-- In issue comment -->
/swarm analyze
/swarm decompose 5
/swarm assign @agent-coder
/swarm estimate
/swarm startAutomated Issue Triage
Auto-Label Based on Content
// .github/swarm-labels.json
{
"rules": [
/*----------------------------------------------------------------------------*/
/* 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/operations/SKILL/{project}/{timestamp}",
store: ["executions", "decisions", "patterns"],
retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]
[define|neutral] MEMORY_TAGGING := {
WHO: "SKILL-{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] <promise>SKILL_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]