agent-penetration-tester
Expert penetration tester specializing in ethical hacking, vulnerability assessment, and security testing. Masters offensive security techni
Open source Repository Open in the app JSON README (API)
About
Expert penetration tester specializing in ethical hacking, vulnerability assessment, and security testing. Masters offensive security techniques, exploit development, and comprehensive security assess
Details
- Kind
- Agent skills
- Topic
- Developer tools
- Publisher
- tony363
- Origin
- majiayu
- Category
- ferramentas
- Stars
- 10
- Forks
- 2
- Open pull requests
- 7
- Last push
- 2026-06-29T09:15:00Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-30 15:40:14
- Updated
- 2026-08-30 15:40:14
- Origin id
tony363/superclaude/.claude/skills/DEPRECATED/agent-penetration-tester@main
README
# SuperClaude Framework
<p align="center">
<img src="https://img.shields.io/badge/version-7.0.0-blue" alt="Version">
<img src="https://img.shields.io/badge/agents-19-orange" alt="Agents">
<img src="https://img.shields.io/badge/skills-43-green" alt="Skills">
<img src="https://img.shields.io/badge/commands-27-purple" alt="Commands">
<img src="https://img.shields.io/badge/modes-6-teal" alt="Modes">
<img src="https://img.shields.io/badge/python_core-2800_lines-red" alt="Core">
<img src="https://img.shields.io/badge/license-MIT-lightgrey" alt="License">
<a href="https://sonarcloud.io/summary/new_code?id=tony363_superclaude"><img src="https://sonarcloud.io/api/project_badges/measure?project=tony363_superclaude&metric=alert_status" alt="Quality Gate"></a>
<a href="https://sonarcloud.io/summary/new_code?id=tony363_superclaude"><img src="https://sonarcloud.io/api/project_badges/measure?project=tony363_superclaude&metric=coverage" alt="Coverage"></a>
</p>
**A config-first meta-framework for Claude Code that provides 19 specialized agent personas (5 core + 7 traits + 7 extensions), 27 structured commands, 43 skills, and comprehensive MCP integration with quality-driven iterative workflows.**
SuperClaude transforms Claude Code into a powerful development platform with specialized agent prompts, signal-based loop orchestration, and multi-model consensus capabilities. The core interface is markdown and YAML configuration files, with a Python orchestration layer for advanced workflows including quality gates, termination detection, and PAL MCP integration.
---
## Table of Contents
- [Overview](#overview)
- [Key Features](#key-features)
- [Architecture](#architecture)
- [Core Orchestration Layer](#core-orchestration-layer)
- [Core API Reference](#core-api-reference)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Dashboard](#dashboard)
- [Agent System](#agent-system)
- [Command System](#command-system)
- [Loop Orchestration](#loop-orchestration)
- [MCP Integrations](#mcp-integrations)
- [Skills System](#skills-system)
- [Quality System](#quality-system)
- [Quality Gates](#quality-gates)
- [Metrics System](#metrics-system)
- [Type System](#type-system)
- [Configuration](#configuration)
- [Directory Structure](#directory-structure)
- [Creating Custom Agents](#creating-custom-agents)
- [Contributing](#contributing)
---
## Overview
SuperClaude is a meta-prompt framework that enhances Claude Code with:
- **19 Specialized Agents**: 5 core + 7 composable traits + 7 domain extensions (tiered architecture)
- **43 Active Skills**: 8 agent personas + 26 command workflows + 6 GitNexus code intelligence + 3 utility skills
- **27 Structured Commands**: analyze, implement, test, design, document, and more
- **6 Framework Modes**: normal, brainstorming, introspection, task_management, token_efficiency, orchestration
- **MCP Integration**: PAL (11 tools), Rube (500+ apps via Composio, including web search)
- **Quality Gates**: KISS, Purity, SOLID, and Let It Crash validators with iterative quality loop
- **Core Orchestration**: ~2,800 lines Python for loop management, PAL integration, and skill learning
- **Signal-Based Architecture**: Structured communication between components
- **Metrics System**: Callback-based operational metrics with Prometheus/StatsD integration
---
## Key Features
### Config-First Hybrid Architecture
SuperClaude v7.0.0 is a **config-first hybrid framework**:
- **Markdown Agent Personas**: Each agent is a self-contained markdown file with YAML frontmatter
- **YAML Configuration**: 6 configuration files for agents, commands, quality, models, MCP, and framework settings
- **Python Orchestration**: Loop orchestrator, quality assessment, PAL integration, and skill learning
- **Portable**: Works with any Claude Code instance
- **Extensible**: Add agents by creating markdown files
### Why Config-First Hybrid?
| Benefit | Description |
|---------|-------------|
| **Simplicity** | Core interface is markdown/YAML files |
| **Portability** | Works with any Claude Code instance |
| **Extensibility** | Add agents by creating markdown files |
| **Maintainability** | Prompts are easy to refine and version |
| **Version Control** | Easy to diff and review prompt changes |
| **Advanced Workflows** | Python orchestration for quality gates and loop control |
| **Safety** | Hard limits on iterations, termination detection |
### Tiered Agent Architecture
19 agents organized in a tiered system for composable expertise:
| Tier | Count | Purpose | Examples |
|------|-------|---------|----------|
| **Core** | 5 | High-priority generalists | architect, developer, optimizer, guardian, communicator |
| **Traits** | 7 | Composable modifiers | security-first, performance-first, test-driven, minimal-changes, principles-enforced, mcp-pal-enabled, mcp-rube-enabled |
| **Extensions** | 7 | Domain specialists | typescript-react-expert, python-expert, golang-expert, rust-expert, kubernetes-specialist, data-engineer, ml-engineer |
**Why Tiered?** The v7 architecture provides a lean, composable system. Core agents handle most tasks, traits modify behavior (e.g., `@architect +security-first`), and extensions provide deep domain expertise when needed.
### Command System
27 structured commands with consistent patterns:
```
/sc:analyze - Static analysis, security review, performance bottlenecks
/sc:implement - Feature implementation with quality gates and loop support
/sc:test - Test execution, coverage analysis, and test generation
/sc:design - Architecture and system design with ADRs
/sc:document - Documentation generation and maintenance
/sc:brainstorm - Creative ideation and exploration
/sc:explain - Educational explanations and learning
/sc:improve - Code improvement, refactoring, optimization
/sc:build - Build system and compilation workflows
/sc:git - Git operations, smart commits, branch management
/sc:workflow - Multi-step task orchestration
/sc:estimate - Effort estimation and planning
/sc:cicd-setup - CI/CD workflow and pre-commit generation
/sc:readme - Auto-update README.md from git diff with PAL consensus
/sc:pr-fix - Create PR and iteratively fix CI failures
/sc:pr-check - Pre-PR local CI validation with auto-fix
/sc:log-fix - Log analysis and iterative bug fixing
/sc:e2e - E2E testing (Playwright, Cypress, Selenium)
/sc:tdd - Test-driven development with Red-Green-Refactor
/sc:principles - KISS, Purity, SOLID, Let It Crash validation
/sc:worktree - Git worktree management for parallel development
/sc:mcp - MCP orchestration hub (PAL + Rube)
/sc:code-review - Multi-model consensus code review
/sc:research - Deep research with web search + consensus
/sc:eda - Exploratory data analysis and visualization
/sc:evaluate - LLM pipeline evaluation with judge scoring
/sc:push - Multi-remote git push with selective filtering
```
---
## Architecture
### How SuperClaude Works
```mermaid
flowchart TB
subgraph User["User Layer"]
REQ["User Request<br/>/sc:implement --loop"]
end
subgraph Runtime["Claude Code Runtime"]
CLAUDE["CLAUDE.md<br/>Master System Prompt"]
SELECT["Agent Selection<br/>Semantic Matching + Weights"]
EXEC["Task Execution"]
end
subgraph Config["Configuration Layer"]
AGENTS[("agents/<br/>5 core + 7 traits + 7 ext")]
COMMANDS[("commands/<br/>16 templates")]
SKILLS[(".claude/skills/<br/>38 skills")]
YAML[("config/<br/>6 YAML files")]
end
subgraph Core["Core Orchestration (Python)"]
direction TB
LOOP["LoopOrchestrator<br/>Max 5 iterations"]
QUALITY["QualityAssessor<br/>9 dimensions"]
TERM["Termination Detection<br/>5 conditions"]
PALINT["PAL Integration<br/>Signal generation"]
LEARN["Skill Learning<br/>Pattern extraction"]
end
subgraph MCP["MCP Integrations"]
PAL["PAL MCP<br/>11 tools"]
RUBE["Rube MCP<br/>500+ apps"]
end
REQ --> CLAUDE
CLAUDE --> SELECT
SELECT --> AGENTS
SELECT --> COMMANDS
SELECT --> EXEC
EXEC --> SKILLS
EXEC <--> Core
LOOP --> QUALITY
QUALITY --> TERM
TERM --> PALINT
PALINT <--> PAL
EXEC <--> RUBE
Config --> Runtime
style User fill:#e8f5e9
style Runtime fill:#e3f2fd
style Config fill:#fff3e0
style Core fill:#fce4ec
style MCP fill:#f3e5f5
```
### File-Based Configuration
SuperClaude uses a layered file-based architecture:
1. **CLAUDE.md** - Master system prompt loaded by Claude Code
2. **agents/index.yaml** - Agent registry with triggers, categories, and selection weights
3. **agents/core/*.md** - 5 core agent persona prompts
4. **agents/traits/*.md** - 7 composable behavior modifier prompts
5. **agents/extensions/*.md** - 7 domain specialist prompts
6. **commands/index.yaml** - Command registry with flags and aliases
7. **commands/*.md** - 16 command templates
8. **config/*.yaml** - 6 configuration files
9. **core/*.py** - Python orchestration modules
10. **mcp/*.md** - MCP integration guides
11. **.claude/skills/** - 43 Claude Code skills
---
## Core Orchestration Layer
SuperClaude v7.0.0 includes a Python orchestration layer in `core/` for advanced workflows:
### Modules
| Module | Lines | Purpose |
|--------|-------|---------|
| `loop_orchestrator.py` | ~410 | Manages iterative improvement with quality gates |
| `quality_assessment.py` | ~265 | 9-dimension quality scoring with evidence collection |
| `pal_integration.py` | ~175 | PAL MCP signal generation (review, debug, validation) |
| `types.py` | ~150 | Core type definitions (TerminationReason, LoopConfig, etc.) |
| `metrics.py` | ~225 | Callback-based metrics protocol and emitters |
| `skill_learning_integration.py` | ~550 | Skill extraction from successful executions |
| `skill_persistence.py` | ~980 | Skill storage, retrieval, and promotion |
### LoopOrchestrator
The `LoopOrchestrator` class manages the `--loop` flag behavior:
```mermaid
stateDiagram-v2
[*] --> Initialize
state "Loop Execution" as Loop {
[*] --> ExecuteSkill
ExecuteSkill --> CollectEvidence
CollectEvidence --> AssessQuality
AssessQuality --> CheckThreshold
state CheckThreshold <<choice>>
CheckThreshold --> Success: score >= 70
CheckThreshold --> CheckTermination: score < 70
state CheckTermination <<choice>>
CheckTermination --> MaxIterations: iteration >= max
CheckTermination --> Timeout: wall-clock exceeded
CheckTermination --> Error: skill execution failure
CheckTermination --> Continue: OK to iterate
Continue --> PALReview: Generate signal
PALReview --> PrepareNext
PrepareNext --> ExecuteSkill
}
Initialize --> Loop
Success --> FinalValidation: PAL final signal
MaxIterations --> [*]
Timeout --> [*]
Error --> [*]
FinalValidation --> [*]
note right of Initialize
Triggered by /sc:implement --loop
end note
```
**Key Features:**
- **Safety**: Hard maximum of 5 iterations (cannot be overridden via `hard_max_iterations`)
- **Quality-Driven**: Stops when score >= 70 (configurable threshold)
- **PAL Integration**: Generates review signals within loop for external validation
- **Signal-Based**: Communicates with Claude Code via structured signals
- **Evidence Collection**: Tracks changes, tests, lint results, and file modifications
### Signal Architecture
The orchestrator uses structured signals for component communication:
```python
# Skill invocation signal
{
"action": "execute_skill",
"skill": "sc-implement",
"parameters": {
"task": "description",
"improvements_needed": [...],
"iteration": 2,
"focus": "remediation" # "implementation" for iter 0
},
"collect": ["changes", "tests", "lint", "changed_files"],
"context": {...}
}
```
---
## Core API Reference
Complete API documentation for the SuperClaude Python orchestration layer (`core/` module).
### Module Exports
```python
from core import (
# Types
TerminationReason, # Enum: 5 loop termination conditions
LoopConfig, # Dataclass: Loop configuration
LoopResult, # Dataclass: Final loop result
IterationResult, # Dataclass: Single iteration result
QualityAssessment, # Dataclass: Quality score + improvements
# Classes
QualityAssessor, # Quality scoring wrapper
PALReviewSignal, # PAL MCP signal generator
LoopOrchestrator, # Main loop controller
)
```
### LoopOrchestrator
The main controller for `--loop` functionality.
```python
class LoopOrchestrator:
"""
Lightweight agentic loop orchestrator.
Thread Safety: NOT thread-safe. Create new instance per task/thread.
"""
def __init__(
self,
config: Optional[LoopConfig] = None,
logger: Optional[logging.Logger] = None,
metrics_emitter: Optional[MetricsEmitter] = None,
) -> None:
"""
Initialize the loop orchestrator.
Args:
config: Loop configuration (defaults to LoopConfig())
logger: Logger instance for structured logging
metrics_emitter: Callback for operational metrics
"""
def run(
self,
initial_context: dict[str, Any],
skill_invoker: Callable[[dict[str, Any]], dict[str, Any]],
) -> LoopResult:
"""
Execute the agentic loop.
Args:
initial_context: Task context with:
- task: Description of what to implement
- improvements_needed: Initial improvements (optional)
- changed_files: Already modified files (optional)
skill_invoker: Function that invokes Skills via Claude Code
Should return dict with: changes, tests, lint, changed_files
Returns:
LoopResult with final output, assessment, and history
"""
```
**Usage Example:**
```python
from core import LoopOrchestrator, LoopConfig
config = LoopConfig(
max_iterations=3,
quality_threshold=70.0,
pal_review_enabled=True,
)
orchestrator = LoopOrchestrator(config)
def my_skill_invoker(context):
return {
"changes": [...],
"tests": {"ran": True, "passed": 10, "failed": 0},
"lint": {"ran": True, "errors": 0},
"changed_files": ["src/module.py"],
}
result = orchestrator.run(
initial_context={"task": "Implement user authentication"},
skill_invoker=my_skill_invoker,
)
print(f"Final score: {result.final_assessment.overall_score}")
print(f"Termination: {result.termination_reason.value}")
```
### QualityAssessor
Wraps `evidence_gate.py` for quality scoring.
```python
class QualityAssessor:
def __init__(self, threshold: float = 70.0) -> None
def assess(self, context: dict[str, Any]) -> QualityAssessment
```
**Scoring Breakdown (Fallback Mode):**
| Component | Points | Condition |
|-----------|--------|-----------|
| File changes | 30 | Any changes detected |
| Tests executed | 25 | Tests ran |
| Tests passing | 20 | All tests pass |
| Lint clean | 15 | No lint errors |
| Coverage | 10 | 80%+ coverage |
### PALReviewSignal
Generates signals for PAL MCP invocation.
```python
class PALReviewSignal:
TOOL_CODEREVIEW = "mcp__pal__codereview"
TOOL_DEBUG = "mcp__pal__debug"
TOOL_THINKDEEP = "mcp__pal__thinkdeep"
TOOL_CONSENSUS = "mcp__pal__consensus"
@staticmethod
def generate_review_signal(
iteration: int,
changed_files: list[str],
quality_assessment: QualityAssessment,
model: str = "gpt-5",
review_type: str = "auto", # auto/quick/full
) -> dict[str, Any]
@staticmethod
def generate_debug_signal(
iteration: int,
termination_reason: str,
score_history: list[float],
model: str = "gpt-5",
) -> dict[str, Any]
@staticmethod
def generate_final_validation_signal(
changed_files: list[str],
quality_assessment: QualityAssessment,
iteration_count: int,
model: str = "gpt-5",
) -> dict[str, Any]
```
### Helper Functions
```python
# Create skill invocation signal
def create_skill_invoker_signal(context: dict[str, Any]) -> dict[str, Any]
# Merge PAL feedback into context
def incorporate_pal_feedback(
context: dict[str, Any],
pal_result: dict[str, Any],
) -> dict[str, Any]
# Convenience function for quality assessment
def assess_quality(
context: dict[str, Any],
threshold: float = 70.0
) -> QualityAssessment
```
---
## Installation
### Option 1: Clone Repository
```bash
git clone https://github.com/Tony363/SuperClaude.git
cd SuperClaude
```
### Option 2: Add as Git Submodule
```bash
git submodule add https://github.com/Tony363/SuperClaude.git SuperClaude
```
### Setup Claude Code
Add to your project's `.claude/settings.json`:
```json
{
"systemPromptFiles": ["SuperClaude/CLAUDE.md"]
}
```
Or copy `CLAUDE.md` to your project root.
### Optional: Python Dependencies
For advanced workflows using the core orchestration layer:
```bash
pip install -e .
```
### Security Layer: Sondera Integration
SuperClaude supports optional integration with **Sondera**, a security harness that validates Claude Code tool executions using LLM-powered policy enforcement and Cedar policy language.
**Important:** Sondera is **not bundled by default**. The standard installation (above) does not include it. Sondera must be explicitly installed when needed for production or compliance requirements.
📖 **[Full integration guide and architecture →](SONDERA_INTEGRATION.md)**
#### Default Install (Development)
The standard installation provides full SuperClaude functionality without security restrictions. This is ideal for:
- Local development and experimentation
- Trusted single-user environments
- Research and learning
- Scenarios where performance is critical
#### Production Setup (With Sondera)
For production deployments or high-security environments, install with Sondera:
```bash
./install-with-sondera.sh
```
**What Sondera provides:**
- Pre-execution validation of core Claude Code tools (Read, Write, Edit, Bash, etc.)
- LLM-powered intent analysis (ministral-3:14b-cloud)
- Safety validation (gpt-oss-safeguard:20b)
- Cedar policy enforcement engine
- Audit logging of all validated operations
**Prerequisites:**
- Rust toolchain (`cargo`)
- Ollama with models: `ministral-3:14b-cloud`, `gpt-oss-safeguard:20b` (~30GB)
- Cedar policy engine (embedded in harness)
- Unix socket support (Linux/macOS only; **Windows not supported**)
**Testing the integration:**
```bash
./test-sondera-integration.sh
```
**Tradeoffs:**
| Aspect | Without Sondera | With Sondera |
|--------|----------------|--------------|
| **Installation** | Simple (git clone) | Complex (Rust, Ollama, models) |
| **Performance** | Fast | Slower (LLM validation overhead) |
| **Security** | Trust-based | Policy-enforced |
| **Use Case** | Development, research | Production, compliance |
| **Maintenance** | Low | Higher (harness service) |
| **Platform** | Cross-platform | Linux/macOS only |
**Disabling Sondera:**
```bash
# Remove hook configuration
rm .claude/settings.local.json
# Stop harness service
# If using systemd (Linux):
systemctl --user stop sondera-harness
# If launched manually:
pkill -f sondera-harness
```
**Failure Mode:** When the harness service is unavailable, tool calls fail-closed (blocked by default) for safety. Configure fail-open mode in the harness settings if needed.
---
## Quick Start
### 1. Natural Language
```
User: Help me debug this authentication issue
Claude: [Selects communicator agent, applies debugging methodology]
```
### 2. Using Commands
```
User: /sc:analyze src/auth/
Claude: [Runs comprehensive static analysis, security review, quality assessment]
```
### 3. Specifying Agents
```
User: @python-expert Review this Flask application
Claude: [Uses Python expert persona with framework-specific knowledge]
```
### 4. Iterative Improvement
```
User: /sc:implement new user registration --loop
Claude: [Implements with quality gates, iterates until score >= 70 or max 5 iterations]
```
### 5. Using MCP Tools
With PAL MCP configured:
```
User: Use PAL to review this code
Claude: [Invokes mcp__pal__codereview for multi-model code review]
```
---
## Dashboard
SuperClaude includes a native desktop dashboard for visualizing and controlling the agentic framework.
### Features
- **Feature Inventory** - Browse all 19 agents, 24 commands, 38 skills, and 6 behavioral modes
- **Live Monitor** - Real-time execution tracking with event streaming, heartbeat indicator, and quality score visualization
- **Execution Control** - Start, stop, pause, and resume executions with configurable parameters; expandable detail panel with 5 tabs (Run Instructions, Execution Log, Files Changed, Quality Breakdown, Execution Tree)
- **Execution Tree** - Visual tree of iterations, tool calls, and subagent spawns built from streaming events
- **Diff Viewer** - Inline diff display for Edit/Write tool invocations showing before/after changes
- **Historical Metrics** - View past session data, event timelines, and performance trends
### Quick Start
```bash
# Terminal 1: Start the daemon
cargo run -p superclaude-daemon
# Terminal 2: Launch the dashboard
cd crates/dashboard
cargo tauri dev
```
The dashboard window will open showing the Feature Inventory page. Navigate using the sidebar to access Monitor, Control, and History views.
### Building for Production
```bash
cd crates/dashboard
cargo tauri build
# Output packages:
# - Debian/Ubuntu: target/release/bundle/deb/superclaude-dashboard_*.deb
# - Universal Linux: target/release/bundle/appimage/superclaude-dashboard_*.AppImage
```
### System Requirements
- **OS**: Linux (tested on Manjaro/Arch, Ubuntu 22.04+)
- **Dependencies**: webkit2gtk-4.1, libappindicator-gtk3, librsvg
- **Runtime**: SuperClaude daemon running on port 50051
See [Dashboard README](crates/dashboard/README.md) for detailed usage guide.
---
## Agent System
### Core Agents (5)
High-priority generalists for common tasks:
| Agent | Triggers | Purpose |
|-------|----------|---------|
| architect | architecture, design, system, backend, frontend, api, database, infrastructure, ci/cd, deploy, fullstack | System design spanning architecture, infrastructure, backend, frontend, and deployment |
| developer | implement, create, build, develop, task, general, requirement, spec | Versatile implementation and multi-domain coordination |
| optimizer | optimize, performance, slow, refactor, improve, clean | Performance optimization and code quality improvement |
| guardian | security, vulnerability, test, validate, quality, qa | Security analysis and comprehensive testing |
| communicator | document, docs, explain, learn, tutorial, debug, investigate | Documentation, teaching, and debugging explanations |
### Traits (7)
Composable behavior modifiers that can be combined with any agent:
| Trait | Purpose | Example Usage |
|-------|---------|---------------|
| security-first | Prioritize security considerations | `@architect +security-first` |
| performance-first | Focus on optimization | `@optimizer +performance-first` |
| test-driven | Enforce TDD practices | `@developer +test-driven` |
| minimal-changes | Reduce change scope | `@optimizer +minimal-changes` |
| principles-enforced | Enforce SOLID, Let It Crash, KISS, Pure Functions | `@architect +principles-enforced` |
| mcp-pal-enabled | Enables PAL MCP tools (consensus, debug, codereview) | `@guardian +mcp-pal-enabled` |
| mcp-rube-enabled | Enables Rube MCP tools (500+ app integrations) | `@architect +mcp-rube-enabled` |
### Extensions (7)
Domain-specialist agents for deep expertise:
| Extension | Triggers | Specialty |
|-----------|----------|-----------|
| typescript-react-expert | typescript, ts, node, react, hooks, nextjs | TypeScript and React with strong typing |
| python-expert | python, django, fastapi, pytest | Python development with modern patterns |
| golang-expert | go, golang, goroutine | Go development with idiomatic patterns |
| rust-expert | rust, cargo, ownership | Rust with ownership patterns |
| kubernetes-specialist | kubernetes, k8s, helm, pod | Kubernetes orchestration |
| data-engineer | data, pipeline, etl, spark, kafka | Data engineering and pipelines |
| ml-engineer | ml, machine learning, pytorch, tensorflow | Machine learning systems |
### Agent Selection Algorithm
Agents are selected using weighted semantic matching:
```mermaid
flowchart TD
START["User Request"] --> PARSE["Parse Keywords & Context"]
PARSE --> TRIGGER{"Exact Trigger<br/>Match?"}
TRIGGER -->|"Yes (0.35)"| CORE{"Core<br/>Agent?"}
TRIGGER -->|"No"| CATEGORY{"Category<br/>Match?"}
CORE -->|"Yes"| USE_CORE["Load Core Agent<br/>(5 available)"]
CORE -->|"No"| USE_EXT["Load Trait/Extension<br/>(14 available)"]
CATEGORY -->|"Yes (0.25)"| CAT_SELECT["Select from Category"]
CATEGORY -->|"No"| DESC{"Description<br/>Match?"}
DESC -->|"Yes (0.20)"| DESC_SELECT["Score by Description"]
DESC -->|"No"| TOOL{"Tool<br/>Match?"}
TOOL -->|"Yes (0.20)"| TOOL_SELECT["Match Required Tools"]
TOOL -->|"No"| FILE{"File<br/>Context?"}
FILE -->|".py files"| PYTHON["Python Expert"]
FILE -->|".ts/.js"| TS["TypeScript Pro"]
FILE -->|".go files"| GO["Golang Pro"]
FILE -->|".rs files"| RUST["Rust Engineer"]
FILE -->|"Other"| GENERAL["General Purpose"]
USE_CORE --> EXEC["Execute Task"]
USE_EXT --> EXEC
CAT_SELECT --> EXEC
DESC_SELECT --> EXEC
TOOL_SELECT --> EXEC
PYTHON --> EXEC
TS --> EXEC
GO --> EXEC
RUST --> EXEC
GENERAL --> EXEC
style START fill:#4caf50,color:#fff
style EXEC fill:#2196f3,color:#fff
```
**Selection Weights (from `select_agent.py`):**
```yaml
selection:
weights:
keyword_match: 0.35 # Keyword/trigger matching (highest priority)
category_match: 0.25 # Domain/category alignment
task_match: 0.20 # Task text matching
file_patterns: 0.10 # File pattern matching
priority_bonus: 0.10 # Tier priority bonus
thresholds:
minimum_score: 0.3 # Minimum match score
confidence_levels:
excellent: 0.7 # >= 0.7 score
high: 0.5 # >= 0.5 score
medium: 0.3 # >= 0.3 score
```
---
## Command System
Commands provide structured execution patterns with optional quality gates.
### Command Format
Each command is a markdown file with YAML frontmatter:
```markdown
---
name: implement
description: Feature implementation with quality gates
aliases: [build, create, develop]
flags: [--loop, --tests, --docs, --pal-review, --consensus]
evidence_required: true
---
# Implement Command
[Structured implementation methodology...]
```
### Command Execution Flow
```mermaid
sequenceDiagram
participant U as User
participant C as Claude Code
participant CMD as Command Template
participant A as Agent Persona
participant O as LoopOrchestrator
participant Q as QualityAssessor
participant P as PAL MCP
U->>C: /sc:implement feature --loop
C->>CMD: Load implement.md template
CMD->>A: Activate relevant personas
C->>O: Initialize loop (max 5 iter)
rect rgb(230, 245, 255)
note over O,P: Iterative Improvement Loop
loop Iteration 0..N (max 5)
O->>A: Execute with context
A-->>O: Return evidence
O->>Q: Assess quality
Q-->>O: Score + improvements
alt Score >= 70
O-->>C: QUALITY_MET
else Oscillation/Stagnation
O->>P: Generate debug signal
O-->>C: Terminate
else Continue
O->>P: Generate review signal
P-->>O: PAL feedback
O->>A: Next iteration
end
end
end
O->>P: Final validation signal
P-->>O: Validation result
O-->>C: LoopResult
C->>U: Implementation + quality report
```
### Available Commands
| Command | Aliases | Evidence | Key Flags |
|---------|---------|----------|-----------|
| analyze | analyse, check, inspect | No | --security, --deep, --performance |
| implement | build, create, develop | Yes | --loop, --pal-review, --consensus, --tests |
| test | verify, check | Yes | --coverage, --unit, --integration |
| design | architect, plan | No | --adr, --diagram, --review |
| document | docs, readme | No | --api, --readme, --changelog |
| brainstorm | ideate, explore | No | --divergent, --constraints |
| explain | teach, learn, understand | No | --verbose, --simple, --diagram |
| improve | refactor, enhance, optimize | Yes | --performance, --readability, --security |
| build | compile, package | No | --watch, --production, --docker |
| git | commit, branch | No | --commit, --pr, --branch |
| workflow | pipeline, sequence | No | --spec, --parallel |
| estimate | scope, size | No | --breakdown, --risks |
| cicd-setup | cicd-init, pipeline-setup | No | --lang, --minimal, --full |
| readme | readme-sync, doc-sync | No | --base, --preview, --consensus |
| pr-fix | prfix, cifix, fix-pr | Yes | --branch, --auto-fix, --max-fix-attempts |
| pr-check | preflight, prepr | No | --fix, --strict |
| log-fix | logfix, debug-logs | No | --source, --pattern |
| e2e | e2e-test, browser-test | Yes | --browser, --headed, --trace |
| tdd | test-driven | Yes | --cycle, --framework |
| principles | validate-principles | No | --kiss, --solid, --purity, --crash |
| worktree | git-worktree | No | --name, --branch |
| mcp | mcp-orchestrate | No | --pal, --rube, --consensus |
| code-review | review, cr | No | --models, --consensus |
| research | deep-research | No | --depth, --sources |
| eda | data-analysis | No | --format, --output |
| evaluate | eval, judge | No | --dataset, --judge-model |
| push | git-push | No | --remotes, --exclude |
---
## Loop Orchestration
The loop orchestration system manages iterative improvement workflows with safety guarantees.
### Termination Conditions
```mermaid
flowchart TD
START["Start Loop Iteration 0"] --> EXEC["Execute Skill"]
EXEC --> EVIDENCE["Collect Evidence"]
EVIDENCE --> ASSESS["Assess Quality"]
ASSESS --> SCORE{"Score >= 70?"}
SCORE -->|"Yes"| QUALITY_MET["QUALITY_MET"]
SCORE -->|"No"| MAX{"Iteration >= max?"}
MAX -->|"Yes"| MAX_ITER["MAX_ITERATIONS"]
MAX -->|"No"| TMOUT{"Timeout?"}
TMOUT -->|"Yes"| TIMEOUT_TERM["TIMEOUT"]
TMOUT -->|"No"| PAL["Generate PAL Review Signal"]
PAL --> FEEDBACK["Incorporate Feedback"]
FEEDBACK --> NEXT["Prepare Next Context"]
NEXT --> EXEC
QUALITY_MET --> FINAL["Final PAL Validation Signal"]
FINAL --> DONE["Return LoopResult"]
MAX_ITER --> DONE
TIMEOUT_TERM --> DONE
style QUALITY_MET fill:#4caf50,color:#fff
style MAX_ITER fill:#ff9800,color:#fff
style TIMEOUT_TERM fill:#f44336,color:#fff
```
### All 5 Termination Reasons
| Reason | Trigger | PAL Signal |
|--------|---------|------------|
| `QUALITY_MET` | Score >= threshold (70) | Final validation |
| `MAX_ITERATIONS` | Iteration >= 5 (hard cap) | None |
| `ERROR` | Skill execution failure | None |
| `HUMAN_ESCALATION` | Requires human review | None |
| `TIMEOUT` | Wall-clock time exceeded | None |
### PAL Signal Types
The loop orchestrator generates two types of PAL signals:
1. **Review Signal** - Within-loop review for quality improvement
2. **Final Validation Signal** - After successful completion (score >= 70)
---
## MCP Integrations
SuperClaude integrates with powerful MCP servers for enhanced capabilities:
```mermaid
flowchart TB
subgraph PAL["PAL MCP - 11 Collaborative Tools"]
P1["chat"] & P2["thinkdeep"] & P3["debug"]
P4["planner"] & P5["precommit"]
P6["codereview"] & P7["consensus"] & P8["challenge"]
P9["apilookup"] & P10["listmodels"] & P11["clink"]
end
subgraph Rube["Rube MCP - 500+ App Integrations (11 tools)"]
R1["SEARCH_TOOLS"] & R2["GET_TOOL_SCHEMAS"] & R3["MULTI_EXECUTE_TOOL"]
R4["REMOTE_BASH_TOOL"] & R5["REMOTE_WORKBENCH"]
R6["FIND_RECIPE"] & R7["EXECUTE_RECIPE"] & R8["GET_RECIPE_DETAILS"]
R9["CREATE_UPDATE_RECIPE"] & R10["MANAGE_CONNECTIONS"] & R11["MANAGE_RECIPE_SCHEDULE"]
end
TASK["SuperClaude Task"] --> PAL
TASK --> Rube
style PAL fill:#e3f2fd
style Rube fill:#fce4ec
```
### PAL MCP (11 Tools)
Collaborative intelligence for code review, debugging, and multi-model consensus:
| Tool | Purpose | Use Case |
|------|---------|----------|
| `mcp__pal__chat` | General collaborative thinking | Brainstorming, second opinions |
| `mcp__pal__thinkdeep` | Multi-stage investigation | Complex problem analysis |
| `mcp__pal__planner` | Interactive sequential planning | Project planning with revision |
| `mcp__pal__consensus` | Multi-model decision making | Architecture decisions, evaluations |
| `mcp__pal__codereview` | Systematic code review | Quality assessment, security review |
| `mcp__pal__precommit` | Git change validation | Pre-commit checks, change impact |
| `mcp__pal__debug` | Root cause analysis | Complex debugging, issue diagnosis |
| `mcp__pal__challenge` | Critical thinking | Push back on assumptions |
| `mcp__pal__apilookup` | API documentation lookup | Current SDK/API information |
| `mcp__pal__listmodels` | List available models | Model selection and capabilities |
| `mcp__pal__clink` | External AI CLI linking | Connect to Gemini, Codex, etc. |
### Rube MCP (Composio)
Session-based workflow automation with 500+ app integrations:
```mermaid
sequenceDiagram
participant C as Claude Code
participant R as Rube MCP
participant A as External App (Slack, GitHub, etc.)
C->>R: RUBE_SEARCH_TOOLS<br/>{session: {generate_id: true}, queries: [...]}
R-->>C: session_id + available tools + execution plan
C->>R: RUBE_MULTI_EXECUTE_TOOL<br/>{session_id, tools: [...], memory: {...}}
R->>A: Execute tool (e.g., SLACK_SEND_MESSAGE)
A-->>R: Tool result
R-->>C: Results + updated memory
opt Complex Workflow
C->>R: RUBE_CREATE_PLAN<br/>{session_id, use_case, difficulty}
R-->>C: Detailed execution plan
end
opt Recipe Execution
C->>R: RUBE_FIND_RECIPE<br/>{query: "..."}
R-->>C: Matching recipes with recipe_id
C->>R: RUBE_EXECUTE_RECIPE<br/>{recipe_id, input_data}
R-->>C: Recipe execution result
end
```
**Connected Services:**
| Category | Examples |
|----------|----------|
| Development | GitHub, GitLab, Bitbucket, Linear, Jira |
| Communication | Slack, Discord, Teams, Email |
| Productivity | Notion, Asana, Trello, Airtable |
| Google Workspace | Gmail, Calendar, Drive, Sheets, Docs |
| Microsoft | Outlook, Teams, OneDrive, SharePoint |
| Social | X (Twitter), LinkedIn, Meta apps |
| AI Tools | Various AI services and APIs |
### Web Search (via Rube MCP)
Web search capabilities are available through Rube MCP's LINKUP_SEARCH tool:
- Deep or standard search depth
- Sourced answers with citations and URLs
- Accessed via `LINKUP_SEARCH` tool in `RUBE_MULTI_EXECUTE_TOOL`
---
## Skills System
SuperClaude includes 43 active Claude Code skills in `.claude/skills/`:
```mermaid
flowchart TB
subgraph Skills[".claude/skills/ (43 active)"]
subgraph Agent["Agent Skills (8)"]
A1["agent-data-engineer"]
A2["agent-fullstack-developer"]
A3["agent-kubernetes-specialist"]
A4["agent-ml-engineer"]
A5["agent-performance-engineer"]
A6["agent-react-specialist"]
A7["agent-security-engineer"]
A8["agent-technical-writer"]
end
subgraph Command["Command Skills (26)"]
C1["sc-analyze"]
C2["sc-brainstorm"]
C3["sc-build"]
C4["sc-cicd-setup"]
C5["sc-code-review"]
C6["sc-design"]
C7["sc-document"]
C8["sc-e2e"]
C9["sc-estimate"]
C10["sc-evaluate"]
C11["sc-explain"]
C12["sc-git"]
C13["sc-implement"]
C14["sc-improve"]
C15["sc-log-fix"]
C16["sc-mcp"]
C17["sc-pr-check"]
C18["sc-pr-fix"]
C19["sc-principles"]
C20["sc-push"]
C21["sc-readme"]
C22["sc-research"]
C23["sc-tdd"]
C24["sc-test"]
C25["sc-workflow"]
C26["sc-worktree"]
end
subgraph GitNexus["GitNexus Skills (6)"]
G1["gitnexus-cli"]
G2["gitnexus-exploring"]
G3["gitnexus-guide"]
G4["gitnexus-impact-analysis"]
G5["gitnexus-debugging"]
G6["gitnexus-refactoring"]
end
subgraph Utility["Utility Skills (3)"]
U1["ask<br/>(single-select)"]
U2["ask-multi<br/>(multi-select)"]
U3["learned<br/>(auto-learned)"]
end
end
subgraph Structure["Skill File Structure"]
direction LR
SKILL["SKILL.md<br/>(AI-facing interface)"]
SCRIPTS["scripts/<br/>(Tool implementations)"]
end
Skills --> Structure
subgraph ScriptDetail["sc-implement/scripts/ (5 tools)"]
S1["select_agent.py (551 lines)<br/>Weighted agent selection"]
S2["run_tests.py (344 lines)<br/>Test framework detection"]
S3["evidence_gate.py (256 lines)<br/>Quality validation"]
S4["skill_learn.py (466 lines)<br/>Skill extraction"]
S5["loop_entry.py (226 lines)<br/>Loop orchestration"]
end
```
### Skill Types
| Type | Pattern | Count | Purpose |
|------|---------|-------|---------|
| Agent Skills | `agent-*` | 8 | Specialized personas for domains |
| Command Skills | `sc-*` | 26 | Structured workflow implementations |
| GitNexus Skills | `gitnexus-*` | 6 | Code intelligence, impact analysis, refactoring |
| Utility Skills | `ask`, `ask-multi`, `learned` | 3 | User interaction and learning |
### Skill Architecture
Each skill follows a standard structure:
```
.claude/skills/agent-security-engineer/
└── SKILL.md # AI-facing interface (what Claude reads)
.claude/skills/sc-implement/
├── SKILL.md # Command interface
└── scripts/ # Optional tool implementations
├── select_agent.py # Weighted agent selection algorithm
├── run_tests.py # Test framework detection and execution
├── evidence_gate.py # Quality validation (production_ready/acceptable/needs_review)
├── skill_learn.py # Skill extraction from successful executions
└── loop_entry.py # Loop orchestration entry point
```
**Important Distinction:**
- `SKILL.md` = The interface Claude reads to understand when/how to invoke
- `scripts/*.py` = Backend implementations executed by the host system
This follows standard agentic patterns: config-first architecture means the AI-facing interface is pure configuration, while Python handles orchestration.
### Skill Learning System
The `learned/` skill directory stores patterns extracted from successful executions:
- **Extraction**: `skill_learn.py` extracts patterns from successful `--loop` executions
- **Promotion**: Skills promoted when quality score >= 85.0 and success rate >= 70%
- **Retrieval**: Auto-retrieved based on task keywords, file types, domain context
- **Metadata**: Tracks provenance (session ID, timestamp, quality progression)
### Using Skills
Skills are invoked via:
1. **Semantic selection** - Claude automatically selects based on task context
2. **Direct reference** - `@agent-security-engineer` or `/sc-implement`
3. **Skill tool** - `Skill("agent-security-engineer")` for explicit invocation
---
## Quality System
SuperClaude uses a 9-dimension quality scoring system with automated actions:
```mermaid
flowchart LR
subgraph Dimensions["9 Quality Dimensions"]
C["Correctness<br/>25%"]
CO["Completeness<br/>20%"]
P["Performance<br/>10%"]
M["Maintainability<br/>10%"]
S["Security<br/>10%"]
SC["Scalability<br/>10%"]
T["Testability<br/>10%"]
U["Usability<br/>5%"]
PAL["PAL Review<br/>0%*"]
end
subgraph Bands["Quality Bands"]
E["90+ Excellent"]
G["70-89 Good"]
A["50-69 Acceptable"]
P2["30-49 Poor"]
F["0-29 Failing"]
end
subgraph Actions["Auto-Actions"]
FT["Fast-track<br/>Skip review"]
AP["Approve<br/>Complete task"]
IT["Iterate<br/>Loop continues"]
ES["Escalate<br/>Human review"]
BL["Block<br/>Reject output"]
end
Dimensions -->|"Weighted<br/>Average"| Bands
E --> FT
G --> AP
A --> IT
P2 --> ES
F --> BL
style E fill:#4caf50,color:#fff
style G fill:#8bc34a
style A fill:#ffeb3b
style P2 fill:#ff9800,color:#fff
style F fill:#f44336,color:#fff
```
*PAL Review: Dynamic weight when `--pal-review` enabled
### Quality Dimensions
| Dimension | Weight | Indicators |
|-----------|--------|------------|
| Correctness | 25% | Tests pass, no runtime errors, output validation |
| Completeness | 20% | Feature coverage, edge cases, documentation |
| Performance | 10% | Time/space complexity, resource usage |
| Maintainability | 10% | Readability, modularity, naming conventions |
| Security | 10% | Input validation, authentication, data protection |
| Scalability | 10% | Architecture patterns, database design, caching |
| Testability | 10% | Unit tests, integration tests, test quality |
| Usability | 5% | UI consistency, error messages, accessibility |
| PAL Review | 0%* | External code review score (dynamic weight) |
### Evidence Requirements
| Requires Evidence | Does Not Require Evidence |
|-------------------|---------------------------|
| `sc-implement` - File diffs, test results | `sc-analyze` - Analysis output |
| `sc-improve` - Before/after, metrics | `sc-design` - Specifications, diagrams |
| `sc-test` - Test output, coverage | `sc-document` - Documentation files |
| | `sc-git` - Git history (self-documenting) |
| | `sc-explain` - Learning materials |
### Quality Thresholds (from `evidence_gate.py`)
| Status | Score | Action |
|--------|-------|--------|
| Production Ready | 90.0+ | Fast-track, skip additional review |
| Acceptable | 70.0-89.9 | Approve, task complete |
| Needs Review | 50.0-69.9 | Iterate or escalate |
| Below Threshold | < 50.0 | Block, requires significant rework |
---
## Quality Gates
SuperClaude enforces code quality through automated validators integrated into the development workflow via the `/sc:principles` command.
### KISS Validator
Enforces code simplicity via AST analysis. Located at `.claude/skills/sc-principles/scripts/validate_kiss.py`.
| Metric | Threshold | Severity | Description |
|--------|-----------|----------|-------------|
| Cyclomatic Complexity | > 10 | error | Number of independent paths through code |
| Cyclomatic Complexity | > 7 | warning | Early warning for growing complexity |
| Cognitive Complexity | > 15 | error | Weighted complexity (nested structures count more) |
| Function Length | > 50 lines | error | Lines of code per function |
| Nesting Depth | > 4 levels | error | If/for/while/with/try nesting |
| Parameter Count | > 5 | warning | Function parameters |
**Usage:**
```bash
python .claude/skills/sc-principles/scripts/validate_kiss.py --scope-root . --json
```
**Cognitive vs Cyclomatic Complexity:**
- Cyclomatic counts decision points (branches)
- Cognitive weights nested structures more heavily: `1 + nesting_depth` per control structure
- Example: `if (if (if ...))` has low cyclomatic but high cognitive (hard to read)
### Purity Validator
Enforces "Functional Core, Imperative Shell" architectural pattern. Located at `.claude/skills/sc-principles/scripts/validate_purity.py`.
| Layer | Path Patterns | I/O Allowed | Severity |
|-------|---------------|-------------|----------|
| Core (purity required) | `*/domain/*`, `*/logic/*`, `*/services/*`, `*/utils/*`, `*/core/*` | NO | error |
| Shell (I/O allowed) | `*/handlers/*`, `*/adapters/*`, `*/api/*`, `*/cli/*`, `*/scripts/*`, `*/tests/*` | YES | warning |
**Detected I/O Patterns:**
| Category | Examples |
|----------|----------|
| File I/O | `open()`, `read()`, `write()`, `Path.read_text()` |
| Network | `requests.get()`, `httpx`, `urllib`, `socket` |
| Database | `execute()`, `query()`, `session.add()`, `cursor` |
| Subprocess | `subprocess.run()`, `os.system()`, `Popen` |
| Global State | `global`, `nonlocal` keywords |
| Side Effects | `print()`, `logging.*`, `logger.*` |
| Async I/O | `async def`, `await`, `async for`, `async with` |
**Usage:**
```bash
python .claude/skills/sc-principles/scripts/validate_purity.py --scope-root . --json
```
### SOLID Validator
Enforces SOLID design principles via AST analysis. Located at `.claude/skills/sc-principles/scripts/validate_solid.py`.
| Principle | Check | Threshold | Severity |
|-----------|-------|-----------|----------|
| SRP (Single Responsibility) | File length | > 300 lines | error |
| SRP (Single Responsibility) | Class public methods | > 5 | error |
| OCP (Open/Closed) | isinstance cascades | > 2 chained | error |
| LSP (Liskov Substitution) | NotImplementedError in overrides | Any | error |
| ISP (Interface Segregation) | Fat interfaces/protocols | > 7 methods | error |
| DIP (Dependency Inversion) | Direct instantiation in business logic | Any | warning |
**Usage:**
```bash
python .claude/skills/sc-principles/scripts/validate_solid.py --scope-root . --json
```
### Let It Crash Validator
Enforces the "Let It Crash" error handling philosophy by detecting anti-patterns. Located at `.claude/skills/sc-principles/scripts/validate_crash.py`.
| Anti-Pattern | Description | Severity |
|--------------|-------------|----------|
| Bare `except:` | Catches all exceptions without specificity | error |
| `except Exception` without re-raise | Swallows exceptions silently | error |
| `except: pass` | Silent failure hiding bugs | error |
| Nested try/except cascades | Deeply nested error handling (> 2 levels) | error |
**Usage:**
```bash
python .claude/skills/sc-principles/scripts/validate_crash.py --scope-root . --json
```
### Pre-commit Integration
Add to `.pre-commit-config.yaml`:
```yaml
repos:
- repo: local
hooks:
- id: kiss-check
name: KISS Validation
entry: python .claude/skills/sc-principles/scripts/validate_kiss.py --scope-root . --json
language: python
types: [python]
pass_filenames: false
- id: purity-check
name: Purity Validation
entry: python .claude/skills/sc-principles/scripts/validate_purity.py --scope-root . --json
language: python
types: [python]
pass_filenames: false
- id: solid-check
name: SOLID Validation
entry: python .claude/skills/sc-principles/scripts/validate_solid.py --scope-root . --json
language: python
types: [python]
pass_filenames: false
- id: crash-check
name: Let It Crash Validation
entry: python .claude/skills/sc-principles/scripts/validate_crash.py --scope-root . --json
language: python
types: [python]
pass_filenames: false
```
### Exit Codes
| Code | Meaning | Action |
|------|---------|--------|
| 0 | Validation passed | Proceed |
| 2 | Violations detected | Blocked - refactor required |
| 3 | Validation error | Manual intervention needed |
### Refactoring Guidance
When violations are detected:
**For Complexity Violations:**
- **Extract Method** - Split large functions into smaller, named pieces
- **Guard Clauses** - Replace nested if/else with early returns
- **Strategy Pattern** - Replace complex switch/if-else with polymorphism
- **Decompose Conditional** - Name complex conditions as explaining variables
**For Purity Violations:**
- **Dependency Injection** - Pass dependencies as arguments
- **Repository Pattern** - Isolate database operations
- **Adapter Pattern** - Wrap external APIs
- **Return Don't Print** - Return values, let callers handle output
---
## Metrics System
SuperClaude includes a callback-based metrics system (`core/metrics.py`) that decouples the orchestrator from specific metrics backends.
### MetricsEmitter Protocol
```python
@runtime_checkable
class MetricsEmitter(Protocol):
"""Protocol defining the interface for metrics emission."""
def __call__(
self,
metric_name: str,
value: Any,
tags: Optional[Dict[str, str]] = None,
) -> None: ...
```
### Available Emitters
| Emitter | Purpose | Use Case |
|---------|---------|----------|
| `noop_emitter` | Does nothing | Default when no emitter configured |
| `InMemoryMetricsCollector` | Collects to list | Testing and debugging |
| `LoggingMetricsEmitter` | Logs via Python logging | Simple production logging |
### InMemoryMetricsCollector
Testing utility for collecting and querying metrics:
```python
from core.metrics import InMemoryMetricsCollector
collector = InMemoryMetricsCollector()
orchestrator = LoopOrchestrator(config, metrics_emitter=collector)
result = orchestrator.run(context, invoker)
# Query collected metrics
assert collector.get("loop.completed.count") == 1
assert collector.get("loop.duration.seconds") > 0
assert collector.count("loop.iteration.quality_score.gauge") == 3
# Filter by tags
error_metrics = collector.filter_by_tags(
"loop.errors.count",
{"reason": "skill_invocation"}
)
```
### Emitted Metrics
**Loop Orchestrator Metrics:**
| Metric | Type | Description |
|--------|------|-------------|
| `loop.started.count` | counter | Loop initiated |
| `loop.completed.count` | counter | Loop finished (tags: termination_reason) |
| `loop.duration.seconds` | timing | Total loop time |
| `loop.iterations.total.gauge` | gauge | Iterations executed |
| `loop.quality_score.final.gauge` | gauge | Final quality score |
| `loop.errors.count` | counter | Errors (tags: reason) |
| `loop.iteration.duration.seconds` | timing | Per-iteration timing |
| `loop.iteration.quality_score.gauge` | gauge | Per-iteration quality |
| `loop.iteration.quality_delta.gauge` | gauge | Quality change per iteration |
**Skill Learning Metrics:**
| Metric | Type | Description |
|--------|------|-------------|
| `learning.skills.applied.count` | counter | Skills injected at start |
| `learning.skills.extracted.count` | counter | Skills learned (tags: domain, success) |
| `learning.skills.promoted.count` | counter | Skills auto-promoted (tags: reason) |
### Naming Convention
```
<component>.<subject>.<unit>
```
| Suffix | Type | Use Case |
|--------|------|----------|
| `.count` | counter | Events (incremental) |
| `.gauge` | gauge | Current state (point-in-time) |
| `.seconds` | timing | Duration measurement |
### Integration Examples
**Prometheus:**
```python
from prometheus_client import Counter, Gauge
counters, gauges = {}, {}
def prometheus_emitter(name, value, tags=None):
labels = tags or {}
if name.endswith('.count'):
if name not in counters:
counters[name] = Counter(name.replace('.', '_'), '', list(labels.keys()))
counters[name].labels(**labels).inc(value)
elif name.endswith('.gauge'):
if name not in gauges:
gauges[name] = Gauge(name.replace('.', '_'), '', list(labels.keys()))
gauges[name].labels(**labels).set(value)
```
**StatsD:**
```python
import statsd
client = statsd.StatsClient()
def statsd_emitter(name, value, tags=None):
if name.endswith('.count'):
client.incr(name, value)
elif name.endswith('.gauge'):
client.gauge(name, value)
elif name.endswith('.seconds'):
client.timing(name, value * 1000) # Convert to ms
```
---
## Type System
Core types defined in `core/types.py`:
### TerminationReason
```python
class TerminationReason(Enum):
"""Reasons for loop termination."""
QUALITY_MET = "quality_threshold_met" # Score >= 70
MAX_ITERATIONS = "max_iterations_reached" # Hard cap at 5
ERROR = "improver_error" # Skill execution failure
HUMAN_ESCALATION = "requires_human_review" # Needs human input
TIMEOUT = "timeout" # Wall-clock exceeded
```
### LoopConfig
```python
@dataclass
class LoopConfig:
"""Configuration for the agentic loop."""
max_iterations: int = 3 # Requested max (user-configurable)
hard_max_iterations: int = 5 # P0 SAFETY: Cannot be overridden
quality_threshold: float = 70.0 # Target score to meet
timeout_seconds: Optional[float] # Wall-clock timeout (optional)
pal_review_enabled: bool = True # Enable PAL MCP review within loop
pal_model: str = "gpt-5" # Model to use for PAL reviews
```
### QualityAssessment
```python
@dataclass
class QualityAssessment:
"""Result of quality assessment for an iteration."""
overall_score: float # Numeric quality score (0-100)
passed: bool # Whether quality threshold was met
threshold: float = 70.0 # The threshold checked against
improvements_needed: list[str] # Specific improvements to make
metrics: dict[str, float] # Detailed breakdown by dimension
band: str = "unknown" # Quality band (excellent/good/acceptable/poor)
metadata: dict[str, Any] # Additional context from evidence
```
### IterationResult
```python
@dataclass
class IterationResult:
"""Result of a single loop iteration."""
iteration: int # Zero-based iteration number
input_quality: float # Quality score before this iteration
output_quality: float # Quality score after this iteration
improvements_applied: list[str] # Improvements that were applied
time_taken: float = 0.0 # Duration in seconds
success: bool = False # Whether iteration improved quality
termination_reason: str = "" # If loop terminated, why
pal_review: Optional[Dict] = None # PAL MCP review results
changed_files: list[str] # Files modified in this iteration
```
### LoopResult
```python
@dataclass
class LoopResult:
"""Final result of complete loop execution."""
final_output: dict[str, Any] # Final state after all iterations
final_assessment: QualityAssessment # Quality assessment of final state
iteration_history: list[IterationResult] # All iteration results
termination_reason: TerminationReason # Why the loop stopped
total_iterations: int # Number of iterations executed
total_time: float = 0.0 # Total wall-clock time
```
---
## Configuration
SuperClaude uses 6 YAML configuration files:
### config/superclaud.yaml
Main framework configuration:
```yaml
version: 7.0.0
name: SuperClaude Framework
modes:
default: normal
available: [normal, brainstorming, introspection, task_management, token_efficiency, orchestration]
agents:
max_delegation_depth: 5
circular_detection: true
parallel_execution: true
default_timeout: 300
commands:
prefix: "/sc:"
discovery_path: SuperClaude/Commands
cache_ttl: 3600
quality:
enabled: true
default_threshold: 70.0
max_iterations: 5
dimensions:
correctness: 0.25
completeness: 0.20
performance: 0.10
maintainability: 0.10
security: 0.10
scalability: 0.10
testability: 0.10
usability: 0.05
pal_review: 0.00 # Dynamic when enabled
mcp_servers:
enabled: true
timeout: 300
retry_attempts: 3
servers:
pal:
enabled: true
models: [gpt-5, claude-opus-4.1, gemini-2.5-pro]
triggers:
mode_triggers:
brainstorming: [explore, brainstorm, "figure out", "not sure"]
introspection: ["analyze my reasoning", reflect, meta-cognitive]
agent_triggers:
communicator: [debug, error, broken, "not working", explain]
optimizer: [refactor, "improve code", "clean up", optimize]
workflows:
default_workflow: [analyze, plan, implement, test, document]
debug_workflow: [reproduce, analyze, isolate, fix, verify]
```
Quality dimensions, model routing, and consensus policies are configured at runtime:
- Quality dimensions and weights are in `config/superclaud.yaml` (quality section)
- Model routing is handled by PAL MCP (`mcp__pal__listmodels` at runtime)
- Agent selection is filesystem-based (`agents/core/`, `agents/traits/`, `agents/extensions/`)
---
## Directory Structure
```
SuperClaude/
├── CLAUDE.md # Master system prompt
├── README.md # This file
├── CONTRIBUTING.md # Contribution guidelines
├── CHANGELOG.md # Version history
├── SONDERA_INTEGRATION.md # Sondera security layer integration guide
├── pyproject.toml # Python project config
├── install-with-sondera.sh # Automated Sondera installer
├── test-sondera-integration.sh # Sondera integration verification
│
├── .claude/
│ └── skills/ # 43 Claude Code skills
│ ├── agent-*/ # 8 agent persona skills
│ │ └── SKILL.md
│ ├── sc-*/ # 26 command skills
│ │ ├── SKILL.md
│ │ └── scripts/ # Optional tool implementations
│ ├── gitnexus/ # 6 GitNexus code intelligence skills
│ │ ├── gitnexus-cli/
│ │ ├── gitnexus-exploring/
│ │ ├── gitnexus-guide/
│ │ ├── gitnexus-impact-analysis/
│ │ ├── gitnexus-debugging/
│ │ └── gitnexus-refactoring/
│ ├── ask/ # Single-select questions
│ ├── ask-multi/ # Multi-select questions
│ └── learned/ # Auto-learned skills
│
├── agents/
│ ├── index.yaml # Agent registry
│ ├── core/ # 5 core agent prompts
│ │ ├── architect.md
│ │ ├── developer.md
│ │ ├── optimizer.md
│ │ ├── guardian.md
│ │ └── communicator.md
│ ├── traits/ # 7 composable trait prompts
│ │ ├── security-first.md
│ │ ├── performance-first.md
│ │ ├── test-driven.md
│ │ ├── minimal-changes.md
│ │ ├── principles-enforced.md
│ │ ├── mcp-pal-enabled.md
│ │ └── mcp-rube-enabled.md
│ ├── extensions/ # 7 domain specialist prompts
│ │ ├── typescript-react-expert.md
│ │ ├── python-expert.md
│ │ ├── golang-expert.md
│ │ ├── rust-expert.md
│ │ ├── kubernetes-specialist.md
│ │ ├── data-engineer.md
│ │ └── ml-engineer.md
│ └── DEPRECATED/ # Archived agents from previous versions (25 files)
│
├── commands/
│ ├── index.yaml # Command registry
│ ├── index.md # Command reference
│ ├── analyze.md
│ ├── implement.md
│ ├── test.md
│ └── ... # 16 command templates
│
├── config/
│ ├── superclaud.yaml # Main framework config (read by Rust crates)
│ └── schemas/ # JSON schemas for validation
│
├── core/ # Python orchestration layer
│ ├── __init__.py
│ ├── loop_orchestrator.py # Loop management (~410 lines)
│ ├── quality_assessment.py # Quality scoring
│ ├── pal_integration.py # PAL MCP signals
│ ├── metrics.py # Callback-based metrics protocol
│ ├── types.py # Core types (150 lines)
│ ├── skill_learning_integration.py
│ └── skill_persistence.py
│
├── SuperClaude/
│