# Skill Registry Arid Irrigation

> Complete skill registry for arid-region-water-saving-irrigation - production-grade harness with dynamic skill routing, lifecycle hooks, and type-safe configuration management.

- **Type:** Skill
- **Install:** `agentstack add skill-dungnotnull-arid-region-water-saving-irrigation-agent-skill-arid-region-water-saving-irrigation-agent-skill`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [dungnotnull](https://agentstack.voostack.com/s/dungnotnull)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [dungnotnull](https://github.com/dungnotnull)
- **Source:** https://github.com/dungnotnull/arid-region-water-saving-irrigation-agent-skill

## Install

```sh
agentstack add skill-dungnotnull-arid-region-water-saving-irrigation-agent-skill-arid-region-water-saving-irrigation-agent-skill
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# SKILL.md — Skill Registry for arid-region-water-saving-irrigation

## Overview

This registry documents the complete skill architecture for the `arid-region-water-saving-irrigation` harness. It defines how skills are registered, resolved, executed, and validated with full JSON schemas for inputs/outputs.

## Skill Architecture Pattern

```
┌─────────────────────────────────────────────────────────────────┐
│                     SKILL REGISTRY                               │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐          │
│  │   Main       │  │   Sub-Skill   │  │    Utility    │          │
│  │   Harness    │─│   Registry    │─│    Skills     │          │
│  │              │  │              │  │              │          │
│  └──────────────┘  └──────────────┘  └──────────────┘          │
└─────────────────────────────────────────────────────────────────┘
         │                       │                    │
         ▼                       ▼                    ▼
    [Router]            [Resolver]          [Executor]
         │                       │                    │
    [Hooks]               [Validation]        [Logging]
         │                       │                    │
    [Config]              [Schemas]          [Metrics]
```

## Registered Skills

### Main Harness Skill

| Property | Value |
|----------|-------|
| **Name** | `arid-region-water-saving-irrigation` |
| **Path** | `skills/main.md` |
| **Type** | `orchestrator` |
| **Version** | `1.0.0` |
| **Trigger** | `/arid-region-water-saving-irrigation [query]` |

**Input Schema:**
```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural language query about arid-region irrigation",
      "minLength": 1
    },
    "language": {
      "type": "string",
      "enum": ["en", "vi", "auto"],
      "default": "auto",
      "description": "Output language preference"
    },
    "context": {
      "type": "object",
      "properties": {
        "location": {"type": "string"},
        "crop": {"type": "string"},
        "climate": {"type": "string"},
        "soil": {"type": "string"},
        "water_quality": {"type": "string"}
      },
      "additionalProperties": true
    }
  },
  "required": ["query"]
}
```

**Output Schema:**
```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "report": {
      "type": "object",
      "properties": {
        "executive_summary": {"type": "string"},
        "inputs_and_scope": {"type": "object"},
        "evidence_collected": {"type": "array"},
        "analysis": {"type": "object"},
        "action_plan": {"type": "array"},
        "academic_evidence": {"type": "array"},
        "disclosure": {"type": "string"},
        "recommendation": {"type": "object"}
      },
      "required": ["executive_summary", "disclosure", "recommendation"]
    },
    "metadata": {
      "type": "object",
      "properties": {
        "version": {"type": "string"},
        "timestamp": {"type": "string"},
        "language": {"type": "string"},
        "degradation_level": {"type": "integer", "minimum": 0, "maximum": 4},
        "gates_passed": {"type": "array", "items": {"type": "string"}},
        "execution_time_ms": {"type": "number"}
      }
    }
  }
}
```

### Sub-Skills

| Name | Path | Type | Input Schema | Output Schema |
|------|------|------|--------------|---------------|
| `sub-gather-requirements` | `skills/sub-gather-requirements.md` | `intake` | RequirementsInput | RequirementsOutput |
| `sub-evidence-collector` | `skills/sub-evidence-collector.md` | `data` | EvidenceInput | EvidenceOutput |
| `sub-core-analysis` | `skills/sub-core-analysis.md` | `analysis` | AnalysisInput | AnalysisOutput |
| `sub-knowledge-updater` | `skills/sub-knowledge-updater.md` | `knowledge` | KnowledgeInput | KnowledgeOutput |
| `sub-advisor` | `skills/sub-advisor.md` | `synthesis` | SynthesisInput | SynthesisOutput |

#### Sub-Skill Input/Output Schemas

**RequirementsInput:**
```json
{
  "raw_message": "string",
  "provided_materials": "array"
}
```

**RequirementsOutput:**
```json
{
  "object": "string",
  "scope": "string",
  "timeframe": "string",
  "available_inputs": "object",
  "target_audience": "string",
  "language": "string",
  "analysis_type": "string"
}
```

**EvidenceInput:**
```json
{
  "requirements": "RequirementsOutput",
  "sources": "array",
  "max_age_days": "integer"
}
```

**EvidenceOutput:**
```json
{
  "current_data": "array",
  "authoritative_docs": "array",
  "recent_news": "array",
  "reference_benchmarks": "array",
  "access_timestamp": "string"
}
```

## Skill Resolution Process

### 1. Registration

Skills are registered in `config/skills_registry.json`:

```json
{
  "registry_version": "1.0.0",
  "skills": [
    {
      "name": "arid-region-water-saving-irrigation",
      "type": "orchestrator",
      "path": "skills/main.md",
      "version": "1.0.0",
      "enabled": true,
      "dependencies": [],
      "hooks": {
        "pre_execution": "hooks/pre_execution.py",
        "post_execution": "hooks/post_execution.py",
        "on_error": "hooks/on_error.py"
      },
      "metadata": {
        "domain": "Arid-Region Irrigation & Water-Efficiency Engineering",
        "quality_gates": ["U1", "U2", "U3", "U4", "U5", "U6", "G1", "G2", "G3", "G4"],
        "languages": ["en", "vi"]
      }
    }
  ],
  "sub_skills": [
    {
      "name": "sub-gather-requirements",
      "parent": "arid-region-water-saving-irrigation",
      "type": "intake",
      "path": "skills/sub-gather-requirements.md",
      "execution_order": 1
    },
    {
      "name": "sub-evidence-collector",
      "parent": "arid-region-water-saving-irrigation",
      "type": "data",
      "path": "skills/sub-evidence-collector.md",
      "execution_order": 2
    },
    {
      "name": "sub-core-analysis",
      "parent": "arid-region-water-saving-irrigation",
      "type": "analysis",
      "path": "skills/sub-core-analysis.md",
      "execution_order": 3
    },
    {
      "name": "sub-knowledge-updater",
      "parent": "arid-region-water-saving-irrigation",
      "type": "knowledge",
      "path": "skills/sub-knowledge-updater.md",
      "execution_order": 4
    },
    {
      "name": "sub-advisor",
      "parent": "arid-region-water-saving-irrigation",
      "type": "synthesis",
      "path": "skills/sub-advisor.md",
      "execution_order": 5
    }
  ]
}
```

### 2. Resolution

The skill resolver performs the following steps:

```python
def resolve_skill(skill_name: str, registry: dict) -> SkillDefinition:
    """
    Resolve a skill from the registry.
    
    Steps:
    1. Look up skill_name in registry
    2. Validate skill is enabled
    3. Load skill file
    4. Parse frontmatter (name, description)
    5. Validate against schema
    6. Return SkillDefinition object
    """
    pass
```

### 3. Execution

The skill executor:

```python
async def execute_skill(
    skill: SkillDefinition,
    input_data: dict,
    context: ExecutionContext
) -> dict:
    """
    Execute a skill with full lifecycle hooks.
    
    Steps:
    1. pre_execution hook
    2. Validate input against schema
    3. Execute skill logic
    4. Validate output against schema
    5. post_execution hook
    6. Log metrics
    7. Return output
    """
    pass
```

## Lifecycle Hooks

### Hook Types

| Hook Type | Timing | Purpose | Example |
|------------|--------|---------|---------|
| `pre_execution` | Before skill execution | Input validation, state initialization | Validate required inputs |
| `post_execution` | After skill execution | Output validation, metrics collection | Log execution metrics |
| `on_error` | On error during execution | Error recovery, graceful degradation | Fallback to knowledge base |
| `pre_validation` | Before quality gate validation | Prepare validation context | Collect validation artifacts |
| `post_validation` | After quality gate validation | Handle validation failures | Trigger auto-fix logic |

### Hook Implementation Pattern

```python
# hooks/pre_execution.py
async def pre_execution_hook(
    skill_name: str,
    input_data: dict,
    context: ExecutionContext
) -> HookResult:
    """
    Pre-execution hook for skill validation and setup.
    
    Returns:
        HookResult with modified_input_data and metadata
    """
    logger.info(f"Pre-execution hook for {skill_name}")
    
    # Validate language setting
    language = detect_language(input_data.get("query", ""))
    context.state["language"] = language
    
    # Initialize degradation level
    context.state["degradation_level"] = 0
    
    return HookResult(
        success=True,
        modified_input_data=input_data,
        metadata={"language": language}
    )
```

## Quality Gate Validation

### Gate Definitions

Quality gates are defined in `config/quality_gates.json`:

```json
{
  "universal_gates": [
    {
      "id": "U1",
      "name": "Source Count",
      "check": "sources_count >= 3",
      "auto_fix": "fetch_from_knowledge_base",
      "max_retries": 2
    },
    {
      "id": "U2",
      "name": "Disclosure Present",
      "check": "disclosure_section_exists",
      "auto_fix": "prepend_disclosure",
      "max_retries": 1
    },
    {
      "id": "U3",
      "name": "Evidence Hierarchy",
      "check": "all_sources_have_tier_labels",
      "auto_fix": "annotate_source_tiers",
      "max_retries": 1
    },
    {
      "id": "U4",
      "name": "Language Match",
      "check": "output_language == input_language",
      "auto_fix": "translate_output",
      "max_retries": 1
    },
    {
      "id": "U5",
      "name": "Template Compliance",
      "check": "all_required_sections_present",
      "auto_fix": "reformat_to_template",
      "max_retries": 1
    },
    {
      "id": "U6",
      "name": "Claim Traceability",
      "check": "every_claim_has_source_or_judgment_flag",
      "auto_fix": "flag_unsupported_claims",
      "max_retries": 1
    }
  ],
  "domain_gates": [
    {
      "id": "G1",
      "name": "ETc Computed",
      "check": "etc_computed_via_penman_monteith",
      "auto_fix": "compute_etc",
      "max_retries": 1
    },
    {
      "id": "G2",
      "name": "Soil-Water Balance",
      "check": "irrigation_from_soil_balance",
      "auto_fix": "schedule_from_balance",
      "max_retries": 1
    },
    {
      "id": "G3",
      "name": "Salinity Managed",
      "check": "salinity_leaching_addressed",
      "auto_fix": "manage_salinity",
      "max_retries": 1
    },
    {
      "id": "G4",
      "name": "Automation Specified",
      "check": "automation_sensors_specified",
      "auto_fix": "specify_automation",
      "max_retries": 1
    }
  ]
}
```

### Validation Process

```python
async def validate_quality_gates(
    output: dict,
    gates: List[QualityGate],
    context: ExecutionContext
) -> ValidationResult:
    """
    Validate output against all quality gates.
    
    Process:
    1. For each gate in order:
       a. Evaluate check against output
       b. If failed, attempt auto_fix
       c. Retry up to max_retries
       d. If still failed, flag limitation
    2. Return ValidationResult with passed/failed gates
    """
    pass
```

## Configuration Management

### Configuration Schema

Configuration is managed through `config/settings.json`:

```json
{
  "$schema": "config/schemas/settings.json",
  "version": "1.0.0",
  "skill": {
    "name": "arid-region-water-saving-irrigation",
    "version": "1.0.0",
    "environment": "production"
  },
  "execution": {
    "timeout_seconds": 300,
    "max_retries": 3,
    "retry_delay_ms": 1000,
    "enable_parallel_execution": true
  },
  "logging": {
    "level": "INFO",
    "format": "json",
    "outputs": ["console", "file"],
    "file_path": "logs/skill_execution.log"
  },
  "quality": {
    "strict_mode": false,
    "auto_fix_enabled": true,
    "max_degradation_level": 4
  },
  "knowledge": {
    "update_schedule": "weekly",
    "cache_duration_hours": 168
  },
  "features": {
    "enable_caching": true,
    "enable_metrics": true,
    "enable_tracing": false
  }
}
```

## Error Handling & Graceful Degradation

### Degradation Levels

| Level | Condition | Behavior |
|-------|-----------|----------|
| 0 | All primary sources reachable | Full evidenced analysis |
| 1 | Some primary sources fail | Use secondary sources; flag substitutions |
| 2 | Most live sources fail | Knowledge base only; flag historical context |
| 3 | Required input missing | Proceed with available; mark unavailable |
| 4 | All sources and KB fail | Emit DATA UNAVAILABLE; no fabrication |

### Error Recovery

```python
async def recover_from_error(
    error: Exception,
    context: ExecutionContext,
    degradation_level: int
) -> RecoveryResult:
    """
    Recover from errors with graceful degradation.
    
    Strategy:
    1. Identify error type
    2. Determine recovery action based on degradation level
    3. Execute recovery
    4. Update degradation level
    5. Log recovery action
    """
    pass
```

## Metrics & Monitoring

### Collected Metrics

| Metric | Type | Description |
|--------|------|-------------|
| `execution_time_ms` | histogram | Skill execution time |
| `gate_validation_count` | counter | Number of gates validated |
| `gate_failure_count` | counter | Number of gate failures |
| `degradation_level` | gauge | Current degradation level |
| `source_fetch_count` | counter | Number of sources fetched |
| `knowledge_base_hit_count` | counter | Number of KB hits |

## Extending the Registry

To add a new skill:

1. Create skill file in `skills/`
2. Add entry to `config/skills_registry.json`
3. Define input/output schemas
4. Implement lifecycle hooks
5. Add quality gates if applicable
6. Test with `tools/test_skill_execution.py`

---

**Registry Version:** 1.0.0
**Last Updated:** 2026-07-27
**Maintained By:** arid-region-water-saving-irrigation development team

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [dungnotnull](https://github.com/dungnotnull)
- **Source:** [dungnotnull/arid-region-water-saving-irrigation-agent-skill](https://github.com/dungnotnull/arid-region-water-saving-irrigation-agent-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-dungnotnull-arid-region-water-saving-irrigation-agent-skill-arid-region-water-saving-irrigation-agent-skill
- Seller: https://agentstack.voostack.com/s/dungnotnull
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.