# Ai Coastal Erosion Monitoring > AI-Based Coastal Erosion Monitoring & Alert — Production-grade harness for Coastal Erosion Remote Sensing & AI Monitoring evidence-backed analysis with 6-step workflow (requirements→evidence→core analysis→knowledge→synthesis→quality gate). Use for shoreline change detection, beach/dune erosion analysis, storm impact assessment, and coastal defense recommendations. Supports Vietnamese/English, gra… - **Type:** Skill - **Install:** `agentstack add skill-dungnotnull-ai-coastal-erosion-monitoring-agent-skill-ai-coastal-erosion-monitoring-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/ai-coastal-erosion-monitoring-agent-skill ## Install ```sh agentstack add skill-dungnotnull-ai-coastal-erosion-monitoring-agent-skill-ai-coastal-erosion-monitoring-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 Documentation ## Skill Registration & Resolution ### Registration Process Skills in the `ai-coastal-erosion-monitoring` system are registered through a hierarchical registry pattern. Each skill has metadata that determines when and how it's invoked: 1. **Frontmatter Registry** (required): All skills MUST include YAML frontmatter with `name` and `description` fields. The description is the PRIMARY triggering mechanism — it must clearly state both what the skill does AND the contexts in which it should be used. 2. **Registry Hierarchy**: ``` Main Skill (main.md) ├── Sub-Skills (sub-*.md) — invoked via Skill() tool └── Bundled Resources — loaded on-demand ``` 3. **Resolution Order**: - Frontmatter is loaded first (~100 words, always in context) - SKILL.md body is loaded when skill triggers ( None: """Called before skill execution starts.""" def before_sub_skill(ctx: SkillContext, sub_skill: str) -> None: """Called before each sub-skill invocation.""" def after_sub_skill(ctx: SkillContext, sub_skill: str, result: Any) -> Any: """Called after each sub-skills completes.""" def on_degradation(ctx: SkillContext, level: int) -> None: """Called when degradation level changes.""" def before_quality_gate(ctx: SkillContext, gate: str) -> None: """Called before each quality gate check.""" def after_execution(ctx: SkillContext, result: Any) -> Any: """Called after skill execution completes.""" ``` ### State Synchronization The skill maintains execution state across sub-skills: ```python class SkillContext: query: str language: str requirements: Dict evidence_bundle: Dict analysis_scorecard: Dict knowledge_entries: List degradation_level: int gates_status: Dict[str, bool] ``` ## Configuration Management ### Environment-Based Configuration The system supports environment-specific configuration: ```bash # Development config/environment/development.json # Staging config/environment/staging.json # Production config/environment/production.json ``` ### Feature Flags Optional functionality can be toggled via feature flags: ```json { "features": { "enable_experimental_ai": false, "enable_degraded_mode": true, "enable_multi_language": true, "enable_auto_crawl": true, "enable_caching": true } } ``` ## Monitoring & Logging ### Structured Logging All operations emit structured logs: ```json { "timestamp": "2026-07-15T10:30:00Z", "level": "INFO", "component": "sub-core-analysis", "operation": "compute_shoreline_change", "correlation_id": "uuid", "duration_ms": 1250, "metadata": { "location": "Vietnam_Coast", "imagery_source": "Sentinel-2" } } ``` ### Metrics Tracking Key metrics tracked: - Execution time per sub-skill - Degradation level distribution - Quality gate pass/fail rates - Source availability - Token usage ## Extension Points ### Adding New Sub-Skills To add a new sub-skill: 1. Create `skills/sub-new-skill.md` with proper frontmatter 2. Define input/output JSON schemas in this SKILL.md 3. Update main.md to invoke the sub-skill 4. Add quality gates if applicable 5. Update test scenarios ### Adding New Data Sources To add a new data source: 1. Update `config/default.config.json` sources section 2. Update SECOND-KNOWLEDGE-BRAIN.md sources section 3. Update CLAUDE.md knowledge sources section 4. Test with degraded mode scenarios ### Modifying Quality Gates To modify quality gates: 1. Update `config/default.config.json` quality_gates section 2. Update skills/main.md quality gates table 3. Update PROJECT-detail.md gates definition 4. Test with gate failure scenarios ## Testing Strategy ### Test Scenarios The system includes 5+ test scenarios in `tests/test-scenarios.md`: 1. Standard analysis (full data availability) 2. Minimal input (partial data) 3. Comparison (multiple locations) 4. Risk/conflict (conflicting data) 5. Degraded mode (source failures) ### Validation Tools ```bash # Validate project structure python scripts/validate.py # Run test scenarios python tools/run_test_scenarios.py --all # Test knowledge updater python tools/test_knowledge_updater.py ``` ## Performance Optimization ### Context Window Management The skill implements progressive disclosure to manage context: 1. **Frontmatter only** (~100 words) — Always loaded 2. **SKILL.md body** (~500 lines) — Loaded on trigger 3. **Sub-skill files** — Loaded on invocation 4. **Reference materials** — Loaded on demand ### Caching Strategy - Knowledge base entries cached for 24 hours - Source responses cached with TTL - LLM responses cached when idempotent ## Troubleshooting ### Common Issues **Issue**: Skill not triggering - **Cause**: Description not matching user intent - **Fix**: Optimize description with trigger eval queries **Issue**: Quality gate failures - **Cause**: Data unavailability or logic error - **Fix**: Check degradation level, source availability **Issue**: Sub-skill timeout - **Cause**: External source slow or unavailable - **Fix**: Check network, enable degraded mode ### Debug Mode Enable debug logging: ```json { "logging": { "level": "DEBUG", "format": "json" } } ``` ## Version Compatibility This skill is compatible with: - **Claude Code**: All versions supporting Skill tool - **Claude.ai**: With limitations (no subagent support) - **Python**: 3.11+ - **Claude Models**: Opus 4.7, Sonnet 4.6, Haiku 4.5 ## License MIT License — See LICENSE file for details. ## 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/ai-coastal-erosion-monitoring-agent-skill](https://github.com/dungnotnull/ai-coastal-erosion-monitoring-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-ai-coastal-erosion-monitoring-agent-skill-ai-coastal-erosion-monitoring-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%.