# Esports Fairness Scoring

> Main harness skill for Esports Tournament Fairness & Integrity Scoring comprehensive analysis. Use when user requests fairness assessment, integrity evaluation, tournament scoring, match-fixing risk analysis, or esports tournament governance evaluation. This skill orchestrates multiple specialized sub-agents to deliver evidence-backed, risk-disclosed outputs with academic research integration.

- **Type:** Skill
- **Install:** `agentstack add skill-dungnotnull-esports-fairness-scoring-agent-skill-esports-fairness-scoring-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/esports-fairness-scoring-agent-skill

## Install

```sh
agentstack add skill-dungnotnull-esports-fairness-scoring-agent-skill-esports-fairness-scoring-agent-skill
```

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

## About

# SKILL REGISTRY — esports-fairness-scoring

## Overview

The `esports-fairness-scoring` skill implements a **production-grade multi-agent harness** for comprehensive esports tournament fairness and integrity analysis. It orchestrates specialized sub-skills through a chain-of-thought routing system with lifecycle hooks, quality gates, and graceful degradation.

## Skill Registration

### Main Harness Entry Point

**Name:** `esports-fairness-scoring`
**File:** `skills/main.md`
**Type:** Main harness orchestrator
**Inputs:** User query + optional context materials
**Outputs:** Structured fairness analysis report with verdict

### Sub-Skills Registry

| Skill Name | File | Category | Purpose | Dependencies |
|------------|------|----------|---------|--------------|
| `sub-gather-requirements` | `skills/sub-gather-requirements.md` | sub | Clarify analysis scope and constraints | None |
| `sub-evidence-collector` | `skills/sub-evidence-collector.md` | sub | Fetch authoritative real-time and reference data | `sub-gather-requirements` |
| `sub-core-analysis` | `skills/sub-core-analysis.md` | sub | Analyze and score fairness dimensions | `sub-evidence-collector` |
| `sub-knowledge-updater` | `skills/sub-knowledge-updater.md` | sub | Query knowledge base for academic evidence | None |
| `sub-advisor` | `skills/sub-advisor.md` | sub | Synthesize into risk-disclosed conclusion | All prior sub-skills |

## Skill Resolution

### Resolution Order

1. **Intent Classification**: Router analyzes user query
2. **Path Selection**: Choose execution path based on intent
3. **Dependency Resolution**: Ensure prerequisites met
4. **Execution Orchestration**: Execute skills in required order
5. **Result Aggregation**: Combine outputs into final report

### Execution Modes

**Sequential (Default):** Skills execute one after another, passing outputs forward

**Parallel:** Independent skills execute concurrently (when dependencies allow)

**Conditional:** Skills execute based on branch conditions (targeted assessment mode)

## Skill Execution

### Lifecycle Hooks

Each skill execution triggers hooks at defined lifecycle points:

**before_execution:**
- `validate_inputs`: Verify input schema compliance
- `check_dependencies`: Ensure prerequisite data available
- `authorize`: Verify permission for requested operation
- `initialize`: Set up execution context

**after_execution:**
- `log_results`: Record execution outcome
- `collect_metrics`: Track token usage, time
- `emit_events**: Notify completion to event bus
- `cleanup`: Release resources

**on_error:**
- `identify_failure`: Categorize error type
- `attempt_recovery`: Execute recovery strategy
- `escalate_if_needed`: Determine if human intervention required
- `notify_stakeholders`: Alert on critical failures

**on_quality_gate_failure:**
- `attempt_auto_fix`: Execute gate-specific fix
- `retry_if_allowed`: Re-execute if retry budget available
- `degrade_gracefully': Fall back to lower fidelity mode
- `flag_limitation`: Record limitation for disclosure

### Execution Context

Each skill receives:

```json
{
  "skill_name": "sub-core-analysis",
  "execution_id": "uuid",
  "timestamp": "ISO-8601",
  "inputs": {
    "from_previous_skills": {},
    "from_user": {}
  },
  "config": {
    "timeout_seconds": 120,
    "max_retries": 2,
    "quality_gates": ["G1", "G2", "G3", "G4"]
  },
  "metadata": {
    "trace_id": "uuid",
    "language": "en",
    "degradation_level": 0
  }
}
```

### Return Schema

Each skill must return:

```json
{
  "status": "success|error|degraded",
  "output": {
    "skill_specific_data": {}
  },
  "metadata": {
    "execution_time_seconds": 0.0,
    "tokens_used": 0,
    "quality_gates_passed": [],
    "quality_gates_failed": [],
    "degradation_events": 0
  },
  "errors": []
}
```

## Tool Definitions

### Available Tools

| Tool | Schema | Handler | Usage |
|------|--------|---------|-------|
| WebSearch | `schemas/web-search-input.json` | Native Claude tool | Fetch live domain news and developments |
| WebFetch | `schemas/web-fetch-input.json` | Native Claude tool | Scrape authoritative documents and standards |
| Read | `schemas/read-input.json` | Native Claude tool | Read files (knowledge base, configs) |
| Write | `schemas/write-input.json` | Native Claude tool | Append to knowledge base |
| Bash | `schemas/bash-input.json` | Native Claude tool | Execute scripts (crawl pipeline) |
| Skill | `schemas/skill-input.json` | Native Claude tool | Invoke sub-skills |

### Tool Invocation Schema

**WebSearch:**
```json
{
  "query": "string (required, max 500 chars)",
  "allowed_domains": ["array of strings (optional)"],
  "blocked_domains": ["array of strings (optional)"],
  "recency_filter": "oneDay|oneWeek|oneMonth|oneYear|noLimit (optional)"
}
```

**WebFetch:**
```json
{
  "url": "string (required, valid URL)",
  "prompt": "string (required, extraction instructions)"
}
```

**Skill:**
```json
{
  "skill": "string (required, registered skill name)",
  "args": "object (optional, skill-specific arguments)"
}
```

## Input/Output Schemas

### Main Harness Input

```json
{
  "user_query": "string (required)",
  "provided_materials": {
    "type": "array of objects (optional)",
    "description": "Optional files or context provided by user"
  },
  "language_preference": "en|vi|auto (optional, default: auto-detect)"
}
```

### Main Harness Output

See `assets/schemas/analysis-output-schema.json` for complete schema.

**Required Top-Level Fields:**
- `analysis_id`: UUID
- `timestamp`: ISO-8601
- `language`: "en" or "vi"
- `requirements`: Object
- `evidence_bundle`: Object
- `scorecard`: Object
- `verdict`: Object
- `recommendations`: Array
- `disclosure`: Object
- `evidence_chain`: Array
- `quality_gates`: Object

## Quality Gates

### Universal Gates (U1-U6)

**U1:** Minimum 3 sources cited, at least 1 academic/authoritative

**U2:** Risk/limitation disclosure appears BEFORE recommendation

**U3:** Evidence hierarchy stated per source (Tier 1-4)

**U4:** Output language matches user preference

**U5:** Output uses declared template with all sections present

**U6:** Every claim traced to ≥1 source OR flagged as [analyst judgment]

### Domain Gates (G1-G4)

**G1:** Fairness dimensions scored (format, integrity, rules, incentives)

**G2:** Integrity risks assessed with signal indicators

**G3:** Dispute resolution and incentive alignment addressed

**G4:** Inclusion and accessibility scored

### Gate Enforcement

- Apply gates in sequential order
- On failure: execute auto-fix strategy
- Max 2 retries per gate
- After 2 failures: emit limitation, continue with degraded output
- Final output includes gate status + limitations

## Graceful Degradation

### Degradation Levels

**Level 0:** Full operation (all sources reachable)

**Level 1:** Partial degradation (some primary sources substituted)

**Level 2:** Knowledge base only (historical context)

**Level 3:** Missing variables (flag as DATA UNAVAILABLE)

**Level 4:** Critical failure (emit DATA UNAVAILABLE notice)

### Degradation Behavior

- Automatic escalation based on data availability
- Explicit limitation banner at levels ≥1
- No data fabrication at any level
- Clear communication of what is/isn't available

## Configuration

### Environment Variables

```bash
# Core
LOG_LEVEL=INFO|DEBUG|WARNING|ERROR|CRITICAL
ENVIRONMENT=development|staging|production

# LLM Settings
LLM_PROVIDER=anthropic|openai|google|azure
LLM_MODEL=claude-sonnet-4-20250514
LLM_TEMPERATURE=0.3
LLM_MAX_TOKENS=8192

# Crawl Pipeline
CRAWL_ENABLED=true|false

# Feature Flags
ENABLE_METRICS=true|false
ENABLE_TRACING=true|false
ENABLE_CACHING=true|false
```

### Config File

See `config/settings.py` for type-safe configuration management.

## Extension Points

### Adding Custom Skills

1. Create skill file following `SKILL.md` template
2. Add to registry in this `SKILL.md`
3. Implement input/output schemas
4. Define dependencies and tools required
5. Add quality gates if applicable

### Adding Custom Hooks

1. Implement hook function in `scripts/hooks/`
2. Register in configuration
3. Define trigger conditions
4. Specify async/sync execution

### Adding Custom Quality Gates

1. Define gate in appropriate skill file
2. Specify check criteria
3. Define auto-fix strategy
4. Set retry limit

## Validation

### Schema Validation

All inputs and outputs are validated against JSON schemas in `assets/schemas/`:

- `skill-registry-schema.json`
- `agent-message-schema.json`
- `analysis-output-schema.json`

### Validation Commands

```bash
# Validate project structure
python tools/validate_project.py

# Validate skill registry
python scripts/validate_skill_registry.py

# Validate configuration
python scripts/validate_config.py
```

## Troubleshooting

### Common Issues

**Skill not found:** Verify skill is registered in `SKILL.md`

**Quality gate failure:** Check auto-fix strategy and retry limit

**Degradation level 4:** All data sources failed, check internet connection and source availability

**Language mismatch:** Verify language detection in pre-flight step

**Schema validation error:** Check input/output against schemas

## Performance Considerations

- Context window management: Progressive summary when approaching limit
- Token budgeting: Allocate per sub-skill based on complexity
- Caching: Cache deterministic LLM operations
- Parallel execution: Enable for independent sub-skills
- Rate limiting: Respect provider rate limits

## Security

- Input validation: All inputs validated against schemas
- Output sanitization: Remove sensitive information before output
- Secret management: Use environment variables for credentials
- Audit logging: Track all skill invocations and modifications

## License

MIT License — see LICENSE file

## 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/esports-fairness-scoring-agent-skill](https://github.com/dungnotnull/esports-fairness-scoring-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-esports-fairness-scoring-agent-skill-esports-fairness-scoring-agent-skill
- Seller: https://agentstack.voostack.com/s/dungnotnull
- Browse the marketplace: https://agentstack.voostack.com/browse

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