# Esports Theorycrafting Meta Analysis > Esports Meta-Game Analysis & Theorycrafting Optimization - evidence-backed analysis harness with quality-gated, risk-disclosed output. Use this skill whenever the user asks about esports meta analysis, theorycrafting, hero tier lists, build optimization, counter-strategies, patch analysis, competitive gaming strategies, or wants authoritative esports analytics with academic backing. - **Type:** Skill - **Install:** `agentstack add skill-dungnotnull-esports-theorycrafting-meta-analysis-agent-skill-esports-theorycrafting-meta-analysis-agent-skill` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [dungnotnull](https://agentstack.voostack.com/s/dungnotnull) - **Installs:** 0 - **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [dungnotnull](https://github.com/dungnotnull) - **Source:** https://github.com/dungnotnull/esports-theorycrafting-meta-analysis-agent-skill ## Install ```sh agentstack add skill-dungnotnull-esports-theorycrafting-meta-analysis-agent-skill-esports-theorycrafting-meta-analysis-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-theorycrafting-meta-analysis ## Overview This document defines the complete skill registry, resolution protocol, execution flow, and validation standards for the `esports-theorycrafting-meta-analysis` harness skill. It serves as the canonical reference for how the skill system operates, how skills are registered, resolved, executed, and validated. ## Skill Identity ```yaml name: esports-theorycrafting-meta-analysis version: 1.1.0 domain: Esports Competitive Analysis & Theorycrafting status: PRODUCTION_READY architecture: Harness (orchestrator + sub-skills) ``` ## Skill Registration Protocol ### Registration Schema Skills are registered via YAML frontmatter in each `.md` file: ```yaml --- name: {skill-identifier} description: {one-line trigger description} --- ``` **Required fields:** - `name`: Unique skill identifier (kebab-case) - `description`: When to trigger, what it does (primary triggering mechanism) **Optional metadata (added to skill card via comments):** - `version`: Skill version - `domain`: Domain category - `compatibility`: Required tools/dependencies - `author`: Author attribution ### Skill Registry Structure ``` skills/ ├── main.md # Main orchestrator (entry point) ├── sub-gather-requirements.md # Sub-skill 1: Requirements parsing ├── sub-evidence-collector.md # Sub-skill 2: Evidence collection ├── sub-core-analysis.md # Sub-skill 3: Core analysis ├── sub-knowledge-updater.md # Sub-skill 4: Knowledge base query └── sub-advisor.md # Sub-skill 5: Advisory synthesis ``` ### Skill Resolution Protocol 1. **Trigger Detection**: Claude analyzes user input against skill `description` fields 2. **Skill Selection**: Matching skill(s) are loaded into context 3. **Sub-skill Resolution**: Main skill invokes sub-skills via `Skill()` tool calls 4. **Execution Order**: Sequential execution following harness protocol **Resolution Logic:** ``` user_query → skill.description_match → skill.load() main_skill → sub_skill_invocation → sequential_execution ``` ## Input/Output JSON Schemas ### Main Harness Input Schema ```json { "type": "object", "properties": { "query": { "type": "string", "description": "Free-form analysis query from user", "example": "Analyze LoL patch 14.10 jungle meta" }, "audience": { "type": "string", "enum": ["analyst", "coach", "player", "broadcaster", "researcher", "learner"], "default": "analyst", "description": "Target audience for the report" }, "language": { "type": "string", "enum": ["en", "vi"], "description": "Output language (auto-detected if not specified)" }, "inputs": { "type": "object", "properties": { "heroes_json": {"type": "string", "description": "Path to heroes data JSON"}, "matchups_json": {"type": "string", "description": "Path to matchups data JSON"}, "builds_json": {"type": "string", "description": "Path to builds data JSON"}, "before_stats_json": {"type": "string", "description": "Path to previous patch stats JSON"} } } }, "required": ["query"] } ``` ### Requirements Output Schema (Step 1) ```json { "type": "object", "properties": { "object_of_analysis": {"type": "string"}, "scope": {"type": "string", "enum": ["meta", "hero", "build", "counter", "patch", "combined"]}, "timeframe": {"type": "string"}, "target_audience": {"type": "string"}, "language": {"type": "string", "enum": ["en", "vi"]}, "analysis_type": {"type": "string"}, "keywords": {"type": "array", "items": {"type": "string"}}, "inputs": {"type": "object"}, "assumptions": {"type": "array", "items": {"type": "string"}}, "open_questions": {"type": "array", "items": {"type": "string"}} }, "required": ["object_of_analysis", "language"] } ``` ### Evidence Bundle Output Schema (Step 2) ```json { "type": "object", "properties": { "items": { "type": "array", "items": { "type": "object", "properties": { "source": {"type": "string"}, "url": {"type": "string", "format": "uri"}, "tier": {"type": "integer", "enum": [1, 2, 3, 4]}, "snippet": {"type": "string"}, "access_date": {"type": "string", "format": "date-time"}, "relevance_score": {"type": "number", "minimum": 0, "maximum": 10} } } }, "tier_counts": {"type": "object", "patternProperties": {"^[1-4]$": {"type": "integer"}}}, "coverage_rating": {"type": "string", "enum": ["Strong", "Moderate", "Weak"]}, "degradation_level": {"type": "integer", "minimum": 0, "maximum": 4}, "limitations": {"type": "array", "items": {"type": "string"}}, "collected_at": {"type": "string", "format": "date-time"} }, "required": ["items", "degradation_level", "coverage_rating"] } ``` ### Core Analysis Output Schema (Step 3) ```json { "type": "object", "properties": { "hero_stats": { "type": "array", "items": { "type": "object", "properties": { "name": {"type": "string"}, "win_rate": {"type": "number", "minimum": 0, "maximum": 1}, "pick_rate": {"type": "number", "minimum": 0, "maximum": 1}, "ban_rate": {"type": "number", "minimum": 0, "maximum": 1}, "games": {"type": "integer", "minimum": 0}, "tier": {"type": "string", "pattern": "^[SABC?]$"}, "ci": { "type": "object", "properties": { "lower": {"type": "number"}, "upper": {"type": "number"}, "center": {"type": "number"}, "confidence": {"type": "number"} } } } } }, "build_rankings": { "type": "array", "items": { "type": "object", "properties": { "rank": {"type": "integer"}, "name": {"type": "string"}, "effective_dps": {"type": "number"}, "effective_ehp": {"type": "number"}, "ttk_vs_reference": {"type": "number"}, "composite_score": {"type": "number"}, "synergy": {"type": "number"} } } }, "counters": { "type": "array", "items": { "type": "object", "properties": { "target": {"type": "string"}, "counter": {"type": "string"}, "advantage": {"type": "number"}, "evidence_games": {"type": "integer", "minimum": 0} } } }, "patch_deltas": { "type": "array", "items": { "type": "object", "properties": { "name": {"type": "string"}, "win_rate_before": {"type": "number"}, "win_rate_after": {"type": "number"}, "win_rate_delta": {"type": "number"}, "pick_rate_delta": {"type": "number"}, "direction": {"type": "string", "enum": ["buff", "nerf", "neutral"]} } } }, "scenarios": { "type": "array", "items": { "type": "object", "properties": { "label": {"type": "string"}, "confidence": {"type": "number", "minimum": 0, "maximum": 1}, "description": {"type": "string"}, "expected_top_builds": {"type": "array", "items": {"type": "string"}}, "expected_tier_movements": {"type": "array", "items": {"type": "string"}} } } } } } ``` ### Knowledge Query Output Schema (Step 4) ```json { "type": "object", "properties": { "citations": { "type": "array", "items": { "type": "object", "properties": { "title": {"type": "string"}, "authors": {"type": "string"}, "year": {"type": "integer"}, "venue": {"type": "string"}, "doi_or_url": {"type": "string"}, "tier": {"type": "integer", "enum": [1, 2, 3, 4]}, "relevance": {"type": "string", "enum": ["H", "M", "L"]}, "key_finding": {"type": "string"} } } }, "gaps": {"type": "array", "items": {"type": "string"}}, "coverage": {"type": "string", "enum": ["Strong", "Moderate", "Weak"]} } } ``` ### Final Report Output Schema (Step 5-6) ```json { "type": "object", "properties": { "requirements": {"type": "object"}, "evidence": {"$ref": "#/definitions/EvidenceBundle"}, "hero_stats": {"type": "array"}, "build_rankings": {"type": "array"}, "counters": {"type": "array"}, "patch_deltas": {"type": "array"}, "scenarios": {"type": "array"}, "knowledge_citations": {"type": "array"}, "knowledge_gaps": {"type": "array"}, "coverage_rating": {"type": "string"}, "degradation_level": {"type": "integer"}, "limitations": {"type": "array"}, "risks": {"type": "array"}, "remediation": {"type": "array"}, "verdict": {"type": "string", "enum": ["Strong Meta Plan", "Conditional (meta shifting)", "Insufficient Data", "Inconclusive"]}, "gates": {"type": "array"}, "language": {"type": "string"}, "generated_at": {"type": "string", "format": "date-time"} }, "required": ["verdict", "gates", "language", "generated_at"] } ``` ## Skill Execution Protocol ### Harness Execution Flow ``` ┌─────────────────────────────────────────────────────────────────┐ │ USER INPUT │ │ /esports-theorycrafting-meta-analysis "Analyze LoL jungle meta" │ └────────────────────────────┬────────────────────────────────────┘ │ v ┌─────────────────────────────────────────────────────────────────┐ │ PRE-FLIGHT: Language Detection │ │ Detect en/vi from diacritics + keywords → LANG │ └────────────────────────────┬────────────────────────────────────┘ │ v ┌─────────────────────────────────────────────────────────────────┐ │ STEP 1: sub-gather-requirements │ │ Input: user query │ │ Internal Gate: At least one object of analysis confirmed │ │ Output: Requirements object │ │ Auto-fix: Ask up to 2 clarifying questions │ └────────────────────────────┬────────────────────────────────────┘ │ v ┌─────────────────────────────────────────────────────────────────┐ │ STEP 2: sub-evidence-collector │ │ Input: Requirements object │ │ Tool: evidence_collector.py (EvidenceCollector) │ │ Internal Gate: Current data + 1 authoritative doc OR flag │ │ Output: EvidenceBundle (tier-labeled) │ │ Auto-fix: Fallback to SECOND-KNOWLEDGE-BRAIN.md, escalate degr. │ └────────────────────────────┬────────────────────────────────────┘ │ v ┌─────────────────────────────────────────────────────────────────┐ │ STEP 3: sub-core-analysis │ │ Input: Requirements, EvidenceBundle, optional JSON inputs │ │ Tool: theorycraft_engine.py │ │ Internal Gates: G1 (formulas+match data), G2 (counters->top5), │ │ G3 (sources dated + deltas), G4 (Tier =1 academic/authoritative source │ │ Output: KnowledgeQuery (citations + gaps + coverage) │ │ Auto-fix: WebSearch gap-fill (max 2 queries) │ └────────────────────────────┬────────────────────────────────────┘ │ v ┌─────────────────────────────────────────────────────────────────┐ │ STEP 5: sub-advisor │ │ Input: All prior outputs │ │ Tool: harness_runner.py: derive_verdict/risks/remediation │ │ Internal Gate: Verdict ∈ {4 declared categories}, disclosure pre │ │ Output: verdict, scenarios, risks, remediation, evidence chain │ │ Auto-fix: Force to "Inconclusive" if invalid verdict │ └────────────────────────────┬────────────────────────────────────┘ │ v ┌─────────────────────────────────────────────────────────────────┐ │ STEP 6: Quality Gate Review (Main Harness) │ │ Input: All outputs, Requirements, EvidenceBundle │ │ Tool: harness_runner.py:evaluate_gates() │ │ Gates: U1-U6 (universal) + G1-G4 (domain) = 10 total │ │ Output: GateResult[] with pass/fail + limitation notes │ │ Auto-fix: 2 retry attempts per gate; emit limitation if fail │ └────────────────────────────┬────────────────────────────────────┘ │ v ┌─────────────────────────────────────────────────────────────────┐ │ FINAL REPORT │ │ JSON: logs/harness_report.json │ │ Markdown: logs/harness_report.md │ │ Includes: Post-Execution Gate Checklist (U1-U6 + G1-G4) │ └─────────────────────────────────────────────────────────────────┘ ``` ### Skill Invocation Patterns **Pattern 1: Main Skill (Entry Point)** ``` User: /esports-theorycrafting-meta-analysis "Analyze LoL patch 14.10" ↓ Claude: Loads skills/main.md ↓ main.md orchestrates Steps 1-6 sequentially ``` **Pattern 2: Sub-Skill Invocation (from main.md)** ``` main.md Step 1: ↓ Skill("sub-gather-requirements") ↓ sub-gather-requirements.md executes ↓ Returns Requirements object ``` **Pattern 3: CLI Execution (bypassing Claude)** ```bash python tools/harness_runner.py \ --query "Analyze LoL patch 14.10 jungle meta" \ --heroes-json inputs/heroes.json \ --output-md logs/report.md ``` ## Skill Validation Protocol ### Validation Levels 1. **Structural Validation** (`tools/validate_project.py`) - 8-File Contract compliance - Directory structure - YAML frontmatter validity - UTF-8 encoding (no BOM) - Cross-reference consistency 2. **Functional Validation** (`tests/test_*.py`) - Unit tests for each module - Integration tests for harness - End-to-end scenario tests 3. **Quality Gate Validation** (runtime) - U1-U6 universal gates - G1-G4 domain gates - Auto-fix attempts - Limitation emission ### Validation Checklist **Pre-Flight Validation (before skill execution):** - [ ] All skill files exist (main.md + 5 sub-*.md) - [ ] YAML frontmatter valid in all files - [ ] SECOND-KNOWLEDGE-BRAIN.md accessible - [ ] Python modules compile without syntax errors **Runtime Validation (during skill execution):** - [ ] Each step completes before next begins - [ ] Internal gates pass or auto-fix attempted - [ ] Degradation level tracked accurately - [ ] Language detection consistent **Post-Execution Validation (after skill execution):** - [ ] All 10 gates evaluated - [ ] Verdict is one of 4 declared categories - [ ] Disclosure present before verdict - [ ] JSON and Markdown outputs generated - [ ] Post-Execution Gate Checklist complete ### Validation Error Handling | Error Type | Detection | Recovery | |------------|-----------|----------| | Missing skill file | File not found | Log error, skip sub-skill, escalate degradation | | Invalid frontmatter | YAML parse error | Use default metadata, log warning | | Internal gate failure | Gate check returns False | Auto-fix (2 retries), then emit limitation | | Verdict invalid | Verdict ∉ {4 categories} | Force to "Inconclusive", flag | | Output write failure | IO error | Log error, return exit code 2 | ## Skill Extension Protocol ### Adding New Sub-Skills 1. Create `skills/sub-{name}.md` with proper frontmatter 2. Implement correspon … ## 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-theorycrafting-meta-analysis-agent-skill](https://github.com/dungnotnull/esports-theorycrafting-meta-analysis-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-theorycrafting-meta-analysis-agent-skill-esports-theorycrafting-meta-analysis-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%.