# When Finding High Ev Hackathons Use Ev Optimizer > When Finding High Ev Hackathons Use Ev Optimizer - Skill: `dnyoussef/when-finding-high-ev-hackathons-use-ev-optimizer` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-finding-high-ev-hackathons-use-ev-optimizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-finding-high-ev-hackathons-use-ev-optimizer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-finding-high-ev-hackathons-use-ev-optimizer --- /*============================================================================*/ /* SKILL SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: skill version: 1.0.0 description: | [assert|neutral] Expected value optimization for hackathons and bounties with judge analysis and MVS generation [ground:given] [conf:0.95] [state:confirmed] category: revenue-generation tags: - general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute skill workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic revenue-generation processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "skill", category: "revenue-generation", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["skill", "revenue-generation", "workflow"], context: "user needs skill capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # Hackathon EV Optimizer ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. Expected Value (EV) calculator for hackathons and bounties. Optimizes for prize × p(win) − time_cost with judge fit analysis and past win pattern learning. ## Overview This skill orchestrates 3 specialist agents to: 1. **Collector** (researcher) - Scrape hackathon/bounty listings, normalize metadata 2. **EVCalc** (analyst) - Calculate expected value: (prize × p_win) − time_cost 3. **TeamBuilder** (researcher) - Identify skill gaps, generate outreach drafts 4. **SubmissionKit** (coder) - Auto-generate README, demo script, form fills **Critical differentiator**: Learns from your past project wins to estimate p(win) for new competitions. ## When to Use - **Weekly**: Scan for new hackathons/bounties (Monday, Thursday) - **Before commitment**: Calculate EV before dedicating 48 hours - **Team formation**: Identify skill gaps and potential collaborators - **Quick wins**: Find high-EV, low-time-cost opportunities ## Assigned Agents ### Primary Agents **researcher (Collector role)** - Phase 1: Web scraping, metadata extraction, judge research - Expertise: API integration, web crawling, YAML processing - Tools: curl, puppeteer, jq, yq - Output: Normalized hackathon CSV with prizes, themes, judges, deadlines **analyst (EVCalc role)** - Phase 2: Probability estimation, EV calculation, risk analysis - Expertise: Statistical modeling, decision analysis, pattern matching - Tools: Python/Node scoring, similarity algorithms, historical analysis - Output: Ranked opportunities with EV justifications ### Secondary Agents **researcher (TeamBuilder role)** - Phase 3: Skill gap analysis, network mapping - Expertise: Competency mapping, collaboration strategy - Tools: LinkedIn API, GitHub analysis, outreach templates - Output: Team composition recommendations + outreach emails **coder (SubmissionKit role)** - Phase 4: Automation, template generation, form filling - Expertise: Boilerplate generation, documentation, automation - Tools: Template engines, README generators, form auto-fill scripts - Output: MVS (Minimum Viable Submission) package ## Coordination Pattern ``` SKILL: hackathon-ev-optimizer ↓ hierarchical-coordinator spawns 4 sequential phases ↓ Phase 1: Collector (researcher) → raw hackathon data Phase 2: EVCalc (analyst) → EV-ranked opportunities Phase 3: TeamBuilder (researcher) → skill gaps + outreach Phase 4: SubmissionKit (coder) → MVS packages for top 3 ↓ All phases coordinate via Memory MCP with WHO/WHEN/PROJECT/WHY tagging ``` --- ## Phase 1: Collector (Hackathon Scanning) **Agent**: `researcher` (Collector role) ### Inputs - `data/sources/hackathons.yml` - Hackathon aggregators, bounty platforms - Web scraping targets (DevPost, Gitcoin, DoraHacks, etc.) ### Commands Executed ```bash #!/bin/bash # Phase 1: Hackathon Data Collection # PRE-TASK HOOK npx claude-flow@alpha hooks pre-task \ --description "Hackathon EV: opportunity scanning" \ --agent "researcher" \ --role "Collector" \ --skill "hackathon-ev-optimizer" # SESSION RESTORE npx claude-flow@alpha hooks session-restore \ --session-id "hackathon-ev-$(date +%Y-%W)" # SETUP WEEK=$(date +%Y-%W) mkdir -p outputs/reports outputs/briefs raw_data/hackathons # READ SOURCES PLATFORMS=$(yq eval '.platforms[].url' data/sources/hackathons.yml) echo "slug,name,theme,prize_pool,top_prize,deadline,location,judges,deliverables,url,posted_date" > raw_data/hackathons/events_${WEEK}.csv # SCRAPE PLATFORMS for PLATFORM in $PLATFORMS; do echo "[Collector] Scanning: $PLATFORM" # Example: DevPost API (adapt to actual endpoints) if [[ $PLATFORM == *"devpost"* ]]; then curl -s "https://devpost.com/api/hackathons?status[]=upcoming&status[]=open" \ | jq -r '.hackathons[] | [ .slug, .title, .themes[0].name, (.prizes | map(.amount) | add // 0), (.prizes | max_by(.amount).amount // 0), /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/revenue-generation/skill/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "skill-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] SKILL_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]