When Finding High Ev Hackathons Use Ev Optimizer

---
name: when-finding-high-ev-hackathons-use-ev-optimizer
description: /============================================================================/
---

/*============================================================================*/
/* 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] <promise>SKILL_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]