Agent skill

when-finding-high-ev-hackathons-use-ev-optimizer

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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/when-finding-high-ev-hackathons-use-ev-optimizer

SKILL.md

/============================================================================/ /* 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]

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