DNYoussef /============================================================================/ /* 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:
- Collector (researcher) - Scrape hackathon/bounty listings, normalize metadata
- EVCalc (analyst) - Calculate expected value: (prize × p_win) − time_cost
- TeamBuilder (researcher) - Identify skill gaps, generate outreach drafts
- 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
#!/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]