Skill: achillesprotocol/ai-research-eta-optimization (version 1.0.0)

# AI Research Task ETA Optimization Workflow **Created:** 2026-04-11 **Based on:** Arkose Labs 50-Prospect Research (1h 41m vs. 5-6h estimate) **Status:** ✅ Operational --- ## Quick Reference Formula ``` AI Research Time = Human Benchmark × 0.2-0.3 Standard Estimate - 30% buffer = Realistic AI Timeline Optimized scenarios = Standard Estimate - 50% ``` **Example:** - Human research (50 prospects): 8-10 hours - AI agent optimized: 2-3 hours - With parallel execution: 1-2 hours --- ## Dynamic ETA Protocol ### Hour 1 (Start): Conservative Initial Estimate - Commit to conservative timeline - Add note: "ETA may accelerate based on execution patterns" - **Do not** commit to fixed deadline ### Hour 2 (Mid-Task): Re-evaluate - Check if accelerating or decelerating - Look for parallel execution opportunities - Adjust ETA if needed - **If accelerating:** Notify stakeholder early ### Hour 3+: Confirm or Finalize - Confirm final ETA - Adjust if unexpected patterns emerge - Document acceleration/deceleration factors --- ## Parallel Execution Optimization ### When to Parallelize - Multiple web searches - Cross-source verification - Data aggregation - Template filling ### How to Parallelize ``` ✅ Use concurrent web_search() calls ✅ Batch data verification tasks ✅ Run multiple source queries simultaneously ✅ Avoid sequential bottlenecks ``` **Impact:** +30-60 minutes on 2-4 hour tasks --- ## Smart Filtering Framework ### Early Elimination Criteria - **Low fraud signal:** <2 verifiable incidents - **Weak Arkose fit:** No clear value proposition - **Missing decision-makers:** Cannot identify contacts - **Low urgency:** No recent incidents or regulatory pressure ### Prioritization Strategy 1. **Tier 1 (Urgent):** Recent breach + regulatory action + clear value prop 2. **Tier 2 (High):** Strong fraud signal + good fit 3. **Tier 3 (Long-term):** Moderate signal, build over time **Impact:** +50-60 minutes saved, focus on high-value prospects --- ## Template-Driven Research ### Pre-Built Templates - Prospect dossier structure - Verification protocol checklist - Decision-maker mapping framework - Value proposition calculator ### Consistent Patterns - Standard data collection (company, fraud signals, fit analysis) - Reusable source verification (15 high-signal sources) - Automated prioritization scoring **Impact:** +30-40 minutes saved per task --- ## Communication Protocol ### Early Acceleration Detection **Signs of potential speedup:** - First 3-5 prospects completed faster than expected - Parallel execution running smoothly - No verification roadblocks - High-signal sources yielding quick results **Action:** - Send progress update: "Accelerating faster than expected" - Adjust ETA: "Completing ~3 hours early" - Maintain quality standards ### Real-Time Progress Updates **Instead of:** Silent execution until completion **Use:** - Hourly status (if task >2 hours) - Early acceleration alerts - Mid-task ETA adjustments **Impact:** Reduced stakeholder anxiety, better expectations management --- ## Token Efficiency Benchmarks ### Target Metrics - **Tokens/prospect:** 10-15k (sweet spot for quality) - **Output ratio:** 3-5% of total tokens - **Token/hour:** 300-400k (sustainable pace) ### Red Flags - >20k tokens/prospect = over-researching - <8k tokens/prospect = potentially skipping verification - <3% output ratio = excessive reasoning - >500k tokens/hour = burning through efficiency --- ## Quality Gates (Must Maintain) ### Non-Negotiables - ✅ All fraud signals verified from 2+ sources - ✅ Decision-makers mapped (LinkedIn, org charts, SEC filings) - ✅ Clear Arkose Labs value proposition for each prospect - ✅ Recent incidents prioritized (last 12-18 months) - ✅ Urgency signals flagged ### Acceptable Trade-offs (if accelerating) - ⚠️ Some Tier 3 prospects may have older incident data - ⚠️ Decision-maker names may vary (role identification acceptable) - ⚠️ Dark web claims flagged as [NEEDS VERIFICATION] --- ## Implementation Checklist **Before Starting Task:** - [ ] Create prospect dossier templates - [ ] Identify 15 high-signal data sources - [ ] Prepare filtering criteria - [ ] Set up parallel execution plan - [ ] Establish communication protocol **During Task:** - [ ] Monitor execution speed in Hour 1 - [ ] Identify acceleration opportunities in Hour 2 - [ ] Send progress update if accelerating - [ ] Document patterns for future tasks - [ ] Maintain quality gates **After Task:** - [ ] Calculate actual runtime vs. estimate - [ ] Document acceleration factors - [ ] Update benchmarks if needed - [ ] Share results with stakeholders - [ ] Refine templates for next task --- ## Success Metrics ### Excellent Performance (9-10/10) - 3x+ faster than estimate - Zero quality compromises - All deliverables complete - High token efficiency ### Good Performance (7-8/10) - 2x+ faster than estimate - Minor quality trade-offs acceptable - All core deliverables complete - Reasonable token usage ### Needs Improvement (below 7/10) - Slower than estimate - Quality compromises - Missing deliverables - Inefficient token usage --- **Last Updated:** 2026-04-11 **Based on:** 1 optimized research task (Arkose Labs) **Next Review:** After 10 tasks (update benchmarks)


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