techwavedev Progressive Estimation
Estimate AI-assisted and hybrid human+agent development work using research-backed formulas with PERT statistics, confidence bands, and calibration feedback loops.
Overview
Progressive Estimation adapts to your team's working mode — human-only, hybrid, or agent-first — applying the right velocity model and multipliers for each. It produces statistical estimates rather than gut feelings.
When to Use This Skill
- Estimating development tasks where AI agents handle part of the work
- Sprint planning with hybrid human+agent teams
- Batch sizing a backlog (handles 5 or 500 issues)
- Staffing and capacity planning with agent multipliers
- Release date forecasting with confidence intervals
How It Works
- Mode Detection — Determines if the team works human-only, hybrid, or agent-first
- Task Classification — Categorizes by size (XS–XL), complexity, and risk
- Formula Application — Applies research-backed multipliers grounded in empirical studies
- PERT Calculation — Produces expected values using three-point estimation
- Confidence Bands — Generates P50, P75, P90 intervals
- Output Formatting — Formats for Linear, JIRA, ClickUp, GitHub Issues, Monday, or GitLab
- Calibration — Feeds back actuals to improve future estimates
Examples
Single task:
"Estimate building a REST API with authentication using Claude Code"
Batch mode:
"Estimate these 12 JIRA tickets for our next sprint"
With context:
"We have 3 developers using AI agents for ~60% of implementation. Estimate this feature."
Best Practices
- Start with a single task to calibrate before moving to batch mode
- Feed back actual completion times to improve the calibration system
- Use "instant mode" for quick T-shirt sizing without full PERT analysis
- Be explicit about team composition and agent usage percentage
Common Pitfalls
Problem: Overconfident estimates Solution: Use P75 or P90 for commitments, not P50
Problem: Missing context Solution: The skill asks clarifying questions — provide team size and agent usage
Problem: Stale calibration Solution: Re-calibrate when team composition or tooling changes significantly
Related Skills
@sprint-planning- Sprint planning and backlog management@project-management- General project management workflows@capacity-planning- Team velocity and capacity planning
Additional Resources
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Progressive Estimation"
Storing Results
After completing work, store AI agent orchestration decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
--type decision --project <project> \
--tags progressive-estimation ai-agents
Multi-Agent Collaboration
This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
--project <project>
Control Tower Integration
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Blockchain Identity
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.