Agent skill

a3-problem-solver

A3 problem-solving and status reporting skill with structured thinking and coaching support

Stars 514
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/operations/skills/a3-problem-solver

Metadata

Additional technical details for this skill

domain
business
category
continuous-improvement
specialization
operations

SKILL.md

A3 Problem Solver

Overview

The A3 Problem Solver skill provides comprehensive capabilities for structured problem-solving using the A3 methodology. It supports problem statement crafting, root cause investigation, countermeasure development, and PDCA-based follow-up tracking.

Capabilities

  • A3 template facilitation
  • Problem statement crafting
  • Current condition analysis
  • Target condition definition
  • Gap analysis
  • Root cause investigation
  • Countermeasure development
  • Follow-up tracking

Used By Processes

  • CI-002: A3 Problem Solving
  • LEAN-003: Kaizen Event Facilitation
  • SIX-005: Root Cause Analysis

Tools and Libraries

  • A3 templates
  • Coaching frameworks
  • Collaboration tools
  • Progress tracking systems

Usage

yaml
skill: a3-problem-solver
inputs:
  problem_type: "problem_solving"  # problem_solving | proposal | status_report
  problem_owner: "John Smith"
  coach: "Jane Doe"
  problem_description: "Customer lead time increased from 5 days to 8 days"
  current_condition:
    metric: "lead_time"
    current_value: 8
    unit: "days"
  target_condition:
    target_value: 4
    unit: "days"
    timeline: "90 days"
outputs:
  - a3_document
  - problem_statement
  - root_cause_analysis
  - countermeasure_plan
  - implementation_schedule
  - follow_up_checklist

A3 Document Sections

Left Side (Understanding)

Section Purpose
Background Why is this important now?
Current Condition What is happening today?
Target Condition What should be happening?
Gap Analysis What is the difference?
Root Cause Analysis Why does the gap exist?

Right Side (Action)

Section Purpose
Countermeasures What will we do?
Implementation Plan How and when?
Follow-up How will we verify?
Results What did we achieve?

Problem Statement Criteria

A good problem statement:

  • Specific - Clear and measurable
  • Observable - Based on data/facts
  • Non-judgmental - No blame
  • Gap-focused - Current vs. target
  • Bounded - Defined scope

Example

Poor: "Quality is bad" Good: "Defect rate on Line 3 increased from 2% to 5% in Q4 2025"

A3 Thinking Process

PDCA Cycle

  1. Plan - Understand problem, identify root cause, develop countermeasures
  2. Do - Implement countermeasures
  3. Check - Verify results
  4. Act - Standardize or adjust

Coaching Questions

Section Coaching Questions
Problem Is it measurable? Based on data?
Current Do you understand the process?
Target Is it realistic? Stretch enough?
Root Cause Did you verify with data?
Countermeasures Do they address root causes?
Implementation Who, what, when?

Integration Points

  • Project tracking systems
  • Knowledge management
  • Coaching platforms
  • Performance dashboards

Expand your agent's capabilities with these related and highly-rated skills.

a5c-ai/babysitter

gsd-tools

Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).

514 31
Explore
a5c-ai/babysitter

model-profile-resolution

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

514 31
Explore
a5c-ai/babysitter

verification-suite

Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

514 31
Explore
a5c-ai/babysitter

state-management

STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick tasks.

514 31
Explore
a5c-ai/babysitter

git-integration

Git commit patterns, formats, and conventions for GSD methodology. Provides atomic commits per task, structured commit messages, planning file commits, branch management, and milestone tag operations.

514 31
Explore
a5c-ai/babysitter

frontmatter-parsing

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

514 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results