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As a developer using AI-assisted development tools I need the pair Knowledge Base to provide executable Agent Skills (agentskills.io standard) alongside the existing documentation So that AI assistants can invoke structured, repeatable workflows instead of interpreting free-form markdown, improving consistency and reducing manual process overhead
Epic Context
The pair KB currently provides excellent documentation (how-to, guidelines, adoption) but relies on AI assistants interpreting free-form markdown for every interaction. By introducing Agent Skills, we transform passive documentation into executable units that AI assistants invoke directly. This epic establishes the skills infrastructure, migrates operational content from how-to and guidelines into skills, and supports 5 AI tools (Claude Code, GitHub Copilot, Cursor, OpenAI Codex, Windsurf) through a registry-based installation system.
Goal 1: Transform KB from passive documentation to executable skills, reducing AI interpretation variability
Goal 2: Support the agentskills.io open standard across 5 major AI tools with a single source of truth
Goal 3: Enable incremental adoption — skills coexist with existing how-to documents, no breaking changes
Success Metrics (KPIs)
Skill Invocation Success Rate: Target ≥ 90% of skill invocations complete without manual fallback
Measurement method: Manual testing with Claude Code and Copilot
Current baseline: 0% (no skills exist)
How-to Size Reduction: Target ~60% reduction in how-to file sizes (operational content migrated to skills)
Measurement method: Line count comparison before/after
Current baseline: 11 how-to files, ~3000+ total lines
Multi-tool Coverage: Target 5 AI tools with working skill installation
Measurement method: Successful pair install with skill targets
Current baseline: 0 tools with skills support
Problem Statement & Market Opportunity
Current State
AI assistants read how-to files as free-form markdown and must interpret instructions each time. This leads to inconsistent execution, missed steps, and no structured argument passing or tool restrictions. Guidelines contain decision matrices embedded in educational content, making operational workflows hard to extract programmatically.
Target State
30 Agent Skills (SKILL.md format) organized in dataset/.skills/ with registry-based installation to 5 AI tools. How-to files become thin orchestrators (~30-40% of current size) that compose skills. Guidelines retain educational content while operational workflows live in capability skills. The adoption binding (Argument > Adoption > Assessment cascade) enables skills to adapt to project-specific decisions.
Pain Points Addressed
Inconsistent AI execution: Free-form markdown interpretation varies across sessions and tools
Impact: Developer must re-verify and correct AI behavior frequently
Frequency: Every AI-assisted session
Tool fragmentation: Each AI tool requires separate prompt/agent configuration
Impact: Maintenance burden multiplied by number of supported tools
Frequency: Every KB update
Target Users & Personas
Primary Users
Developer using AI assistants: Uses Claude Code, Copilot, Cursor, or other AI tools daily for development tasks. Wants consistent, reliable AI behavior when following pair processes.
Team lead / KB maintainer: Manages the pair KB for their team. Wants a single source of truth that works across all AI tools without maintaining separate configurations.
Solution Overview
Core Solution Approach
Introduce Agent Skills (agentskills.io) into dataset/.skills/ with a two-dimensional model: process skills (from how-to lifecycle) and capability skills (from actionable guidelines). Registry-based installation with prefix/flatten configuration distributes skills to each tool's expected folder via canonical + symlink targets.
Key Capabilities
Registry-based skill installation: Extend content-ops with prefix, flatten, targets config to install skills to 5 AI tool folders
Value: Single source, multi-tool distribution with zero duplication
Process skills (11): Executable versions of how-to phases (implement, review, plan-tasks, etc.)
Value: Consistent, structured execution of development lifecycle phases
Capability skills (18): Assessment, workflow, compliance, and setup skills extracted from actionable guidelines
Epic Information
Epic ID: EP-006
Initiative: Dynamic Knowledge Base Management #64
Epic Owner: Product Engineer
Status: Done (8/8 stories completed — P0 Circle 1: 4/4, Circle 2: 4/4)
Priority: P0
Epic Points: 30-40 points (estimated)
Target Release: KB Skills Release
Epic Statement
As a developer using AI-assisted development tools
I need the pair Knowledge Base to provide executable Agent Skills (agentskills.io standard) alongside the existing documentation
So that AI assistants can invoke structured, repeatable workflows instead of interpreting free-form markdown, improving consistency and reducing manual process overhead
Epic Context
The pair KB currently provides excellent documentation (how-to, guidelines, adoption) but relies on AI assistants interpreting free-form markdown for every interaction. By introducing Agent Skills, we transform passive documentation into executable units that AI assistants invoke directly. This epic establishes the skills infrastructure, migrates operational content from how-to and guidelines into skills, and supports 5 AI tools (Claude Code, GitHub Copilot, Cursor, OpenAI Codex, Windsurf) through a registry-based installation system.
Design specification:
docs/design/skills-in-knowledge-base.mdBusiness Value & Objectives
Primary Business Goals
Success Metrics (KPIs)
Skill Invocation Success Rate: Target ≥ 90% of skill invocations complete without manual fallback
How-to Size Reduction: Target ~60% reduction in how-to file sizes (operational content migrated to skills)
Multi-tool Coverage: Target 5 AI tools with working skill installation
pair installwith skill targetsProblem Statement & Market Opportunity
Current State
AI assistants read how-to files as free-form markdown and must interpret instructions each time. This leads to inconsistent execution, missed steps, and no structured argument passing or tool restrictions. Guidelines contain decision matrices embedded in educational content, making operational workflows hard to extract programmatically.
Target State
30 Agent Skills (SKILL.md format) organized in
dataset/.skills/with registry-based installation to 5 AI tools. How-to files become thin orchestrators (~30-40% of current size) that compose skills. Guidelines retain educational content while operational workflows live in capability skills. The adoption binding (Argument > Adoption > Assessment cascade) enables skills to adapt to project-specific decisions.Pain Points Addressed
Inconsistent AI execution: Free-form markdown interpretation varies across sessions and tools
Tool fragmentation: Each AI tool requires separate prompt/agent configuration
Target Users & Personas
Primary Users
Developer using AI assistants: Uses Claude Code, Copilot, Cursor, or other AI tools daily for development tasks. Wants consistent, reliable AI behavior when following pair processes.
Team lead / KB maintainer: Manages the pair KB for their team. Wants a single source of truth that works across all AI tools without maintaining separate configurations.
Solution Overview
Core Solution Approach
Introduce Agent Skills (agentskills.io) into
dataset/.skills/with a two-dimensional model: process skills (from how-to lifecycle) and capability skills (from actionable guidelines). Registry-based installation with prefix/flatten configuration distributes skills to each tool's expected folder via canonical + symlink targets.Key Capabilities
Registry-based skill installation: Extend content-ops with
prefix,flatten,targetsconfig to install skills to 5 AI tool foldersProcess skills (11): Executable versions of how-to phases (implement, review, plan-tasks, etc.)
Capability skills (18): Assessment, workflow, compliance, and setup skills extracted from actionable guidelines
Navigator skill (/next): Reads project state, suggests next skill to invoke
Solution Differentiators
Epic Breakdown
User Stories — Execution Order
Circle 1: Sprint Cycle (P0)
Circle 2: Bootstrap & Planning (P1)
Epic Acceptance Criteria
dataset/.skills/with valid frontmatterpair installdistributes skills to.claude/skills/,.github/skills/,.cursor/skills/,.agents/skills/,.windsurf/skills/using canonical + symlinkTechnical Considerations
Architecture Overview
dataset/.skills/— skills in categories (process/,capability/) or top-level (next/)prefix: pair,flatten: true→pair-process-implement/SKILL.md.claude/skills/(canonical), others as directory-level symlinksTechnical Requirements
prefix,flatten,targetsin registry config.github/skills/→.claude/skills/etc.Technical Risks
Timeline & Roadmap
Development Phases
Circle 1: Sprint Cycle (P0) — Stories #98, #99, #100, #101
Circle 2: Bootstrap & Planning (P1) — Stories #102, #104, #103, #105
Critical Path Dependencies
Risk Management
Business Risks
Change Log
.skills/navigator/next/→.skills/next/for simpler naming (pair-nextinstead ofpair-navigator-next). Updated AC1 and architecture paths.