RPI Workflow

You have access to workflow skills for structured development.

The RPI Workflow

Research → Plan → Implement → Validate
    ↑                            │
    └──── Knowledge Flywheel ────┘

Research Phase

/research <topic>      # Deep codebase exploration
/knowledge <query>     # Query existing knowledge

Output: .agents/research/<topic>.md

Plan Phase

/pre-mortem <spec>     # Simulate failures before implementing
/plan <goal>           # Decompose into trackable issues

Output: Beads issues with dependencies

Implement Phase

/implement <issue>     # Single issue execution
/crank <epic>          # Autonomous epic loop (uses swarm for waves)
/swarm                 # Parallel execution (fresh context per agent)

Output: Code changes, tests, documentation

Validate Phase

/vibe [target]         # Code validation (security, quality, architecture)
/post-mortem           # Extract learnings after completion
/retro                 # Quick retrospective

Output: .agents/learnings/, .agents/patterns/

Release Phase

/release [version]     # Full release: changelog + bump + commit + tag
/release --check       # Readiness validation only (GO/NO-GO)
/release --dry-run     # Preview without writing

Output: Updated CHANGELOG.md, version bumps, git tag, .agents/releases/

Phase-to-Skill Mapping

Phase	Primary Skill	Supporting Skills
Research	/research	/knowledge, /inject
Plan	/plan	/pre-mortem
Implement	/implement	/crank (epic loop), /swarm (parallel execution)
Validate	/vibe	/retro, /post-mortem
Release	/release	—

Choosing the skill:

• Use /implement for single issue execution.
• Use /crank for autonomous epic execution (loops waves via swarm until done).
• Use /swarm directly for parallel execution without beads (TaskList only).
• Use /ratchet to gate/record progress through RPI.

Available Skills (42 user-facing)

Core Skills (start here)

Skill	Purpose
/research	Deep codebase exploration
/brainstorm	Structured idea exploration before planning
/plan	Epic decomposition into issues
/implement	Execute single issue
/vibe	Code validation (complexity + multi-model council)
/status	Single-screen dashboard of current work and suggested next action

Power Skills (when you're ready)

Skill	Purpose
/council	Multi-model consensus review (validate, brainstorm, research)
/pre-mortem	Failure simulation before implementing
/post-mortem	Full validation + knowledge extraction
/bug-hunt	Root cause analysis
/release	Pre-flight, changelog, version bumps, tag
/crank	Autonomous epic loop (uses swarm for each wave)
/doc	Documentation generation
/retro	Extract learnings from completed work
/knowledge	Query knowledge artifacts
/learn	Capture knowledge manually into the flywheel

Expert Skills (advanced workflows)

Skill	Purpose
/swarm	Fresh-context parallel execution (Ralph pattern)
/rpi	Full RPI lifecycle orchestrator (research → plan → implement → validate)
/evolve	Goal-driven fitness-scored improvement loop
/codex-team	Parallel Codex agent execution
/openai-docs	Official OpenAI docs lookup with citations
/oss-docs	OSS documentation scaffold and audit
/pr-research	Upstream repository research before contribution
/pr-plan	External contribution planning
/pr-implement	Fork-based PR implementation
/pr-validate	PR-specific validation and isolation checks
/pr-prep	PR preparation and structured body generation
/pr-retro	Learn from PR outcomes
/complexity	Code complexity analysis
/product	Interactive PRODUCT.md generation
/handoff	Session handoff for continuation
/inbox	Agent mail monitoring
/recover	Post-compaction context recovery
/trace	Trace design decisions through history
/provenance	Trace artifact lineage to sources
/beads	Issue tracking operations
/heal-skill	Detect and fix skill hygiene issues
/converter	Convert skills to Codex/Cursor formats
/update	Reinstall all AgentOps skills from latest source

Knowledge Flywheel

Every /post-mortem feeds back to /research:

1. Learnings extracted → .agents/learnings/
2. Patterns discovered → .agents/patterns/
3. Research enriched → Future sessions benefit

Natural Language Triggers

Skills auto-trigger from conversation:

Say This	Runs
"I need to understand how auth works"	/research
"Check my code for issues"	/vibe
"Review my code"	/vibe
"What could go wrong with this?"	/pre-mortem
"Let's execute this epic"	/crank
"Execute this epic"	/crank
"Spawn agents to work in parallel"	/swarm
"Run tasks in parallel"	/swarm
"Debug this"	/bug-hunt
"Remember this" / "I learned something"	/learn
"How did we decide on this?"	/trace
"Where did this learning come from?"	/provenance
"Cut a release"	/release
"Are we ready to release?"	/release --check
"What am I working on?"	/status
"Get started" / "How do I start?"	/quickstart
"Define the product"	/product
"Run the full lifecycle"	/rpi
"Improve toward goals"	/evolve
"Where was I?" / "Lost context"	/recover
"End session" / "Pick up later"	/handoff
"Update skills"	/update

Issue Tracking

This workflow uses beads for git-native issue tracking:

bd ready              # Unblocked issues
bd show <id>          # Issue details
bd close <id>         # Close issue
bd sync               # Sync with git

Examples

SessionStart Auto-Injection

Hook triggers: session-start.sh runs at session start

What happens:

1. Hook injects this skill automatically into session context
2. Agent loads RPI workflow overview, phase-to-skill mapping, trigger patterns
3. Agent understands available skills without user prompting
4. User says "check my code" → agent recognizes /vibe trigger naturally
5. Agent executes skill using workflow knowledge from this reference

Result: Agent knows the full skill catalog and workflow from session start, enabling natural language skill invocation.

Workflow Reference During Planning

User says: "How should I approach this feature?"

What happens:

1. Agent references this skill's RPI workflow section
2. Agent recommends Research → Plan → Implement → Validate phases
3. Agent suggests /research for codebase exploration, /plan for decomposition
4. Agent explains /pre-mortem for failure simulation before implementation
5. User follows recommended workflow with agent guidance

Result: Agent provides structured workflow guidance based on this meta-skill, avoiding ad-hoc approaches.

Troubleshooting

Problem	Cause	Solution
Skill not auto-loaded	Hook not configured or SessionStart disabled	Verify hooks/session-start.sh exists; check hook enable flags
Outdated skill catalog	This file not synced with actual skills/ directory	Update skill list in this file after adding/removing skills
Wrong skill suggested	Natural language trigger ambiguous	User explicitly calls skill with /skill-name syntax
Workflow unclear	RPI phases not well-documented here	Read full workflow guide in README.md or docs/ARCHITECTURE.md