using-agentops

name	using-agentops
description	Meta skill explaining the AgentOps workflow. Auto-injected on session start. Covers RPI workflow, Knowledge Flywheel, and skill catalog.

AgentOps Workflow

You have access to the AgentOps skill set for structured development workflows.

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/

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

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

Skill	Purpose
/research	Deep codebase exploration
/pre-mortem	Failure simulation before implementing
/plan	Epic decomposition into issues
/implement	Execute single issue
/crank	Autonomous epic loop (uses swarm for each wave)
/swarm	Fresh-context parallel execution (Ralph pattern)
/vibe	Code validation
/retro	Extract learnings
/post-mortem	Full validation + knowledge extraction
/beads	Issue tracking operations
/bug-hunt	Root cause analysis
/knowledge	Query knowledge artifacts
/complexity	Code complexity analysis
/doc	Documentation generation
/provenance	Trace artifact lineage to sources
/trace	Trace design decisions through history

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
"What could go wrong with this?"	/pre-mortem
"Let's execute this epic"	/crank
"Spawn agents to work in parallel"	/swarm
"How did we decide on this?"	/trace
"Where did this learning come from?"	/provenance

Issue Tracking

AgentOps 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