tomevault-io 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
/implementfor single issue execution. - Use
/crankfor autonomous epic execution (loops waves via swarm until done). - Use
/swarmdirectly for parallel execution without beads (TaskList only). - Use
/ratchetto 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:
- Learnings extracted →
.agents/learnings/ - Patterns discovered →
.agents/patterns/ - 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
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