using-agentops

Original：🇺🇸 English
Translated

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

4installs
Sourceboshu2/agentops
Added on

NPX Install

npx skill4agent add boshu2/agentops using-agentops

Tags

Translated version includes tags in frontmatter
agentops-workflowrpi-frameworkknowledge-flywheelskill-catalogissue-tracking

SKILL.md Content

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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

bash
/research <topic>      # Deep codebase exploration
/knowledge <query>     # Query existing knowledge
Output: 
.agents/research/<topic>.md

Plan Phase

bash
/pre-mortem <spec>     # Simulate failures before implementing
/plan <goal>           # Decompose into trackable issues
Output: Beads issues with dependencies

Implement Phase

bash
/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

bash
/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

PhasePrimary SkillSupporting 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

SkillPurpose
/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 ThisRuns
"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:
bash
bd ready              # Unblocked issues
bd show <id>          # Issue details
bd close <id>         # Close issue
bd sync               # Sync with git