Agent skill

using-agentops

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

Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/using-agentops-boshu2-agentops

Metadata

Additional technical details for this skill

tier
meta
internal
YES
dependencies
[]

SKILL.md

RPI Workflow

You have access to workflow skills for structured development.

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/

Release Phase

bash
/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 (43 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

Issue Tracking

This workflow 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

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

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