---

name: using-agentops description: 'Meta skill explaining the RPI workflow. Auto-injected on session start. Covers Research-Plan-Implement workflow, Knowledge Flywheel, and skill catalog.' skill_api_version: 1 user-invocable: false context: window: isolated intent: mode: none sections: exclude: [HISTORY, INTEL, TASK] intel_scope: none metadata: tier: meta dependencies: [] internal: true

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
ao search "<query>"    # Search existing knowledge
ao lookup <id>         # Pull full content of specific learning
ao lookup --query "x"  # Search knowledge by relevance

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

Plan Phase

/pre-mortem <spec>     # Simulate failures (error/rescue map, scope modes, prediction tracking)
/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 (finding classification + suppression + domain checklists)
/post-mortem           # Validation + streak tracking + prediction accuracy + retro history
/retro                 # Quick-capture a single learning

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
Discovery	/discovery	/brainstorm, /research, /plan, /pre-mortem
Implement	/crank	/implement (single issue), /swarm (parallel execution)
Validate	/validation	/vibe, /post-mortem, /retro, /forge
Release	/release	—

Choosing the skill:

• Use /implement for single issue execution. Now defaults to TDD-first — writes failing tests before implementing. Skip with --no-tdd.
• Use /crank for autonomous epic execution (loops waves via swarm until done). Auto-generates file-ownership maps to prevent worker conflicts.
• Use /swarm directly for parallel execution without beads (TaskList only).
• Use /discovery for the discovery phase only (brainstorm → search → research → plan → pre-mortem).
• Use /validation for the validation phase only (vibe → post-mortem → retro → forge).
• Use /rpi for full lifecycle — delegates to /discovery → /crank → /validation.
• Use /ratchet to gate/record progress through RPI.

Available Skills

Start Here (11 starters)

These are the skills every user needs first. Everything else is available when you need it.

Skill	Purpose
/quickstart	Guided onboarding — run this first
/research	Deep codebase exploration
/council	Multi-model consensus review + finding auto-extraction
/vibe	Code validation (classification + suppression + domain checklists)
/rpi	Full RPI lifecycle orchestrator (research → plan → implement → validate)
/implement	Execute single issue
/retro --quick	Quick-capture a single learning into the flywheel
/status	Single-screen dashboard of current work and suggested next action
/goals	Maintain GOALS.yaml fitness specification
/push	Atomic test-commit-push workflow
/flywheel	Knowledge flywheel health monitoring (σ×ρ > δ)

Advanced Skills (when you need them)

Skill	Purpose
/athena	Active knowledge intelligence — Mine → Grow → Defrag cycle
/brainstorm	Structured idea exploration before planning
/discovery	Full discovery phase orchestrator (brainstorm → search → research → plan → pre-mortem)
/plan	Epic decomposition into issues
/pre-mortem	Failure simulation (error/rescue, scope modes, temporal, predictions)
/post-mortem	Validation + streak tracking + prediction accuracy + retro history
/bug-hunt	Root cause analysis
/release	Pre-flight, changelog, version bumps, tag
/crank	Autonomous epic loop (uses swarm for each wave)
/swarm	Fresh-context parallel execution (Ralph pattern)
/evolve	Goal-driven fitness-scored improvement loop
/doc	Documentation generation
/retro	Quick-capture a learning (full retro → /post-mortem)
/validation	Full validation phase orchestrator (vibe → post-mortem → retro → forge)
/ratchet	Brownian Ratchet progress gates for RPI workflow
/forge	Mine transcripts for knowledge — decisions, learnings, patterns
/readme	Generate gold-standard README for any project
/security	Continuous repository security scanning and release gating
/security-suite	Binary and prompt-surface security suite — static analysis, dynamic tracing, offline redteam, policy gating

Expert Skills (specialized workflows)

Skill	Purpose
/grafana-platform-dashboard	Build Grafana platform dashboards from templates/contracts
/codex-team	Parallel Codex agent execution
/openai-docs	Official OpenAI docs lookup with citations
/oss-docs	OSS documentation scaffold and audit
/reverse-engineer-rpi	Reverse-engineer a product into feature catalog and specs
/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
/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:

bd ready              # Unblocked issues
bd show <id>          # Issue details
bd close <id>         # Close issue
bd vc status          # Inspect Dolt state if needed (JSONL auto-sync is automatic)

Examples

SessionStart Context Loading

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

What happens:

1. In manual mode (default): MEMORY.md is auto-loaded by Claude Code; hook emits a pointer to on-demand retrieval (ao search, ao lookup)
2. In lean mode: hook extracts pending knowledge and injects prior learnings with a reduced token budget
3. Hook injects this skill automatically into session context
4. Agent loads RPI workflow overview, phase-to-skill mapping, trigger patterns
5. User says "check my code" → agent recognizes /vibe trigger naturally

Result: Agent knows the full skill catalog and workflow from session start. MEMORY.md is auto-loaded by default (manual mode). Set AGENTOPS_STARTUP_CONTEXT_MODE=lean for automatic knowledge injection alongside MEMORY.md.

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