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
"
Search existing knowledge
ao lookup < id
Pull full content of specific learning
ao lookup --query "x"
Search knowledge by relevance
Output:
.agents/research/
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
Full validation + knowledge extraction (council + learnings + activation)
/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 Research /research ao lookup Plan /plan /pre-mortem Implement /implement /crank (epic loop), /swarm (parallel execution) Validate /vibe /post-mortem (full retro + knowledge lifecycle), /retro (quick-capture) 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 /rpi for full lifecycle with optional --budget flag for phase time guards (e.g., --budget=research:180,plan:120 ). Use /ratchet to gate/record progress through RPI. Available Skills Start Here (12 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 (validate, brainstorm, research) /vibe Code validation (complexity + multi-model council) /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 /plan Epic decomposition into issues /pre-mortem Failure simulation before implementing /post-mortem Full validation + knowledge lifecycle (council + extraction + activation + retirement) /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) /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 security suite — static analysis, dynamic tracing, 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 : Learnings extracted → .agents/learnings/ Patterns discovered → .agents/patterns/ 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: In manual mode (default): MEMORY.md is auto-loaded by Claude Code; hook emits a pointer to on-demand retrieval ( ao search , ao lookup ) In lean mode: hook extracts pending knowledge and injects prior learnings with a reduced token budget Hook injects this skill automatically into session context Agent loads RPI workflow overview, phase-to-skill mapping, trigger patterns 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: Agent references this skill's RPI workflow section Agent recommends Research → Plan → Implement → Validate phases Agent suggests /research for codebase exploration, /plan for decomposition Agent explains /pre-mortem for failure simulation before implementation 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