AgentOps Operating Model

AgentOps is the operational layer for coding agents.

Publicly, it gives you four things:

• Bookkeeping — captured learnings, findings, and reusable context

• Validation — plan and code review before work ships

• Primitives — single skills, hooks, and CLI surfaces

• Flows — named compositions like /research, /validation, and /rpi

Technically, AgentOps acts as a context compiler: raw session signal becomes reusable knowledge, compiled prevention, and better next work.

Core Flow: RPI

Research → Plan → Implement → Validate

    ↑                            │

    └──── Knowledge Flywheel ────┘

Research Phase

/research <topic>      # Deep codebase exploration

ao search "<query>"    # Search existing knowledge

ao search "<query>" --cite retrieved  # Record adoption when a search result is reused

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/

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

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 /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.

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

/bootstrap

One-command full AgentOps setup — fills gaps only

/research

Deep codebase exploration

/council

Multi-model consensus review + finding auto-extraction

/validate

Canonical PASS/WARN/FAIL verdict over an artifact, plan, code change, PR, or gate

/vibe

Code validation (classification + suppression + domain checklists)

/rpi

Full RPI lifecycle orchestrator (/discovery → /crank → /validation)

/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

Advanced Skills (when you need them)

Skill

Purpose

/compile, /flywheel

Active knowledge intelligence and flywheel health — Mine → Grow → Defrag cycle

/curate

Canonical miner role for transcripts, .agents/, bd, git, skill diffs, and rare wiki entries

/llm-wiki

External reading wiki proposal — raw sources to compiled wiki

/expert-council

Alias for /council --mode=debate (kept 1 release) — adversarial named-persona duel

/harvest

Cross-rig knowledge consolidation — sweep, dedup, promote to global hub

/knowledge-activation

Operationalize a mature .agents corpus into beliefs, playbooks, briefings, and gap surfaces

/brainstorm

Structured idea exploration before planning

/discovery

Full discovery phase orchestrator (brainstorm → search → research → plan → pre-mortem)

/plan

Epic decomposition into issues

/design

Product validation gate — goal alignment, persona fit, competitive differentiation

/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

/autodev

PROGRAM.md autonomous development contract setup and validation

/dream

Interactive Dream operator surface for setup, bedtime runs, and morning reports

/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

/test

Test generation, coverage analysis, and TDD workflow

/hooks-authoring

Author and validate AgentOps runtime hooks

/red-team

Persona-based adversarial validation — probe docs and skills from constrained user perspectives

/review

Review incoming PRs, agent output, or diffs — SCORED checklist

/refactor

Safe, verified refactoring with regression testing at each step

/deps

Dependency audit, update, vulnerability scanning, and license compliance

/perf

Performance profiling, benchmarking, regression detection, and optimization

/system-tuning

Restore system responsiveness via safe, ordered process cleanup and agent-swarm hygiene

/scaffold

Project scaffolding, component generation, and boilerplate setup

/scenario

Author and manage holdout scenarios for behavioral validation

/skill-auditor

Two-pass audit of an existing SKILL.md against the unified template (15 checks)

/skill-builder

Scaffold or absorb new SKILL.md files against the unified template

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

/ship-loop

Bot-paired internal-PR fast-lane cycle

/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 promotes learnings and patterns into .agents/ so future /research starts with better context instead of zero.

Inspect, lint, and triage the .agents/ write surface contract via ao agents inspect | lint | doctor (doctor rolls up inspect + lint + orphan/stray-dir report; --strict fails on orphans).

Runtime Modes

AgentOps has four runtime modes. Do not assume hook automation exists everywhere.

Mode

When it applies

Start path

Closeout path

Guarantees

gc

Gas City (gc) binary available and city.toml present

gc controller manages sessions; ao rpi auto-selects gc executor

gc event bus captures phase/gate/failure/metric events

Default when gc is available. Phase execution via gc sessions, events via gc event bus, agent health via gc health patrol

hook-capable

Claude/OpenCode with lifecycle hooks installed (no gc)

Runtime hook or ao inject / ao lookup

Runtime hook or ao forge transcript + ao flywheel close-loop

Automatic startup/context injection and session-end maintenance when hooks are installed

codex-native-hooks

Codex CLI v0.115.0+ with native hook support (March 2026)

Runtime hooks (same as hook-capable)

Runtime hooks (same as hook-capable)

Native lifecycle hooks — same guarantees as hook-capable mode

codex-hookless-fallback

Codex Desktop / Codex CLI pre-v0.115.0 without hook surfaces

ao codex start

ao codex stop

Explicit startup context, citation tracking, transcript fallback, and close-loop metrics without hooks

manual

No hooks and no Codex-native runtime detection

ao inject / ao lookup

ao forge transcript + ao flywheel close-loop

Works everywhere, but lifecycle actions are operator-driven

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

Startup context loading. Hook-capable runtimes run session-start.sh at session start (manual mode auto-loads MEMORY.md and points to ao search/ao lookup; lean mode injects prior learnings on a reduced token budget). Codex v0.115.0+ fires hooks automatically; pre-v0.115.0 runs ao codex start / ao codex stop explicitly. Either way the agent gets the RPI workflow, prior context, and a citation path.

Workflow reference during planning. When a user asks how to approach a feature, the agent uses this skill's RPI section to recommend Research → Plan → Implement → Validate — /research for exploration, /plan for decomposition, /pre-mortem for failure simulation — instead of an ad-hoc approach.

Troubleshooting

Problem

Cause

Solution

Skill not auto-loaded

Hook runtime unavailable or startup path not run

Hook-capable runtimes: verify hooks/session-start.sh exists and is enabled. Codex: run ao codex start explicitly

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