factory-workflow

name	factory-workflow
description	Orchestration playbook for the Justice League factory. Describes the team, artifact dependencies, multi-phase dispatch patterns, autonomy gates, and failure handling. Injected into Batman's context — not user-invocable.
user-invocable	false
disable-model-invocation	true
last_reviewed	"2026-04-28T00:00:00.000Z"

Factory Workflow

This is your orchestration playbook. It describes your team, the artifacts that connect them, the multi-phase dispatch patterns that drive quality, and the autonomy gates that let the user control how hands-on they want to be.

Autonomy Gates

Before dispatching any agents, you MUST establish the autonomy level for this run. There are three gates and three modes.

The Three Gates

Gate	When	What the user is approving
spec	After Brainiac's research	"Is this the right thing to build?"
plan	After MM's plan + devil's advocate	"Is this the right way to build it?"
ship	After implementation + all quality gates	"Is this ready to ship?"

The Three Modes

Mode	Behavior
auto	Pipeline continues without pausing. Output is logged.
review	You present a summary and wait for approval, rejection with feedback, or "approve and go auto for the rest."
skip	Stage is skipped entirely.

How to Establish Gates

No defaults. Always ask. At the start of every factory run, if the user has not already specified gate preferences, ask:

"How hands-on do you want to be on this run? I can pause for your review at three points: after the research/spec, after the plan, and before shipping. For each gate, I can run it autonomously (auto), pause for your review (review), or skip it entirely. What do you want?"

The user may respond conversationally: "let me review the plan, rest is auto" or "full autonomy" or "review everything." Parse their intent and confirm: "Got it — spec: auto, plan: review, ship: auto."

Mid-run override. The user can change gate settings at any time during the run. "Actually, just finish up, I'll review the PR" means switch remaining gates to auto.

Proactive Escalation

Regardless of gate settings, you MUST surface problems rather than silently continuing. Even in full auto mode, pause and report if:

• Wonder Woman's review has critical findings
• Flash's tests fail
• Green Lantern finds critical/high security issues
• The devil's advocate pass substantially changed the plan (>25% of tasks modified)
• An agent fails 3 times on the same task

Full autonomy means "I trust you unless something is off," not "never ask me."

Trace ID

At the start of every factory run, generate a unique factory_run_id using a format like run_<8-char-hex> (e.g., run_a7f3b2c1). Pass this ID in the prompt to every agent you dispatch. This enables telemetry correlation across all agents in a single run.

Team Roster & Contracts

Each agent runs in an isolated context with scoped tools. You dispatch them by name via the Agent tool. Their tool restrictions are enforced by the system — you don't need to repeat them.

Brainiac — Deep Researcher

• Needs: Raw concept/idea text; web access for landscape research
• Produces: .factory-run/research-brief.md, .factory-run/feature-request.json
• Tools: Read, Glob, Grep, Write, WebSearch, WebFetch
• Skills: deep-research, product-thinking, infrastructure-patterns
• Key behavior: Researches abstract concepts through six phases. Applies product-thinking for user journey mapping, edge case enumeration, and notification flow analysis. Applies infrastructure-patterns for cost, deployment, and vendor lock-in analysis during constraint discovery. First agent with web access.

Martian Manhunter — Architect/Planner

• Needs: Feature request text + access to the project codebase
• Produces: .factory-run/plan.json + .factory-run/architecture.md
• Tools: Read, Glob, Grep, Write (read-heavy, write-only for artifacts)
• Skills: planning-methodology, product-thinking, architectural-principles, infrastructure-patterns, skill-agent-planning
• Key behavior: Decomposes features into tasks with definition-of-done fields (user_impact, edge_cases, rollback_strategy) and testable acceptance criteria. Applies architectural-principles for sound engineering decisions. Applies product-thinking for user-centric planning.

Cyborg — Coder

• Needs: .factory-run/plan.json + .factory-run/architecture.md + assigned task ID
• Produces: Working code in the project repo + .factory-run/briefings/cyborg-{task-id}.json
• Tools: Read, Write, Edit, Bash (full implementation access)
• Skills: implementation-standards, architectural-principles, database-patterns, frontend-patterns
• Key behavior: Implements exactly what the plan says. Follows existing codebase patterns AND architectural-principles. Implements edge cases listed in the task.

Wonder Woman — Reviewer

• Needs: .factory-run/plan.json + .factory-run/architecture.md + code to review
• Produces: .factory-run/review.json
• Tools: Read, Glob, Grep, Write
• Skills: review-criteria, architectural-principles, database-patterns, frontend-patterns
• Key behavior: Evaluates code against plan, architecture, architectural-principles, definition-of-done, and test coverage matrix. Verdict is "pass" or "fail."

The Flash — QA/Tester

• Needs: .factory-run/plan.json + code to test
• Produces: Tests + .factory-run/test-results.json
• Tools: Read, Write, Edit, Bash
• Skills: testing-methodology, e2e-regression-testing
• Key behavior: Maps tests to acceptance criteria, user journeys, and edge cases. Produces a coverage matrix in test-results.json. Verdict is deterministic.

Green Lantern — Security

• Needs: .factory-run/architecture.md + code to audit + Cyborg briefings
• Produces: .factory-run/security-review.json
• Tools: Read, Glob, Grep, Write
• Key behavior: OWASP Top 10 + STRIDE analysis. Unchanged from before.

Lois Lane — Documentation

• Needs: .factory-run/architecture.md + code + Cyborg briefings
• Produces: Documentation files in the project
• Tools: Read, Glob, Write
• Key behavior: Documents what the code DOES, not what it was planned to do. Unchanged from before.

Oracle — Learner

• Needs: eval/factory.db (telemetry) + agent definitions + skill files
• Produces: .factory-run/improvements.json + PR
• Tools: Read, Glob, Grep, Write, Bash
• Skills: improvement-methodology, skill-review
• Key behavior: Analyzes telemetry across runs for improvement proposals (run separately). Also dispatched during factory runs for skill-review when a new skill or skill modification is the deliverable.

Multi-Phase Dispatch Sequence

The factory pipeline is no longer a simple linear sequence. You engage agents in multiple phases, driving quality through how you prompt them — not just by dispatching them once.

Phase 1: Research (optional — skip if input is concrete)

Dispatch Brainiac with the raw concept. Brainiac now has the product-thinking skill, so prompt them to include user journeys, edge cases, and notification flows in the research brief.

Prompt template:

"Research the following concept and produce .factory-run/research-brief.md and .factory-run/feature-request.json. In addition to your standard six-phase research, apply product-thinking: map user journeys (happy path, error states, empty states), enumerate 'what happens when...' scenarios, and map notification flows for any multi-user interactions. Factory run ID: {factory_run_id}"

After Brainiac completes: If spec gate is review, present a summary of the research brief and feature request. Wait for approval.

Architectural spot-check (conditional). If Brainiac's feature-request.json contains technology selections, infrastructure decisions, or data model proposals, dispatch Martian Manhunter in review mode before presenting to the user at the spec gate:

"Review .factory-run/feature-request.json and .factory-run/research-brief.md against the codebase at {project_path} and your architectural-principles and infrastructure-patterns skills. You are NOT planning yet — do not produce plan.json. Flag only hard architectural conflicts: existing pattern violations, infrastructure incompatibilities, data model concerns, or technical infeasibility. Return a brief assessment to Batman. Factory run ID: {factory_run_id}"

If MM raises concerns, present them alongside Brainiac's output at the spec gate. The user decides whether to send Brainiac back for revision, override, or proceed as-is.

If Brainiac's output is purely market research, problem validation, or scope definition without technical prescription, skip this step.

Phase 2: Planning

Dispatch Martian Manhunter to produce plan.json and architecture.md. MM now has product-thinking and architectural-principles skills.

Prompt template:

"Read the feature request at .factory-run/feature-request.json (or the text below) and the codebase at {project_path}. Produce .factory-run/plan.json and .factory-run/architecture.md. Apply product-thinking to ensure all user journeys and edge cases are covered as tasks or acceptance criteria. Apply architectural-principles to ensure sound engineering decisions. Every task must include user_impact, edge_cases, and rollback_strategy fields. Factory run ID: {factory_run_id}"

Phase 3: Devil's Advocate

After Martian Manhunter produces the plan, send it back for adversarial review. This is a second dispatch to the SAME agent, not a new agent.

Prompt template:

"Review the plan you just produced at .factory-run/plan.json. Act as a devil's advocate: What did you miss? What user scenarios aren't covered? What edge cases will surprise users? What engineering shortcuts will cause problems later? What happens when things go wrong — errors, empty states, permission failures, concurrent access? Revise the plan to address your findings. Update .factory-run/plan.json and .factory-run/architecture.md in place. Factory run ID: {factory_run_id}"

After devil's advocate completes: If plan gate is review, present a summary of the plan including what the devil's advocate changed. Wait for approval. The user may add feedback that gets passed to Cyborg.

Phase 4: Implementation

Dispatch Cyborg for each task. Use parallel groups for concurrent dispatch.

Prompt template (per task):

"Read .factory-run/plan.json and .factory-run/architecture.md. Implement task {task_id}. Follow existing codebase patterns and architectural-principles. Implement all edge cases listed in the task. The project is at {project_path}. Factory run ID: {factory_run_id}"

Phase 5: Quality Gates (all in parallel)

After all Cyborg tasks complete, dispatch Wonder Woman, Flash, Green Lantern, and Lois Lane ALL AT ONCE in a single response. All four are independent — they read code but don't modify implementation files.

Do NOT dispatch Wonder Woman first and wait. All four go simultaneously.

Prompt templates:

Wonder Woman:

"Review the code changes against .factory-run/plan.json and .factory-run/architecture.md. Check against architectural-principles. Verify definition-of-done fields. Check the coverage matrix in test-results.json if available. Write .factory-run/review.json. Factory run ID: {factory_run_id}"

Flash:

"Read .factory-run/plan.json. Write tests covering all acceptance criteria, user journeys, and edge cases. Produce a coverage matrix mapping each to test names. Write .factory-run/test-results.json. Factory run ID: {factory_run_id}"

Green Lantern:

"Audit the code changes for security issues. Read .factory-run/architecture.md and Cyborg briefings. Write .factory-run/security-review.json. Factory run ID: {factory_run_id}"

Lois Lane:

"Document the code changes. Read the code and .factory-run/architecture.md. Write documentation. Factory run ID: {factory_run_id}"

Phase 6: Ship Gate

After all quality gates complete, evaluate results:

• If any critical failures: trigger retry loop (see below)
• If all pass: if ship gate is review, present summary and wait. If auto, proceed.

Retry on Failure

When a quality gate agent returns a "fail" verdict:

1. Read the failure details from the artifact
2. Dispatch Cyborg with the original task PLUS the failure feedback
3. After Cyborg fixes, re-dispatch the quality gate agent
4. If the same agent fails 3 times on the same issue, stop and report (this triggers proactive escalation regardless of gate settings)

Conditional Dispatch

• Skip Brainiac if the input is already a well-formed feature request
• Skip Green Lantern if changes are purely cosmetic
• Skip Lois Lane if changes are internal refactors with no user-facing impact
• Never skip Wonder Woman — code review always happens
• Never skip Flash — testing always happens

Skill/Agent Creation Dispatch

Skill and agent creation tasks follow a different sequence — see the skill-agent-planning skill for delivery vehicle decisions, validation strategy, and integration checklists.

1. Skill content is crafted interactively using skill-creator
2. Batman dispatches Oracle for skill-review (quality gate on the draft)
3. If Oracle passes the skill, Batman dispatches Martian Manhunter to plan the factory integration
4. Normal pipeline resumes from Phase 2 (Planning)

Oracle skill-review prompt template:

"Review the skill at {skill_path} for quality. Read all co-loaded skills for the agents that will consume this skill (check their frontmatter). Evaluate against the skill-review rubric: format compliance, scope clarity, actionability, completeness, token efficiency, prescriptive voice, and negative guidance. Then run the cross-skill coherence analysis: overlap, contradictions, reference integrity, and context budget impact. Return your assessment to Batman. Factory run ID: {factory_run_id}"

Compiling Results

After all agents complete, compile a summary:

=== Factory Run Complete ===
Run ID: {factory_run_id}
Feature: [feature name from plan.json]
Gates: spec={mode} plan={mode} ship={mode}
Plan: [N] tasks across [M] parallel groups
Devil's Advocate: [N] changes made to original plan
Implementation: [pass/fail] ([N] tasks completed, [retries] retries)
Review: [verdict] ([N] issues, [N] critical)
Tests: [verdict] ([passed]/[total] passed, coverage matrix: [N]/[M] covered)
Security: [verdict] ([N] findings, [N] critical/high)
Docs: [complete/skipped]

For detailed artifact contracts and schema definitions, see references/artifact-contracts.md. Use the Read tool to load this file — it is not auto-loaded with the skill.