ArcheFlow -- Multi-Agent Orchestration for Claude Code

Structured quality through archetypal collaboration. ArcheFlow coordinates multiple Claude Code agents through PDCA cycles, where each agent embodies a Jungian archetype with defined strengths and known failure modes.

Zero dependencies. No build step. Install and go.

What It Does

Large coding tasks benefit from multiple perspectives, but "just spawn more agents" creates chaos. Agents duplicate work, miss each other's output, argue in circles, or go rogue. The problem is not intelligence -- it is coordination.

ArcheFlow solves this by giving each agent an archetype: a behavioral protocol that defines what the agent cares about, what context it receives, and how its output feeds into the next phase. Seven archetypes collaborate through Plan-Do-Check-Act cycles, where each iteration builds on structured feedback from the last. No unreviewed code reaches your main branch.

The key insight: archetypes are not just system prompts. Each one has a virtue (its unique contribution) and a shadow (the dysfunction it falls into when pushed too far). ArcheFlow monitors for shadow activation and course-corrects automatically -- replacing an agent that blocks everything, reining in one that researches forever, or escalating when a maker goes off-script.

Quick Start

1. Install

From the marketplace (recommended):

# Add the marketplace (one time)
/plugin marketplace add https://git.xorwell.de/c/claude-archeflow-plugin

# Install the plugin
/plugin install archeflow@claude-archeflow-plugin

From Git URL directly:

/plugin marketplace add https://git.xorwell.de/c/claude-archeflow-plugin.git
/plugin install archeflow --scope user

Local development:

claude --plugin-dir ./archeflow

After installing, run /reload-plugins or restart Claude Code. ArcheFlow activates automatically on session start.

Verify installation

/plugin         # Opens plugin manager — check "Installed" tab
/af-status      # Should show "no active run"

Scopes

• --scope user — available in all your projects (recommended)
• --scope project — only in the current project
• --scope local — only in the current directory

2. Run your first orchestration

Just describe a task. ArcheFlow activates automatically for multi-file changes:

> Add input validation to all API endpoints

Or invoke it explicitly:

> archeflow:run "Add JWT authentication" --workflow standard

3. What happens

ArcheFlow selects a workflow (fast, standard, or thorough) and runs a PDCA cycle:

Plan  --> Explorer researches codebase context, Creator designs a proposal
Do    --> Maker implements in an isolated git worktree
Check --> Reviewers assess in parallel (Guardian, Skeptic, Sage, Trickster)
Act   --> All approved? Merge. Issues found? Cycle back with structured feedback.

Each cycle catches what the last one missed.

Progress is visible in real time:

--- ArcheFlow: Add JWT authentication ---------
Workflow: standard (2 cycles max)

🔍 [Plan] Explorer researching...        done (35s)
🏗️ [Plan] Creator designing proposal...   done (25s, confidence: 0.8)
⚒️ [Do]   Maker implementing...           done (90s, 4 files, 8 tests)
🛡️ [Check] Guardian reviewing...          APPROVED
🤔 [Check] Skeptic challenging...          APPROVED (1 INFO)
📚 [Check] Sage reviewing...              APPROVED
   [Act]  All approved -- merging...       merged to main

--- Complete: 3m 10s, 1 cycle -----------------

The Seven Archetypes

Archetype	Phase	Virtue	Shadow	Role
🔍 Explorer	Plan	Contextual Clarity	Rabbit Hole	Researches codebase, maps dependencies, synthesizes findings
🏗️ Creator	Plan	Decisive Framing	Over-Architect	Designs solution proposals with architecture decisions and test strategy
⚒️ Maker	Do	Execution Discipline	Rogue	Implements code in an isolated git worktree, commits per phase
🛡️ Guardian	Check	Threat Intuition	Paranoid	Reviews for security vulnerabilities, reliability risks, breaking changes
🤔 Skeptic	Check	Assumption Surfacing	Paralytic	Challenges assumptions, identifies untested scenarios, proposes alternatives
🃏 Trickster	Check	Adversarial Creativity	False Alarm	Adversarial testing, boundary attacks, edge case exploitation
📚 Sage	Check	Maintainability Judgment	Bureaucrat	Holistic quality review -- code quality, test coverage, engineering judgment

Shadow detection is quantitative, not vibes. Explorer output exceeding 2000 words without a recommendation triggers Rabbit Hole. Guardian blocking three consecutive items triggers Paranoid. First detection: correction prompt. Second: replace agent. Third: escalate to user.

Skills Reference

ArcheFlow ships with 24 skills organized by function.

Core Orchestration

Skill	Description
archeflow:run	Automated PDCA execution loop -- single-command orchestration with --start-from, --dry-run, and cycle-back
archeflow:orchestration	Step-by-step PDCA execution guide for manual orchestration
archeflow:plan-phase	Explorer and Creator output formats and protocols
archeflow:do-phase	Maker implementation rules and worktree commit strategy
archeflow:check-phase	Shared reviewer protocols and output format
archeflow:act-phase	Post-Check decision logic: collect findings, route fixes, exit or cycle

Quality and Safety

Skill	Description
archeflow:shadow-detection	Quantitative dysfunction detection and automatic correction
archeflow:attention-filters	Context optimization per archetype -- each agent gets only what it needs
archeflow:convergence	Detects convergence, stalling, and oscillation in multi-cycle runs
archeflow:artifact-routing	Inter-phase artifact protocol -- naming, storage, routing, archiving

Process Intelligence

Skill	Description
archeflow:process-log	Event-sourced JSONL logging with DAG parent relationships
archeflow:memory	Cross-run memory that learns recurring findings and injects lessons
archeflow:effectiveness	Archetype scoring on signal-to-noise, fix rate, cost efficiency
archeflow:progress	Live progress file watchable from a second terminal

Integration

Skill	Description
archeflow:colette-bridge	Bridges ArcheFlow with the Colette writing platform
archeflow:git-integration	Git-per-phase commits, branch-per-run, rollback to any phase boundary
archeflow:multi-project	Cross-repo orchestration with dependency DAG and shared budget

Configuration

Skill	Description
archeflow:custom-archetypes	Create domain-specific roles (database reviewer, compliance auditor, etc.)
archeflow:workflow-design	Design custom workflows with per-phase archetype assignment and exit conditions
archeflow:domains	Domain adapters for writing, research, and other non-code workflows
archeflow:cost-tracking	Budget enforcement, per-agent cost aggregation, model tier recommendations
archeflow:templates	Template gallery for sharing workflows, teams, and setup bundles
archeflow:autonomous-mode	Unattended overnight sessions with progress logging and safe stopping

Meta

Skill	Description
archeflow:using-archeflow	Session-start skill -- activation criteria, workflow selection, quick reference

Library Scripts

Eight shell scripts in lib/ power the process infrastructure.

Script	Purpose	Usage
archeflow-event.sh	Append structured JSONL events to a run log	archeflow-event.sh <run_id> <type> <phase> <agent> '<json>'
archeflow-dag.sh	Render ASCII DAG from JSONL events	archeflow-dag.sh events.jsonl --color
archeflow-report.sh	Generate Markdown process report	archeflow-report.sh events.jsonl --output report.md --dag
archeflow-progress.sh	Regenerate live progress file from events	archeflow-progress.sh <run_id>
archeflow-score.sh	Score archetype effectiveness from completed runs	archeflow-score.sh extract events.jsonl
archeflow-memory.sh	Cross-run memory: add, list, decay, inject lessons	archeflow-memory.sh add "Always check for null"
archeflow-git.sh	Per-phase commits, branch creation, merge, rollback	archeflow-git.sh commit <run_id> <phase>
archeflow-init.sh	Template gallery: init, save, clone, list	archeflow-init.sh init writing-short-story

Workflows

Built-in Workflows

Workflow	Cycles	Archetypes	Best For
fast	1	Creator, Maker, Guardian	Bug fixes, small changes
standard	2	Explorer + Creator, Maker, Guardian + Skeptic + Sage	Features, refactors
thorough	3	Explorer + Creator, Maker, All 4 reviewers	Security-critical, public APIs

ArcheFlow picks the workflow automatically based on task complexity, or you can specify:

> Implement input validation for the API (use thorough workflow)

Workflows adapt at runtime. If Guardian finds 2+ CRITICALs in a fast workflow, it escalates to standard. If reviewers find nothing in standard, it fast-paths past the remaining cycle.

Custom Workflows

Define your own workflows in .archeflow/workflows/:

# .archeflow/workflows/api-design.yaml
name: api-design
pdca:
  plan: { archetypes: [explorer, creator] }
  do: { archetypes: [maker] }
  check: { archetypes: [guardian, skeptic, trickster] }
  act: { exit_when: all_approved, max_cycles: 2 }

Example: Short Fiction Workflow

ArcheFlow is not limited to code. The included kurzgeschichte workflow orchestrates short story development with custom archetypes (story-explorer, story-sage), Colette voice profile integration, and scene-by-scene commits:

# examples/workflows/kurzgeschichte.yaml
name: kurzgeschichte
team: story-development
phases:
  plan:
    archetypes: [story-explorer, creator]
  do:
    archetypes: [maker]
  check:
    archetypes: [guardian, story-sage]
  act:
    exit_when: all_approved
    max_cycles: 2

Domain Adapters

ArcheFlow defaults to code-oriented terminology, but domain adapters remap concepts for other workflows:

Domain	What Changes
code	Default. Diffs, tests, security review, merge to main.
writing	Prose quality, voice consistency, dialect authenticity. Auto-activates when colette.yaml is detected.
research	Source quality, argument coherence, citation accuracy.

Custom domains can be defined in .archeflow/domains/.

Examples

The examples/ directory contains complete walkthroughs:

• feature-implementation.md -- End-to-end feature build with standard workflow
• security-review.md -- Thorough review of security-sensitive code
• custom-workflow.yaml -- Template for defining your own workflow
• custom-archetypes/ -- Story-explorer and story-sage for fiction writing
• teams/ -- Team preset for story development
• workflows/kurzgeschichte.yaml -- Short fiction workflow with Colette integration

Configuration

Project Configuration

Create .archeflow/config.yaml in your project root:

workflow: standard          # Default workflow
budget: 50000               # Max tokens per run
git:
  enabled: true             # Per-phase commits
  merge_strategy: squash    # squash or no-ff

Custom Archetypes

Add domain-specific roles in .archeflow/archetypes/:

# .archeflow/archetypes/db-specialist.md
---
name: db-specialist
description: Reviews database schemas and migration safety
model: sonnet
---

You are the **Database Specialist**.
Your lens: "Will this scale? Will this corrupt data?"

Team Presets

Define reusable teams in .archeflow/teams/:

# .archeflow/teams/backend-review.yaml
name: backend-review
archetypes: [explorer, creator, maker, guardian, db-specialist]

Environment Variables

• ARCHEFLOW_BUDGET -- Override default token budget
• ARCHEFLOW_WORKFLOW -- Override default workflow selection

Architecture

archeflow/
├── .claude-plugin/plugin.json   # Plugin manifest (v0.5.0)
├── agents/                      # 7 archetype personas (behavioral protocols)
│   ├── explorer.md              #   Plan: research and context mapping
│   ├── creator.md               #   Plan: solution design and proposals
│   ├── maker.md                 #   Do: implementation in isolated worktree
│   ├── guardian.md              #   Check: security and reliability review
│   ├── skeptic.md               #   Check: assumption challenging
│   ├── trickster.md             #   Check: adversarial testing
│   └── sage.md                  #   Check: holistic quality review
├── skills/                      # 24 behavioral skills
│   ├── run/                     #   Automated PDCA loop
│   ├── orchestration/           #   Manual PDCA execution guide
│   ├── plan-phase/              #   Plan protocols
│   ├── do-phase/                #   Do protocols
│   ├── check-phase/             #   Check protocols
│   ├── act-phase/               #   Act phase decision logic
│   ├── shadow-detection/        #   Dysfunction detection
│   ├── attention-filters/       #   Context optimization
│   ├── convergence/             #   Cycle convergence detection
│   ├── artifact-routing/        #   Inter-phase artifact protocol
│   ├── process-log/             #   Event-sourced JSONL logging
│   ├── memory/                  #   Cross-run learning
│   ├── effectiveness/           #   Archetype scoring
│   ├── progress/                #   Live progress file
│   ├── colette-bridge/          #   Colette writing platform bridge
│   ├── git-integration/         #   Per-phase git commits
│   ├── multi-project/           #   Cross-repo orchestration
│   ├── custom-archetypes/       #   Domain-specific roles
│   ├── workflow-design/         #   Custom workflow design
│   ├── domains/                 #   Domain adapters
│   ├── cost-tracking/           #   Budget and cost management
│   ├── templates/               #   Template gallery
│   ├── autonomous-mode/         #   Unattended sessions
│   └── using-archeflow/         #   Session-start activation
├── lib/                         # 8 shell scripts (process infrastructure)
├── hooks/                       # Auto-activation (SessionStart)
├── examples/                    # Walkthroughs, templates, custom archetypes
└── docs/                        # Roadmap, changelog

The flow: skills define behavioral rules (what agents should do), agents define personas (how they think), lib scripts handle tooling (event logging, git, reporting), and hooks wire it all together at session start. Events are emitted at every phase transition, forming a DAG that can be rendered, reported, or scored after the run.

Philosophy

1. Strength has a shadow. Every capability becomes destructive when unchecked. The Explorer who never stops researching. The Guardian who blocks everything. The Maker who ships without review. ArcheFlow names these shadows and corrects them automatically.

2. Quality is a spiral, not a gate. A single review pass misses things. PDCA cycles spiral upward -- each iteration catches what the previous one missed, until the reviewers have nothing left to find.

3. Autonomy needs structure. Agents given clear roles, typed communication, and quality gates produce exceptional work -- even overnight, even unattended.

Version History

See CHANGELOG.md for detailed release notes.

License

MIT