ea-harness

ArchHarness

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Enterprise architecture design and validation skill pack for Claude Code, OpenCode, Codex, GitHub Copilot, and Cursor.

ArchHarness turns your AI coding assistant into a team of architecture specialists — a requirements analyst, a senior architect, a paranoid security auditor, a committee reviewer, and a technical writer — each invocable on demand with a single command.

Rendered with D2. One architecture model compiles to both renderers: draw.io for the editable source, D2 for the crisp presentation output. The images on this page are D2 renders.

Not yet another README-only repo. archharness ships a real CLI (python -m archharness), a multi-project workspace layout, and platform skills that load enterprise values from a single config file — then proves the result: every artifact is hash-verified and the gate is deterministic.

Example gallery

Six worked examples, one per platform standard — each end-to-end and runnable: requirements, a req/v2 inventory, an architecture blueprint, a rendered diagram, and a standards check. Tiles show the D2 render; click one to open the example, which also carries the editable draw.io source.

Eleven examples in total: 01–06 cover private cloud, AWS, and Azure, 09–11 add Google Cloud, Microsoft SaaS, and Alibaba Cloud, and 07–08 are scaffolds awaiting input. Each is self-contained and version-controlled; see examples/README.md for the full matrix and how to run one.

Two renderers, one model

Every example ships the same model in both formats, and each renderer has a job:

Renderer	Artifacts	Why
draw.io	diagram-v<N>.drawio	Editable source only. Open it in the draw.io desktop app to hand-tweak layout or annotations. It no longer produces the image by default; --png-engine drawio opts back into a draw.io PNG render.
D2	diagram-v<N>.d2 + PNG/SVG	The image renderer (default). One command (d2 --layout elk diagram.d2 out.png), crisper and more consistent — so the gallery above and the hero use D2. When the d2 CLI is absent the tool falls back to matplotlib. A very large diagram can exhaust d2's raster backend at full size; pass --d2-scale 0.2 (or render the .d2 yourself with d2 --scale) to bring it back.

The same model (example 09) through both renderers:

draw.io — editable source

D2 — presentation render

What it does

Agent / Skill	Claude Code	OpenCode	Role
arch-workflow	/arch-workflow	@arch-workflow	Pipeline gatekeeper — enforces stage order; BLOCK stops pipeline
arch-requirements	/arch-requirements	@arch-requirements	Structured interview → REQ.md + req.yaml
arch-req-from-diagram	/arch-req-from-diagram	@arch-req-from-diagram	draw.io / PNG → partial req.yaml
arch-req-from-doc	/arch-req-from-doc	@arch-req-from-doc	PDF / DOCX / MD → partial req.yaml
arch-req-from-api	/arch-req-from-api	@arch-req-from-api	CMDB / ServiceNow / CSV → partial req.yaml
arch-req-merge	/arch-req-merge	@arch-req-merge	Merge partials, detect conflicts, gap report
arch-design	/arch-design	@arch-design	Requirements → architecture YAML + draw.io guidance
arch-diagram	/arch-diagram	@arch-diagram	Architecture YAML → draw.io XML + PNG
arch-validate	/arch-validate	@arch-validate	Diagram image → scored JSON report (6 dimensions)
arch-enforce	/arch-enforce	@arch-enforce	CI enforcement gate — PASS / WARN / BLOCK with exit code
arch-security	/arch-security	@arch-security	Auth / credentials / network boundary deep-dive
arch-review	/arch-review	@arch-review	Committee gate: APPROVED / CONDITIONS / REJECTED
arch-optimize	/arch-optimize	@arch-optimize	Prioritized fix backlog (P0/P1/P2/P3)
arch-report	/arch-report	@arch-report	Confluence page / executive summary / risk brief

Workflow

Requirements → arch-design → draw in draw.io → arch-validate
                                                      │
                                              arch-enforce gate
                                           PASS / WARN / BLOCK
                                                      │  if PASS/WARN
                                                      │
                                          arch-security  arch-review
                                                      │
                                               arch-optimize
                                                      │
                                               arch-report

The pipeline is mandatory and gated by artifacts. The order and required input/output files are defined in standards/workflow.yaml. The arch-workflow gatekeeper checks that every required artifact of the next stage exists (and that the enforce gate recorded PASS or WARN) before the stage starts. A BLOCK decision stops the pipeline until findings are fixed and validation is re-run. Never skip a stage or fabricate predecessor outputs; invoke @arch-workflow status / @arch-workflow can <stage> when in doubt.

Recorded artifacts are hash-verified, not just named: a manifest that no longer matches the file on disk fails the gate closed, so a diagram cannot be edited underneath a recorded decision. python -m archharness workflow verify reports those findings directly and exits 1 when any artifact is stale or altered.

Roadmap

The repository-level priorities for product capability, engineering quality, and reference content are maintained in docs/ROADMAP.md. The detailed draw.io layout and connection-routing plan is maintained in DIAGRAM_GENERATION_ANALYSIS.md. Released and unreleased changes, plus the interfaces consumers pin, are listed in CHANGELOG.md. A step-by-step walkthrough from a fresh checkout to a gated diagram, with the diagnosis for each common failure, is in docs/first-run.md; the recipe for running the gate on every pull request, with the evidence archived, is in docs/ci-pipeline.md; and the engine↔platform boundary with AXISRobo-PAMP — including the privacy-preserving metrics/v1 observability contract — is in docs/pamp-integration.md. Re-validating an example against its current (D2) image, with the measured binding status, is in docs/revalidation-guide.md.

Setup

1. Clone

git clone https://github.com/axisrobo/ea-harness.git
cd ea-harness

2. Configure the organisation profile

Edit config.yaml at the repository root to match your organisation's infrastructure (DC names, platform names, classification prefix). Skills and LLM rules load these values at runtime.

company:
  name: "Acme Corp"

datacenters:
  - id: "dc-primary"
    aliases: ["Primary DC", "Tokyo DC"]
    location: { city: "Tokyo", country: "JP" }
    zones: ["DMZ", "App Zone", "DB Zone"]

platforms:
  api_gateway: "Kong API Gateway"   # or WSO2, AWS API GW, Azure APIM…
  message_bus: "RabbitMQ"           # or Kafka, Azure Service Bus…
  k8s_platform: "Rancher"
  integration_platforms:
    - "Kong API Gateway"
    - "RabbitMQ"
    - "SFTP/MFT"

If you manage more than one architecture project, put these company values in config.yaml once and create isolated projects (next step). Per-project inputs and outputs live under projects/<id>/.

3. Create a workspace and a project

One workspace can hold many architecture projects. Each project has its own input/, working/, and output/ trees so files never bleed between projects.

# POSIX / macOS / Linux
python -m archharness init-workspace .
python -m archharness init-project payments --name "Payments Platform" --default
python -m archharness list-projects

# Windows PowerShell
python -m archharness init-workspace .
python -m archharness init-project payments --name "Payments Platform" --default
python -m archharness list-projects

This creates:

projects/payments/
├─ project.yaml               # id, name, platform, data classification
├─ input/                     # documents, diagrams, api exports, requirements
├─ working/                   # intermediate files
└─ output/                    # requirements, designs, diagrams, validation, reports

project.yaml and all generated files are git-ignored — only project.yaml and README.md are tracked when you choose to commit them.

When you work inside a project directory, tools and skills auto-detect the active project (--project also works from anywhere in the workspace).

4. Install Python dependencies

# POSIX / macOS / Linux
./install.sh

# Windows PowerShell
.\install.ps1

Or manually:

pip install -e ".[all]"
python -m archharness init-workspace .   # only if not created above
python -m archharness doctor             # verify the install

The installer registers skills with your AI tool, creates a workspace when one is missing, and runs doctor. Add ARCHHARNESS_HOME=/path/to/ea-harness to your environment if you ever run tools from a different working directory.

5. Open in your AI coding tool

Claude Code

claude .

Skills under .claude/skills/ register as /arch-* slash commands.

OpenCode

opencode .

Agents under .opencode/agents/ register as @arch-* agents.

Codex / GitHub Copilot / Cursor Point the tool at this repository root. AGENTS.md is read by all three; Codex discovers skills under .agents/skills/; GitHub Copilot discovers the @arch-* custom agents under .github/agents/; Cursor builds also read .claude/skills/.

Tip: working directory should be the repository root (or a project directory) so skills, tools, and config.yaml are found automatically.

Where each tool discovers ArchHarness

Tool	Project rules	Skills / agents	Invocation
Claude Code	CLAUDE.md	.claude/skills/	/arch-validate, /arch-design, …
OpenCode	AGENTS.md	.opencode/agents/	@arch-validate, @arch-design, …
Codex	AGENTS.md	.agents/skills/	skill selector on .agents/skills/
GitHub Copilot	AGENTS.md	.github/agents/	@arch-validate, @arch-design, …
Cursor	AGENTS.md	.claude/skills/ (supported builds)	/skills

.agents/skills/ is a generated mirror of .claude/skills/. Update it with python scripts/sync_agents_skills.py after editing any skill; CI enforces the mirror stays in sync (scripts/check_repo.py validates the whole pack).

Can users install from the chat window?

Claude Code — yes, via the plugin marketplace. In the Claude Code chat window:

/plugin marketplace add axisrobo/ea-harness
/plugin install archharness@archharness-marketplace
/reload-plugins

Plugin skills are namespaced as /archharness:arch-validate, /archharness:arch-design, /archharness:arch-workflow, etc. (the plugin caches a copy of the skills). For shared resources (standards/, tools/, config.yaml) the skills resolve through the installed package or a checkout — so run pip install archharness[all] (or set ARCHHARNESS_HOME) once.

Every other tool: open this repository as the working directory (claude ., opencode ., codex, or point Copilot/Cursor at it). Skills, agents, and AGENTS.md are then discovered automatically and stay able to reach tools/, standards/, and config.yaml.

Installers (install.ps1 / install.sh) prepare a fresh clone: they install the Python package, initialise the workspace, and run doctor.

Command-line reference

Command	Purpose
python -m archharness --version	Show the installed version
python -m archharness root	Print the resource root (config.yaml + tools/)
python -m archharness doctor	Self-check installation, workspace, and project
python -m archharness init-workspace .	Create the workspace metadata
python -m archharness init-project <id>	Scaffold an isolated project
python -m archharness list-projects	List the projects in a workspace
python -m archharness diagram -i arch.yaml	Run the diagram generator (draw.io/PNG/D2/PlantUML)
python -m archharness diagram -i arch.yaml --routing-diagnostics routes.json	Also record per-edge routing strategy, lane, and fallback
python -m archharness diagram -i arch.yaml --d2 out.d2 --png out.png	Render the PNG — D2 by default, matplotlib fallback (--d2-scale shrinks very large diagrams)
python -m archharness diagram -i arch.yaml --png out.png --png-engine drawio	Opt into a draw.io PNG render (draw.io is otherwise edit-only)
python -m archharness arch-check -i blueprint.yaml [--json]	Deterministic rules on an architecture model (A-01…A-06)
python -m archharness trace-check -r req.yaml -b blueprint.yaml	Verify a req/v2 inventory traces to typed blueprint nodes
python -m archharness req --doc brief.md	Run the requirements readers + merger
python -m archharness req-validate req.yaml	Cross-field validation of a req/v2 document (rules V1–V7)
python -m archharness validate-yaml config.yaml	YAML syntax gate (CI fail-closed check)
python -m archharness workflow status	Show per-stage readiness
python -m archharness workflow can <stage>	Exit 0 only if that stage may start
python -m archharness workflow verify [--json]	Re-verify recorded artifact digests
python -m archharness manifest --file out/diagram-v2.png --id diagram.png --type diagram --schema diagram/png	Build an artifact/v1 provenance manifest for a file
python -m archharness enforce --validation validate_result.json	Apply the gate policy (PASS/WARN/BLOCK)
python -m archharness validate-check -v validate_result.json -r req.yaml -b blueprint.yaml	Prove each finding cites an element that exists
python -m archharness backlog -v validate_result.json -r req.yaml -b blueprint.yaml	Ordered remediation list (backlog/v1) grouped by element
python -m archharness metrics --project <id> [--json]	Aggregate governance metrics with no architecture payloads
python -m archharness schema-check --baseline <ref>	Classify a contract change as breaking / additive / cosmetic
python -m archharness migrate-status	req/v2 migration state across the examples
python -m archharness view "<question>"	Recommend a viewpoint for a question
python -m archharness sketch "Browser -> API -> DB" -o d.drawio	One-shot sketch without a YAML file
python -m archharness model diff old.yaml new.yaml	Semantic model diff
python -m archharness plugins	List discovered plugins and capabilities

arch-check, diagram, req, and validate-yaml forward their flags to the same Python tools under tools/, so both invocation styles are equivalent:

python tools/arch-diagram-gen/arch_diagram_gen.py -i arch.yaml
python -m archharness diagram -i arch.yaml

Run a tool from inside projects/<id>/ to target that project automatically; pass --project <id> to target one from anywhere.

Self-contained install (no checkout needed). pip install archharness[all] ships tools/, standards/, and the skill tree inside the package, so python -m archharness root returns a bundled resource root and the CLI tools work from any working directory:

pip install "archharness[all]"              # from PyPI, or pin the release:
pip install https://github.com/axisrobo/ea-harness/releases/download/v1.1.0/archharness-1.1.0-py3-none-any.whl
python -m archharness root        # → …/site-packages/archharness/data
python -m archharness doctor

Regenerate the bundled data before building a wheel with python scripts/assemble_data.py.

Usage examples

Design a new system

/arch-requirements

Claude conducts a structured interview and produces REQ.md + req.yaml in the active project's output/requirements/.

Generate a diagram

/arch-design

Produces an architecture YAML blueprint. Then, from inside the project directory:

python ../../tools/arch-diagram-gen/arch_diagram_gen.py -i arch.yaml
# → output/diagrams/arch.drawio

Or explicitly target a project from anywhere in the workspace:

python tools/arch-diagram-gen/arch_diagram_gen.py -i projects/payments/input/arch.yaml \
  --project payments

Check a model before review

Objective rules can be decided from the architecture YAML alone, so clear them before spending a review cycle:

python -m archharness arch-check -i output/designs/blueprint.yaml
# ERROR A-03: interaction api -> db has no protocol label
# ERROR A-04: interaction api -> db has no authentication label
#   ...
# 2 error(s), 0 warning(s)

python -m archharness arch-check -i output/designs/blueprint.yaml --json

Exit code 1 means at least one ERROR. Every finding carries a rule id (A-01…A-06) and the evidence it came from.

Validate a diagram

Attach your diagram PNG and run:

/arch-validate

Returns a scored JSON report with must_fix, should_fix, and consider findings.

Full pipeline (OpenCode)

@arch-requirements   # gather requirements
@arch-design         # design the architecture
@arch-validate       # validate the diagram
@arch-enforce        # CI enforcement gate decision
@arch-security       # deep security audit
@arch-review         # committee gate decision
@arch-optimize       # prioritized fix backlog
@arch-report         # Confluence-ready documentation

Scoring dimensions

Dimension	Weight
Cloud / Network Completeness	2.0
Connectivity	1.0
Technical Component Completeness	2.0
Interaction / Integration	2.0
Security Compliance	2.0
Terminology Expression	1.0
Total	10.0

Validation rules

Rules live in .claude/skills/arch-validate/rules/:

File	Series	Coverage
diagram-rules.yaml	V-	Shape, color, arrow direction, legend
interaction-rules.yaml	W-	Protocol, auth, integration platform placement
security-rules.yaml	S-	System auth, user auth, credential protection
accuracy-rules.yaml	E-	DC location, network segments, component completeness
platform-rules.yaml	—	Platform-specific rules: private cloud, AWS, Azure, GCP, Alibaba Cloud, Microsoft SaaS (E-* families)
compliance/terminology.yaml	—	Cloud terms, ISO 27001 / TOGAF mapping

Enforcement gate

After validation, the arch-enforce gate applies policy thresholds to the validation result and emits a CI-ready decision:

Decision	Condition	Exit code
PASS	Score ≥ 8.0 AND no must_fix issues	0
WARN	Score ≥ 6.0 AND < 8.0 AND no must_fix issues	0
BLOCK	Score < 6.0 OR any must_fix issue present	1

The gate is designed for automated CI pipelines. For human review, skip the gate and use arch-review directly.

Policy lives in two files:

• standards/arch-gate-policy.yaml — enforcement bounds, override conditions, meta-control
• standards/ci-gate-spec.yaml — per-dimension minimums, blocking rule IDs, profiles (financial / internet-facing / internal)

See ARCHITECTURE.md for the full control objective and audit trail specification.

Benchmark suite

The benchmark/ directory contains the AI and Ethics revision measurement suite for:

• Exp1: strict C-layer gate-to-A-layer build overhead.
• Exp2: temperature consistency at temperature=0.1 and temperature=0.3.

Current completed candidate results are documented in benchmark/EXPERIMENT_STATUS.md and summarized in benchmark/results/summary.md when local generated result files are present. Generated CSV/summary files are git-ignored; preserve final artifacts separately when preparing a manuscript revision package.

Supported platforms

Standards in standards/ cover six deployment targets. Each has a placement model, a zone model, identity and secret handling, data classification, matching placement rules, a design template, and a worked example:

Platform	Standard	Model	Example
Private cloud	private-cloud-standard.yaml	F5 ingress, east-west isolation via integration platform, PAW/ADFS	05
AWS	aws-standard.yaml	Hub-Spoke VPC, ALB+WAF, API Gateway in Spoke VPC, IAM + Secrets Manager	03
Azure	azure-standard.yaml	Hub-Spoke VNET, App Gateway WAF v2, APIM in Spoke VNET, Key Vault	01
Google Cloud	gcp-standard.yaml	Shared VPC host + service projects, global HTTPS LB + Cloud Armor, Cloud NAT, CMEK	09
Alibaba Cloud	aliyun-standard.yaml	Resource directory + central VPC, Anti-DDoS → WAF → SLB, CEN, RAM roles + STS	11
Microsoft SaaS	microsoft-saas-standard.yaml	Black-box tenant/environment containers, one boundary component, Entra ID + DLP (M365 / Power Platform / Dynamics 365)	10

All platform-specific names (API gateway, message bus, K8s platform) are read from config.yaml — no hardcoding in rules or skill files.

Project structure

ea-harness/
├── config.yaml              ← Organisation profile — edit this first
├── README.md
├── CLAUDE.md                ← Claude Code project rules
├── AGENTS.md                ← OpenCode / Codex / Copilot / Cursor project rules
├── ARCHITECTURE.md          ← Design rationale
├── archharness/             ← `python -m archharness` CLI (workspace + tools)
├── install.ps1 / install.sh ← cross-platform installers
├── benchmark/               ← Experiment scripts, prompts, status, and generated results
├── examples/                ← 11 worked examples (registry, req/v2, blueprint, diagram)
├── docs/                    ← first-run guide, CI recipe, platform recipes, integrations
├── schemas/                 ← Versioned contracts (req/v2, artifact/v1, validation/v1,
│                              enforcement/v1, metrics/v1)
├── projects/<id>/           ← Workspace projects (init with `archharness init-project`)
├── standards/               ← Platform-agnostic rules, topology specs, and gate policy
├── tools/
│   ├── config_loader.py     ← Shared config reader for Python tools
│   ├── arch-diagram-gen/    ← YAML → draw.io + PNG
│   └── arch-req-readers/    ← diagram / doc / API → req.yaml
├── tests/                   ← pytest suite
├── scripts/                 ← check_repo.py, sync_agents_skills.py (also run in CI)
├── .github/workflows/       ← CI pipeline
├── .github/agents/          ← GitHub Copilot custom agents (@arch-*)
├── .claude-plugin/          ← Claude Code plugin marketplace manifest
├── plugins/archharness/     ← Claude Code plugin bundle (skills mirror, generated)
├── .agents/skills/          ← Codex discovery mirror (generated)
├── .claude/skills/          ← Skill definitions (Claude Code slash commands)
└── .opencode/agents/        ← Agent definitions (OpenCode @agent-name)

Requirements

• Claude Code, OpenCode, Codex, GitHub Copilot, or Cursor
• Python 3.10+ (pip install -e ".[all]" pulls everything; pyyaml matplotlib is the minimal set)
• D2 CLI (d2lang.com) — renders the PNG image; matplotlib is the fallback, and draw.io desktop is needed only to edit the .drawio source

License

MIT — see LICENSE.