Business Blueprint Skill
Use the Python scripts in this repository as the execution surface.
Output Directory
All generated files (blueprint JSON, viewers, exports) go into projects/workspace/ — not the repository root.
python -m business_blueprint.cli --plan projects/workspace/solution.blueprint.json --from "..."
python -m business_blueprint.cli --project projects/workspace/solution.blueprint.json
python -m business_blueprint.cli --export projects/workspace/solution.blueprint.json
Industry Selection
Choose --industry from exactly one of: "common", "finance", "manufacturing", "retail". Select the closest match based on the user's domain and materials; do not invent other values.
| Industry | Hints content |
|---|
common | No hints — generic domains |
finance | Risk control, credit, compliance, customer profile, etc. |
manufacturing | Production planning, quality, warehouse, supply chain, etc. |
retail | Store operations, membership, POS, order fulfillment, etc. |
How to Generate a Blueprint
The AI agent is responsible for entity extraction. The Python tool handles JSON writing, visualization, and export.
Step 1: Read industry hints
Read the seed template at business_blueprint/templates/{industry}/seed.json and get the industryHints.checklist.
Step 2: Extract entities from source text
Using the user's source material AND the industry hints checklist, extract:
- capabilities: business capability areas (name, description)
- actors: roles/people involved (name)
- flowSteps: business process steps (name, actorId, capabilityIds, stepType)
- systems: IT systems that support capabilities (name, description, capabilityIds)
Step 3: Write the blueprint JSON
Write the JSON file directly to the output path. Use this schema:
{
"version": "1.0",
"meta": {
"title": "...",
"industry": "retail",
"revisionId": "rev-YYYYMMDD-NN",
"parentRevisionId": null,
"lastModifiedAt": "ISO8601",
"lastModifiedBy": "ai"
},
"context": {
"goals": [],
"scope": [],
"assumptions": [],
"constraints": [],
"sourceRefs": [{"type": "inline-text", "excerpt": "..."}],
"clarifyRequests": [],
"clarifications": []
},
"library": {
"capabilities": [
{"id": "cap-xxx", "name": "...", "level": 1, "description": "...", "ownerActorIds": [], "supportingSystemIds": []}
],
"actors": [
{"id": "actor-xxx", "name": "..."}
],
"flowSteps": [
{"id": "flow-xxx", "name": "...", "actorId": "actor-xxx", "capabilityIds": ["cap-xxx"], "systemIds": [], "stepType": "task", "inputRefs": [], "outputRefs": []}
],
"systems": [
{"id": "sys-xxx", "kind": "system", "name": "...", "aliases": [], "description": "...", "resolution": {"status": "canonical", "canonicalName": "..."}, "capabilityIds": ["cap-xxx"]}
]
},
"relations": [
{"id": "rel-xxx", "type": "supports", "from": "sys-xxx", "to": "cap-xxx", "label": "支撑"}
],
"views": [],
"editor": {"fieldLocks": {}, "theme": "enterprise-default"},
"artifacts": {}
}
Step 4: Generate visualizations
python -m business_blueprint.cli --export <blueprint.json>
This generates SVG + HTML viewer by default. Use --format drawio|excalidraw|mermaid for other formats.
Export View Selection Policy
Treat export view choice as a routing decision, not a styling preference.
- If a request matches a supported, standard export template, use that template.
- If there is no standard export template for the requested diagram, fall back to
freeflow.
- Do not substitute
swimlane, matrix, product tree, or other generic views just because they are available.
- When embedding a blueprint diagram into a report or ad hoc analysis,
freeflow is the safe default unless the user explicitly asks for a supported standard template.
Step 5: Generate downstream projection
python -m business_blueprint.cli --project <blueprint.json>
This generates solution.projection.json, the canonical machine projection consumed by downstream report/slide workflows.
Workflow Decision Tree
User provides raw requirements / meeting notes?
→ AI agent reads hints, extracts entities, writes blueprint JSON
→ Optionally run --project for downstream machine handoff
→ Then run --export for visualization
User needs diagram files (SVG, draw.io, etc.)?
→ --export (default: SVG + HTML viewer)
User unsure about blueprint quality?
→ --validate
User wants downstream report / slide generation?
→ --project
Commands
| Command | Description |
|---|
--plan <path> --from <text> | Generate empty blueprint JSON from source text (AI should prefer writing JSON directly) |
--project <path> | Generate canonical projection JSON for downstream skills |
--export <path> | Export SVG + HTML viewer (default), or use --format for other formats |
--validate <path> | Validate a blueprint and print JSON results |
Export Formats
| Format | File | Use Case |
|---|
svg (default) | solution.exports/solution.svg + HTML viewer | Quick preview, embedding |
drawio | solution.exports/solution.drawio | Editable diagrams |
excalidraw | solution.exports/solution.excalidraw | Whiteboard-style diagrams |
mermaid | solution.exports/solution.mermaid.md | GitHub-native rendering |
Collaboration Boundary
This skill produces semantic intermediate artifacts. Downstream skills consume them:
report-creator consumes solution.projection.json → assembles reports
slide-creator consumes solution.projection.json → assembles presentations
- Other skills may consume
relations → generate PlantUML or other diagram syntax
- Downstream skills should never directly edit
solution.blueprint.json
solution.handoff.json is viewer-only metadata, not a downstream narrative input
Sandbox Execution
When running in an isolated Python sandbox (Jupyter, notebook, cloud REPL) that auto-installs dependencies:
-
The sandbox auto-installs kai-business-blueprint from PyPI. Do NOT try to pip install -e or use sys.path hacks with __file__ — __file__ is undefined in Jupyter.
-
For --export (after blueprint JSON exists):
from pathlib import Path
from business_blueprint.model import load_json
from business_blueprint.export_svg import export_svg_auto
from business_blueprint.export_html import export_html_viewer
blueprint_path = Path("solution.blueprint.json")
blueprint = load_json(blueprint_path)
export_dir = blueprint_path.with_name("solution.exports")
export_dir.mkdir(parents=True, exist_ok=True)
# Default fallback is freeflow unless a standard export template applies.
export_svg_auto(blueprint, export_dir / "solution.svg")
export_html_viewer(blueprint, blueprint_path.with_name("solution.blueprint.html"))
-
Prohibited patterns in sandbox:
__file__ — undefined in Jupyter
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) — will raise NameError
subprocess.run(["business-blueprint", ...]) — sandbox runs Python cells, not shell
os.system() — same reason
Architecture Diagram Generation
When user requests an architecture diagram (keywords: "架构图", "architecture diagram", "--export", "diagram"):
- Read
references/architecture-design-system.md for the complete design system.
- Read the appropriate template from
references/architecture-templates/ based on the user's domain:
- AWS/Serverless/Lambda →
serverless.md
- Microservices/Kubernetes/微服务 →
microservices.md
- Other → use
serverless.md as a structural reference
- Read the blueprint JSON to extract entities and flow steps.
- Generate a self-contained HTML file with inline SVG following the design system rules.
- Write the output file to the same directory as the blueprint JSON.
If the request does not match one of the supported standard templates above, stay on the default freeflow export path. Do not switch to another generic view type as a fallback.
If a standard template would create a squeezed, clipped, or overcrowded diagram, stop using the fixed template geometry and fall back to freeflow or a wrapped multi-row layout.
Route eligibility matrix
Use an explicit route contract before rendering:
| Route | Structural prerequisites | First fallback | Terminal behavior |
|---|
freeflow | Any valid blueprint with at least one renderable node or relation | None | If integrity still fails, export exits non-zero with a structural diagnostics payload |
architecture-template | Recognizable L→R architecture shape, categorized systems, limited per-layer density, and no route-breaking overflow risk | freeflow | Same as above |
poster | Clear layer/group structure with bounded peer density per row or wrapped-row support | wrapped poster or freeflow | Same as above |
swimlane | Actor-owned flow steps with meaningful lane grouping | freeflow | Same as above |
hierarchy | Stable tree/group relationship with low ambiguity in parent-child grouping | freeflow | Same as above |
evolution | Ordered chronological or staged progression data | freeflow | Same as above |
Do not invent route heuristics ad hoc inside a renderer. Route eligibility must stay explicit and reviewable.
Generation Rules
- Use dark mode by default (
#020617 bg + 40px grid). Only use light mode when the user explicitly asks for it.
- L→R data flow: Clients(左) → Frontend → Backend → Database(右)
- Map
systems[].category to semantic colors from the design system
- Map
systems[].properties.type == "aws" → AWS Region boundary box
- Map
systems[].properties.type == "k8s" → Kubernetes Cluster boundary box
- Use
flowSteps[].seqIndex for L→R ordering
- Component sizing: 0-1 cap = small(44px h), 2-4 = medium(80px h), 5+ = large(80px h)
- Layout must be content-driven. Never force every node in a layer into one fixed row if that creates toothpaste-style squeezing.
- When a layer has more than 3 peer nodes, or labels/features become tight, wrap into multiple rows or widen the canvas before shrinking the content.
- Render users/actors as actor labels, badges, or lane headers by default. Do not render them as ordinary system cards unless the user explicitly asks for that visual treatment.
- Legend must live in a bottom safe area and participate in canvas sizing. Never place the legend as a floating overlay in the top-right corner.
- Final SVG/HTML height must be derived from the bottom-most node, legend, summary cards, and footer plus padding. Do not use fixed-height wrappers or
overflow: hidden that can clip the last row.
- Z-order: bg → grid → title → region → arrows → nodes → legend → cards → footer
- Component border:
rx="8", stroke-width="2"
- Region border:
rx="16", stroke-dasharray="8,4", opacity="0.4"
- Geometry-sensitive integrity checks must use the numeric thresholds from
evals/export-integrity-thresholds.json, not prose heuristics.
Output
- Single HTML file:
{blueprint_stem}.html alongside the blueprint JSON
- No external dependencies (except Google Fonts CDN for JetBrains Mono)
- Opens in any browser, printable to PDF
Error Handling
- If
--validate returns errors: fix structural issues before proceeding to --export.
- If
--validate returns only warnings: proceed but note the warnings in any handoff.
- If Python version < 3.12: the package will refuse to install. Use
python3 -m business_blueprint.cli with system Python as fallback.
- If a specialized route fails integrity: fall back to its configured fallback route.
- If
freeflow also fails integrity: export exits non-zero with a structural diagnostics payload instead of emitting a silently broken artifact.
Cross-Platform Scope
Phase 2 does not attempt full Windows terminal parity.
Known deferred cases:
- PowerShell pipe quirks beyond documented CLI contract tests
- console-default encoding issues outside explicit UTF-8 execution paths
Accepted workaround for encoding-sensitive runs:
- use
python -m business_blueprint.cli
- set
PYTHONIOENCODING=utf-8 where needed