dotdog Dogfood 🐕

dotdog validates itself. This page shows real output from running dotdog on its own spec. No mock data. No demos. The tool eating its own dogfood.

.dog files

100%

complete

nodes in .dag

94%

token savings

28/28

tests passing

6/6

simulate

Spec Files

SPEC.dog data-model.dog constitution.dog DESIGN-SYSTEM.dog plan.dog INDEX.dog COPY.dog

Compile

8563 → 512 tokens (94% savings)
8 nodes, 4 edges
8051 tokens saved

Predictions

✅ Correct

.dag token savings exceed 90%

90% confidence — verified at 94% actual

✅ Correct

First external dotdog adopter

50% confidence — community contributors merged

⏳ Pending

dotdog reaches 200 PRs by July 2026

80% confidence — tracking at current velocity

Simulation

Scenario: Node, Task, Prediction → Compile → DAG

[1/6] Write Node, Task, and Prediction specs       ✓
[2/6] Compile parses entities into a DAG graph     ✓
[3/6] DAG has token savings exceeding prediction   ✓
[4/6] Validate Task and Node completeness at 100%  ✓
[5/6] Prediction list shows forecasts with status  ✓
[6/6] DAG served via MCP to AI agents              ✓

RESULT: Success (6/6 steps passed)

The Pipeline

.spec files → dotdog validate  → 100% complete
            → dotdog compile   → 94% savings
            → dotdog simulate   → 6/6 passed
            → dotdog staleness  → spec matches reality
            → dotdog badge      → shields.io SVG
            → dotdog serve      → AI agents query specs

For AI Agents

This page's data comes from dotdog commands. An AI agent can query the same data via MCP:

getEntity("Node")    → exact properties
traverse("DAG", 2)   → full compile graph
search("Prediction") → all predictions

Read the for agents page for setup instructions.