ai_dev.yaml

# Configuration that produced the analysis in coprogramming_effectiveness.ipynb.
#
# This is the *anonymized* config — it shows the shape used to analyze a real
# 117K-LOC ROS2 robotics project (1 developer + Claude, ~10 months). Specific
# package names have been replaced with their generic role; specific roles and
# hour ranges reflect the original analysis but are commented to explain the
# reasoning.

project:
  name: "Robotics Project"
  team_size: 1                            # solo developer
  human_daily_rate_usd: 800
  ai_monthly_cost_usd: 200
  package_root: "src/robotics_project"    # ROS2 src-layout convention

# Eight functional domains. The patterns target the file paths in this
# project's src tree. Adapt for your own structure.
domains:
  vision:        { pattern: "vision/",        color: "#2563EB" }
  control_cpp:   { pattern: "control/.*\\.(cpp|hpp|h)", color: "#DC2626" }
  game_logic:    { pattern: "control/.*\\.py", color: "#F59E0B" }
  prediction:    { pattern: "prediction/",    color: "#059669" }
  ml:            { pattern: "ml/",            color: "#7C3AED" }
  depth_vision:  { pattern: "depth_vision/",  color: "#0891B2" }
  msgs:          { pattern: "msgs/",          color: "#6B7280" }
  bringup:       { pattern: "bringup/",       color: "#EC4899" }
  visualization: { pattern: "visualization/", color: "#8B5CF6" }
  config:        { pattern: "description/",   color: "#78716C" }

# Specialist roles for the top-down comparison. The hour ranges are what a
# senior engineer working in each domain would reasonably need to deliver
# the LOC range listed, including design + implementation + testing +
# self-review (no AI assist).
roles:
  - role: "Senior Robotics Engineer"
    scope: "C++ control, MoveIt2, motion planning"
    loc: 19447
    pm_low: 8
    pm_high: 10
    color: "#DC2626"

  - role: "Computer Vision Engineer"
    scope: "CUDA pipeline, Kalman filters, tracking"
    loc: 13153
    pm_low: 5
    pm_high: 7
    color: "#2563EB"

  - role: "ML Engineer"
    scope: "Training pipeline, ONNX, prediction"
    loc: 47979
    pm_low: 6
    pm_high: 8
    color: "#7C3AED"

  - role: "Depth Vision Engineer"
    scope: "Depth-sensor integration, 3D detection"
    loc: 7861
    pm_low: 3
    pm_high: 4
    color: "#0891B2"

  - role: "ROS2 Systems Integrator"
    scope: "Launch files, msgs, bringup, deployment"
    loc: 6115
    pm_low: 3
    pm_high: 4
    color: "#EC4899"

  - role: "UI/Visualization Dev"
    scope: "Control center GUI, RViz, game logic"
    loc: 23288
    pm_low: 3
    pm_high: 4
    color: "#8B5CF6"

# Bottom-up estimator constants. These match the original notebook's values:
# C++/CUDA-heavy work uses higher per-LOC rates; Python and config use lower.
effort:
  integration_multiplier: 1.3   # cross-domain commits are harder
  test_debug_multiplier: 1.5    # testing + debugging overhead
  max_hours_per_commit: 40      # 1-week hard cap

# The default agent registry already covers the four Claude variants observed
# in this repo's commit trailers (Opus 4.6, Opus 4.6 1M, Opus 4.7 1M, Sonnet
# 4.6). No extensions needed.
agents:
  extend: []

# Annotate the cumulative-LOC chart with milestones.
milestones:
  "initial commit": "Project Start"
  "MoveIt": "MoveIt2 Integration"
  "vision": "Vision Pipeline"
  "play mode": "Play Mode"
  "prediction": "Prediction System"
  "depth": "Depth Vision"
  "ONNX": "ML Model Deploy"
  "kalman": "Kalman Tracking"

output:
  formats: ["html", "json"]
  out_dir: "."
  hide_code: true

# AI judge disabled by default — the case study analysis was generated before
# the judge module existed. To enable it for your own run, set:
#   judge.enabled: true
#   judge.provider: claude-cli
# (after running `ai-dev-effectiveness init-judge` to install the subagent.)
judge:
  enabled: false