Odoo Doctor 🩺

Unified health scoring for Odoo custom addons.

Combines confidence-aware static analysis with optional external linters (Ruff, Pylint-Odoo) to produce a single 0–100 score per addon — designed for CI pipelines and AI coding agents.

Quick Start

# 1. Chạy ngay (không cần install)
pipx run odoo-doctor scan .

# 2. Install global
pip install odoo-doctor
odoo-doctor scan .

# 3. JSON output cho CI / agents
odoo-doctor scan . --json

# 4. Fail nếu score < 80
odoo-doctor scan . --min-score 80

# 5. Chỉ scan file đã thay đổi (PR review)
odoo-doctor scan . --diff main --json

What it checks

Plus Ruff and Pylint-Odoo findings when those tools are installed.

The full, generated reference (36 rules, with before/after examples) is in docs/rules.md; every finding links to its entry. Disable a rule with odoo-doctor rules disable <rule-name>; write your own with the stable plugin API. What you can rely on across upgrades (CLI flags, exit codes, JSON keys, rule IDs) is in the stability contract.

Score explained

Each category score starts at 100 and loses points per high-confidence finding, where each finding deducts tier_impact × category_weight (default weight 1.0; override via [category_weights]). The overall score blends only in-scope categories (those with at least one active rule):

category_score = max(0, 100 − Σ(tier_impact × category_weight))
overall        = 0.4 × min(in_scope_category_scores)
               + 0.6 × avg(in_scope_category_scores)

Tier impacts: P0 = 25, P1 = 10, P2 = 4, P3 = 1.

Each finding deducts points by tier: P0 = −25, P1 = −10, P2 = −4, P3 = −1.
Only high confidence findings count toward the score.

Fix first

Every scan ranks the score-eligible findings of each module by marginal score gain per effort, so you (or an agent) fix the right thing first. The terminal prints the top 5 under Fix first; --json has the top 10 per module as modules.<name>.fix_priorities:

{"rank": 1, "rule": "missing-ondelete", "file_path": "...", "line": 79,
 "impact": 10.0, "effort": 1, "roi": 10.0, "projected_score": 52.1,
 "score_gain": 4.7, "fixable": false}

• effort is a coarse 1-3 estimate per rule (1 mechanical, 2 local code change, 3 restructuring); auto-fixable rules count as 1.
• projected_score is the module score after fixing this finding and every one ranked before it; score_gain is the change this step made. A category that is already at 0 shows +0.0 until enough of its findings are fixed — the weakest category carries the 0.4 × min term, so it is ranked first.

Configuration

odoo-doctor init   # creates odoo-doctor.toml

[odoo-doctor]
odoo_version = "17.0"
addons_paths = ["."]
odoo_source_path = "/path/to/odoo/source"
capabilities = ["enterprise", "owl"]
min_score = 75

[adapters]
ruff = true
pylint_odoo = false

[severity]
"search-in-loop" = "warning"

[ignore]
rules = []
files = ["**/migrations/**"]
modules = []

[category_weights]
Security = 1.5

[surfaces.pr_comment]
min_confidence = "all"
categories = []

[surfaces.ci_failure]
min_confidence = "high"
categories = []

CI Integration

GitHub Actions

The easiest way to integrate Odoo Doctor into GitHub Actions is using our official composite action. See .github/workflows/odoo-doctor.example.yml for a full example.

- name: Odoo Doctor Scan
  uses: minhhq-a1/odoo-doctor@v0.7.0
  with:
    fail-on: warning
    min-score: 75
    diff-base: main
    pr-comment: true
    paths: "."

If you prefer pip install, you can run it directly:

- name: Odoo Doctor (pip)
  run: |
    pip install odoo-doctor
    odoo-doctor scan . --format github --min-score 75 --fail-on error

SARIF & Baseline Mode

For GitHub Code Scanning and IDE integration:

odoo-doctor scan . --format sarif > results.sarif
# Then upload via github/codeql-action/upload-sarif

To capture current debt and block only new findings in CI:

odoo-doctor scan . --write-baseline .odoo-doctor-baseline.json
# Commit the baseline, then in CI:
odoo-doctor scan . --baseline .odoo-doctor-baseline.json --fail-on warning

CI/PR Surfaces

• --format github: Emits GitHub Actions annotations inline.
• --score-delta <base-ref>: Opt-in PR score delta. It does a worktree-isolated second scan and needs git history (fetch-depth: 0 in Actions).
• Sticky PR comment: Posted/updated via gh when --format github runs in a PR with a valid GH_TOKEN. Idempotent via a hidden marker.

CI failure policy

--fail-on <severity> only counts findings admitted by [surfaces.ci_failure], which defaults to P0/P1 at high confidence: style/advisory (P2/P3) and low-confidence findings are reported but never fail a build. Adjust it:

[surfaces.ci_failure]
tiers = ["P0", "P1", "P2"]   # [] = every tier
min_confidence = "high"

Score history & badge

Track the score over time and publish a badge without any server (see docs/score-history.md):

odoo-doctor scan . --history .odoo-doctor/history.jsonl --badge badge.svg
odoo-doctor history show .odoo-doctor/history.jsonl --max-drop 3   # exit 2 on regression
odoo-doctor history import history.jsonl old-report.json           # pre-0.4.0 reports

pre-commit

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: odoo-doctor
        name: Odoo Doctor
        language: system
        entry: odoo-doctor scan --diff HEAD --fail-on error
        pass_filenames: false
        types: [python]

Agent Usage

Odoo Doctor is designed for AI coding agents. Install the SKILL.md files:

odoo-doctor install   # installs to .odoo-doctor/skills/

Then in your agent workflow:

# After editing Odoo code
odoo-doctor scan . --diff main --json

# Fix P0/P1 findings with confidence: "high"
# Re-scan to verify fixes
odoo-doctor scan . --diff main --json

Use odoo-doctor rules explain <rule-name> to understand any finding (description, why, fix, examples and a docs link).

Use odoo-doctor rules stats to see which rules your team suppresses most (inline # odoo-doctor: disable, [ignore] rules, [severity] = "off"). Rules where most findings are suppressed are flagged as noisy, with a suggestion to lower their severity.

In your editor (experimental). pip install 'odoo-doctor[lsp]' adds odoo-doctor lsp, a language server that shows the same findings as diagnostics and offers quick fixes (apply the auto-fix, or disable the rule on a line, in a file or in odoo-doctor.toml). A VS Code extension is in editors/vscode; setup for VS Code, Neovim and Helix is in docs/lsp.md.

Generating stubs for your Odoo version

Bundled stubs cover 17.0, 18.0, 19.0 (core models only).
For full accuracy, generate from source or a live instance:

# From Odoo source checkout
python -m odoo_doctor.graph.stubs.build_stubs source \
  --odoo-path /path/to/odoo \
  --version 17.0

# From a live Odoo instance (no source needed)
python -m odoo_doctor.graph.stubs.build_stubs rpc \
  --rpc-url http://localhost:8069 \
  --rpc-db mydb \
  --rpc-password admin \
  --version 17.0

The generated JSON is written to src/odoo_doctor/graph/stubs/data/<version>.json
(or --output <path> for a custom location).

Inline suppression

x = self.env.cr.execute(f"SELECT ...")  # odoo-doctor: disable=raw-sql-string-interpolation

<record id="my_record" model="ir.ui.view">  <!-- odoo-doctor: disable=duplicate-xml-id -->

Exit codes

odoo-doctor history show --max-drop N also exits 2 when the score regressed.

Development

git clone https://github.com/minhhq-a1/odoo-doctor
cd odoo-doctor
pip install -e ".[dev]"
pytest                    # 492 test cases
pytest --cov=odoo_doctor  # with coverage