headroom / tests /test_cli /test_cli_learn.py
tudragon154203
fix: route count_tokens to api.anthropic.com, not proxy base_url
0adb431
from __future__ import annotations
from pathlib import Path
from types import SimpleNamespace
import click
import click.shell_completion as click_shell_completion
import pytest
from click.testing import CliRunner
from headroom.cli.learn import _AgentChoice
from headroom.cli.main import main
@pytest.fixture
def runner() -> CliRunner:
return CliRunner()
class FakeWriter:
def __init__(self) -> None:
self.calls: list[tuple[list[object], object, bool]] = []
def write(self, recommendations, project, dry_run: bool): # noqa: ANN001, ANN201
self.calls.append((recommendations, project, dry_run))
return SimpleNamespace(
dry_run=dry_run,
content_by_file={
Path(project.project_path) / "AGENTS.md": "<!-- headroom -->\nRule 1\nRule 2"
},
)
class FakePlugin:
def __init__(self, name: str, display_name: str, projects: list[object]) -> None:
self.name = name
self.display_name = display_name
self._projects = projects
self.writer = FakeWriter()
self.scan_calls: list[tuple[object, int]] = []
def detect(self) -> bool:
return True
def create_writer(self) -> FakeWriter:
return self.writer
def discover_projects(self) -> list[object]:
return self._projects
def scan_project(self, project, max_workers: int = 1): # noqa: ANN001, ANN201
self.scan_calls.append((project, max_workers))
return [SimpleNamespace(events=["event"], tool_calls=[], failure_count=0)]
class FakeAnalyzer:
def __init__(self, model: str | None = None) -> None:
self.model = model
self.calls: list[tuple[object, list[object]]] = []
def analyze(self, project, sessions): # noqa: ANN001, ANN201
self.calls.append((project, sessions))
return SimpleNamespace(
total_sessions=len(sessions),
total_calls=3,
total_failures=1,
failure_rate=1 / 3,
recommendations=[SimpleNamespace(section="Rules")],
)
def test_agent_choice_convert_and_shell_complete(monkeypatch: pytest.MonkeyPatch) -> None:
choice = _AgentChoice()
monkeypatch.setattr(click, "shell_completion", click_shell_completion)
monkeypatch.setattr(
"headroom.learn.registry.get_registry",
lambda: {"codex": object(), "claude": object()},
)
monkeypatch.setattr(
"headroom.learn.registry.available_agent_names",
lambda: ["claude", "codex"],
)
assert choice.convert("auto", None, None) == "auto"
assert choice.convert("CODEX", None, None) == "codex"
with pytest.raises(Exception, match="Unknown agent: bad"):
choice.convert("bad", None, None)
completions = choice.shell_complete(None, None, "c") # type: ignore[arg-type]
assert [item.value for item in completions] == ["claude", "codex"]
assert choice.get_metavar(None) == "[auto|<agent>]" # type: ignore[arg-type]
def test_learn_exits_cleanly_when_model_detection_fails(
monkeypatch: pytest.MonkeyPatch, runner: CliRunner
) -> None:
monkeypatch.setattr(
"headroom.learn.analyzer._detect_default_model",
lambda: (_ for _ in ()).throw(RuntimeError("no model")),
)
result = runner.invoke(main, ["learn"], catch_exceptions=False)
assert result.exit_code == 1
assert "Error: no model" in result.output
def test_learn_auto_agent_reports_no_detected_plugins(
monkeypatch: pytest.MonkeyPatch, runner: CliRunner
) -> None:
monkeypatch.setattr("headroom.learn.analyzer._detect_default_model", lambda: "gpt-4o")
monkeypatch.setattr("headroom.learn.registry.auto_detect_plugins", lambda: [])
monkeypatch.setattr("headroom.learn.analyzer.SessionAnalyzer", FakeAnalyzer)
result = runner.invoke(main, ["learn"], catch_exceptions=False)
assert result.exit_code == 0
assert "No coding agent data found." in result.output
def test_learn_single_agent_shows_available_projects_when_cwd_missing(
monkeypatch: pytest.MonkeyPatch, runner: CliRunner, tmp_path: Path
) -> None:
project = SimpleNamespace(name="demo", project_path=tmp_path / "demo")
plugin = FakePlugin("codex", "Codex", [project])
monkeypatch.setattr("headroom.learn.analyzer._detect_default_model", lambda: "gpt-4o")
monkeypatch.setattr("headroom.learn.registry.get_plugin", lambda name: plugin)
monkeypatch.setattr("headroom.learn.analyzer.SessionAnalyzer", FakeAnalyzer)
with runner.isolated_filesystem(temp_dir=tmp_path):
result = runner.invoke(main, ["learn", "--agent", "codex"], catch_exceptions=False)
assert result.exit_code == 0
assert "No codex project data found for" in result.output
assert "Available codex projects:" in result.output
assert "demo" in result.output
def test_learn_project_lookup_and_apply_flow(
monkeypatch: pytest.MonkeyPatch, runner: CliRunner, tmp_path: Path
) -> None:
project_path = tmp_path / "project-a"
project_path.mkdir()
matched = SimpleNamespace(name="project-a", project_path=project_path)
unmatched = SimpleNamespace(name="project-b", project_path=tmp_path / "project-b")
plugin = FakePlugin("codex", "Codex", [matched, unmatched])
analyzer = FakeAnalyzer()
monkeypatch.setattr("headroom.learn.analyzer._detect_default_model", lambda: "gpt-4o")
monkeypatch.setattr("headroom.learn.registry.get_plugin", lambda name: plugin)
monkeypatch.setattr("headroom.learn.analyzer.SessionAnalyzer", lambda model=None: analyzer)
result = runner.invoke(
main,
["learn", "--agent", "codex", "--project", str(project_path), "--apply", "--workers", "4"],
catch_exceptions=False,
)
assert result.exit_code == 0, result.output
assert "Path: " in result.output
assert "Analyzing with gpt-4o..." in result.output
assert "Recommendations: 1" in result.output
assert "[WROTE]" in result.output
assert "Rule 1" in result.output
assert plugin.scan_calls == [(matched, 4)]
assert analyzer.calls[0][0] is matched
assert plugin.writer.calls[0][2] is False
def test_learn_reports_missing_requested_project_and_lists_discovered(
monkeypatch: pytest.MonkeyPatch, runner: CliRunner, tmp_path: Path
) -> None:
requested = tmp_path / "missing"
requested.mkdir()
discovered = SimpleNamespace(name="project-a", project_path=tmp_path / "project-a")
plugin = FakePlugin("claude", "Claude Code", [discovered])
monkeypatch.setattr("headroom.learn.analyzer._detect_default_model", lambda: "gpt-4o")
monkeypatch.setattr("headroom.learn.registry.get_plugin", lambda name: plugin)
monkeypatch.setattr("headroom.learn.analyzer.SessionAnalyzer", FakeAnalyzer)
result = runner.invoke(
main,
["learn", "--agent", "claude", "--project", str(requested)],
catch_exceptions=False,
)
assert result.exit_code == 0
assert f"No project data found for {requested.resolve()}" in result.output
assert "Available discovered projects:" in result.output
assert "[claude]" in result.output
def test_learn_analyze_all_uses_default_workers_and_prints_summary(
monkeypatch: pytest.MonkeyPatch, runner: CliRunner, tmp_path: Path
) -> None:
projects_a = [SimpleNamespace(name="a", project_path=tmp_path / "a")]
projects_b = [SimpleNamespace(name="b", project_path=tmp_path / "b")]
plugin_a = FakePlugin("codex", "Codex", projects_a)
plugin_b = FakePlugin("claude", "Claude Code", projects_b)
analyzer = FakeAnalyzer()
monkeypatch.setattr("headroom.learn.analyzer._detect_default_model", lambda: "gpt-4o")
monkeypatch.setattr(
"headroom.learn.registry.auto_detect_plugins",
lambda: [plugin_a, plugin_b],
)
monkeypatch.setattr("headroom.learn.analyzer.SessionAnalyzer", lambda model=None: analyzer)
monkeypatch.setattr("os.cpu_count", lambda: 12)
result = runner.invoke(main, ["learn", "--all"], catch_exceptions=False)
assert result.exit_code == 0, result.output
assert "Detected agents: Codex, Claude Code" in result.output
assert "Total: 2 projects, 2 failures, 2 recommendations" in result.output
assert plugin_a.scan_calls == [(projects_a[0], 8)]
assert plugin_b.scan_calls == [(projects_b[0], 8)]
def test_learn_handles_empty_sessions_and_no_pattern_outputs(
monkeypatch: pytest.MonkeyPatch, runner: CliRunner, tmp_path: Path
) -> None:
no_sessions = SimpleNamespace(name="empty", project_path=tmp_path / "empty")
no_failures = SimpleNamespace(name="clean", project_path=tmp_path / "clean")
no_actions = SimpleNamespace(name="no-actions", project_path=tmp_path / "no-actions")
class BranchingPlugin(FakePlugin):
def scan_project(self, project, max_workers: int = 1): # noqa: ANN001, ANN201
self.scan_calls.append((project, max_workers))
if project is no_sessions:
return []
return [SimpleNamespace(events=["event"], tool_calls=[], failure_count=0)]
class BranchingAnalyzer(FakeAnalyzer):
def analyze(self, project, sessions): # noqa: ANN001, ANN201
self.calls.append((project, sessions))
if project is no_failures:
return SimpleNamespace(
total_sessions=1,
total_calls=2,
total_failures=0,
failure_rate=0.0,
recommendations=[],
)
return SimpleNamespace(
total_sessions=1,
total_calls=2,
total_failures=1,
failure_rate=0.5,
recommendations=[],
)
plugin = BranchingPlugin("codex", "Codex", [no_sessions, no_failures, no_actions])
analyzer = BranchingAnalyzer()
monkeypatch.setattr("headroom.learn.analyzer._detect_default_model", lambda: "gpt-4o")
monkeypatch.setattr("headroom.learn.registry.get_plugin", lambda name: plugin)
monkeypatch.setattr("headroom.learn.analyzer.SessionAnalyzer", lambda model=None: analyzer)
result = runner.invoke(main, ["learn", "--agent", "codex", "--all"], catch_exceptions=False)
assert result.exit_code == 0, result.output
assert "No conversation data found." in result.output
assert "No failures or patterns found." in result.output
assert "No actionable patterns found." in result.output