Files
repo-scoping/src/repo_scoping/cli.py

718 lines
27 KiB
Python

from __future__ import annotations
import argparse
import json
from dataclasses import asdict
from pathlib import Path
from typing import Sequence
from repo_scoping.acceptance import (
criteria_registry_json,
criteria_registry_markdown,
load_quality_criteria,
)
from repo_scoping.core.models import CharacteristicRebuildResult, Repository
from repo_scoping.core.service import RegistryService
from repo_scoping.llm_extraction import LLMCandidateExtractor, create_llm_connect_adapter
from repo_scoping.repo_ingestion.git import GitIngestionService
from repo_scoping.self_scoping.assessment import artifact_json, export_assessment_artifact
from repo_scoping.self_scoping.comparison import (
compare_assessment_to_golden,
comparison_json,
comparison_markdown,
load_json,
)
from repo_scoping.storage.sqlite import NotFoundError, RegistryStore
from repo_scoping.web_api.app import Settings
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
prog="repo-scoping",
description="Repository Scoping maintenance commands.",
)
subparsers = parser.add_subparsers(dest="command", required=True)
rebuild = subparsers.add_parser(
"rebuild-characteristics",
help="Rebuild candidate characteristics for one or more repositories.",
)
target = rebuild.add_mutually_exclusive_group(required=True)
target.add_argument("--repo", help="Repository id or exact repository name.")
target.add_argument("--all", action="store_true", help="Rebuild every repository.")
rebuild.add_argument("--dry-run", action="store_true", help="Preview without clearing approved characteristics.")
rebuild.add_argument("--no-llm", action="store_true", help="Disable configured LLM assistance.")
rebuild.add_argument(
"--agentic-review",
action="store_true",
help="Request configured agentic review after a confirmed rebuild.",
)
rebuild.add_argument(
"--confirm",
action="store_true",
help="Confirm a destructive rebuild for selected repositories.",
)
rebuild.add_argument(
"--confirm-all",
action="store_true",
help="Confirm a destructive all-repository rebuild.",
)
rebuild.add_argument("--database-path", help="Override REPO_SCOPING_DATABASE_PATH.")
rebuild.add_argument("--checkout-root", help="Override REPO_SCOPING_CHECKOUT_ROOT.")
export = subparsers.add_parser(
"export-assessment",
help="Export a completed analysis run as a self-scoping assessment artifact.",
)
export.add_argument("--repo", required=True, help="Repository id or exact repository name.")
export.add_argument("--analysis-run", type=int, required=True, help="Completed analysis run id.")
export.add_argument("--output", help="Write artifact JSON to this path instead of stdout.")
export.add_argument(
"--role",
choices=["baseline", "challenger", "negative_regression_seed"],
default="challenger",
help="Assessment artifact role.",
)
export.add_argument(
"--outcome",
choices=[
"baseline",
"challenger",
"preferred",
"tied",
"rejected",
"superseded",
"needs-human",
],
default="challenger",
help="Initial assessment outcome.",
)
export.add_argument("--reviewer", default="codex", help="Reviewer name recorded in the artifact.")
export.add_argument("--summary", help="Assessment summary override.")
export.add_argument("--database-path", help="Override REPO_SCOPING_DATABASE_PATH.")
export.add_argument("--checkout-root", help="Override REPO_SCOPING_CHECKOUT_ROOT.")
compare = subparsers.add_parser(
"compare-assessment",
help="Compare a self-scoping assessment artifact against a golden profile.",
)
compare.add_argument("--golden", required=True, help="Golden profile JSON path.")
compare.add_argument(
"--assessment",
required=True,
help="Assessment artifact JSON path.",
)
compare.add_argument("--output", help="Write comparison report to this path instead of stdout.")
compare.add_argument(
"--format",
choices=["json", "markdown"],
default="markdown",
help="Comparison report format.",
)
self_assess = subparsers.add_parser(
"self-assess",
help="Run repo-scoping against a source tree and compare the result to a golden profile.",
)
self_assess.add_argument(
"--repo",
default="repo-scoping",
help="Repository id or exact repository name to reuse; created by name when absent.",
)
self_assess.add_argument(
"--source-path",
default=".",
help="Source tree to analyze; defaults to the current working directory.",
)
self_assess.add_argument(
"--golden",
default="docs/self-scoping/golden/repo-scoping-golden-profile.v1.json",
help="Golden profile JSON path.",
)
self_assess.add_argument(
"--assessment-output",
help="Write challenger assessment artifact JSON to this path.",
)
self_assess.add_argument(
"--comparison-output",
help="Write comparison report to this path instead of stdout.",
)
self_assess.add_argument(
"--format",
choices=["json", "markdown"],
default="markdown",
help="Comparison report format.",
)
self_assess.add_argument(
"--with-llm",
action="store_false",
dest="no_llm",
help="Use configured LLM assistance during the self-assessment run.",
)
self_assess.add_argument(
"--agentic-review",
action="store_true",
help="Request configured agentic review; leaves candidates pending when none is configured.",
)
self_assess.add_argument(
"--fail-on-regression",
action="store_true",
help="Return exit code 1 only when comparison status is regression.",
)
self_assess.add_argument("--database-path", help="Override REPO_SCOPING_DATABASE_PATH.")
self_assess.add_argument("--checkout-root", help="Override REPO_SCOPING_CHECKOUT_ROOT.")
self_assess.set_defaults(no_llm=True)
criteria = subparsers.add_parser(
"list-quality-criteria",
help="List the active characteristic quality criteria registry.",
)
criteria.add_argument(
"--criteria-path",
help="Override the default quality criteria registry JSON path.",
)
criteria.add_argument("--output", help="Write criteria output to this path instead of stdout.")
criteria.add_argument(
"--format",
choices=["json", "markdown"],
default="markdown",
help="Criteria output format.",
)
legacy = subparsers.add_parser(
"list-legacy-auto-approvals",
help="List historical trusted deterministic auto-approval records.",
)
legacy.add_argument("--database-path", help="Override REPO_SCOPING_DATABASE_PATH.")
legacy.add_argument("--checkout-root", help="Override REPO_SCOPING_CHECKOUT_ROOT.")
legacy.add_argument("--output", help="Write inventory output to this path instead of stdout.")
legacy.add_argument(
"--format",
choices=["json", "markdown"],
default="markdown",
help="Inventory output format.",
)
dataset = subparsers.add_parser(
"assess-dataset",
help="Summarize repository generation coverage across the local dataset.",
)
dataset.add_argument("--database-path", help="Override REPO_SCOPING_DATABASE_PATH.")
dataset.add_argument("--checkout-root", help="Override REPO_SCOPING_CHECKOUT_ROOT.")
dataset.add_argument("--output", help="Write dataset assessment to this path instead of stdout.")
dataset.add_argument(
"--format",
choices=["json", "markdown"],
default="markdown",
help="Dataset assessment output format.",
)
return parser
def main(argv: Sequence[str] | None = None) -> int:
parser = build_parser()
args = parser.parse_args(argv)
if args.command == "rebuild-characteristics":
return rebuild_characteristics_command(args, parser)
if args.command == "export-assessment":
return export_assessment_command(args, parser)
if args.command == "compare-assessment":
return compare_assessment_command(args)
if args.command == "self-assess":
return self_assess_command(args, parser)
if args.command == "list-quality-criteria":
return list_quality_criteria_command(args)
if args.command == "list-legacy-auto-approvals":
return list_legacy_auto_approvals_command(args)
if args.command == "assess-dataset":
return assess_dataset_command(args)
parser.error(f"unknown command: {args.command}")
return 2
def rebuild_characteristics_command(
args: argparse.Namespace,
parser: argparse.ArgumentParser,
) -> int:
dry_run = bool(args.dry_run)
if not dry_run and args.all and not args.confirm_all:
parser.error("--all destructive rebuilds require --confirm-all")
if not dry_run and not (args.confirm or args.confirm_all):
parser.error("destructive rebuilds require --confirm or --confirm-all")
service = service_from_args(args)
repositories = selected_repositories(service, args)
if not repositories:
parser.error("no repositories matched the requested target")
for repository in repositories:
result = service.rebuild_characteristics_from_scratch(
repository.id,
dry_run=dry_run,
confirm=not dry_run,
use_llm_assistance=not args.no_llm,
)
if args.agentic_review and not dry_run and result.analysis_run.status == "completed":
service.request_agentic_review(
repository.id,
result.analysis_run.id,
notes="CLI agentic review request after rebuild.",
)
print(rebuild_summary_line(service, result, args))
return 0
def compare_assessment_command(args: argparse.Namespace) -> int:
comparison = compare_assessment_to_golden(
load_json(args.golden),
load_json(args.assessment),
)
content = (
comparison_json(comparison)
if args.format == "json"
else comparison_markdown(comparison)
)
if args.output:
write_text(args.output, content)
else:
print(content, end="" if content.endswith("\n") else "\n")
return 0
def list_quality_criteria_command(args: argparse.Namespace) -> int:
registry = load_quality_criteria(args.criteria_path)
content = (
criteria_registry_json(registry)
if args.format == "json"
else criteria_registry_markdown(registry)
)
if args.output:
write_text(args.output, content)
else:
print(content, end="" if content.endswith("\n") else "\n")
return 0
def list_legacy_auto_approvals_command(args: argparse.Namespace) -> int:
service = service_from_args(args)
records = service.list_trusted_auto_approval_migration_records()
if args.format == "json":
content = json.dumps([asdict(record) for record in records], indent=2) + "\n"
else:
content = legacy_auto_approval_records_markdown(records)
if args.output:
write_text(args.output, content)
else:
print(content, end="" if content.endswith("\n") else "\n")
return 0
def assess_dataset_command(args: argparse.Namespace) -> int:
service = service_from_args(args)
report = dataset_assessment(service)
content = (
json.dumps(report, indent=2) + "\n"
if args.format == "json"
else dataset_assessment_markdown(report)
)
if args.output:
write_text(args.output, content)
else:
print(content, end="" if content.endswith("\n") else "\n")
return 0
def dataset_assessment(service: RegistryService) -> dict[str, object]:
repositories = []
totals = {
"repositories": 0,
"facts": 0,
"content_chunks": 0,
"candidate_abilities": 0,
"candidate_capabilities": 0,
"candidate_features": 0,
"candidate_evidence": 0,
"approved_abilities": 0,
"approved_capabilities": 0,
"approved_features": 0,
"approved_evidence": 0,
"dependency_graph_nodes": 0,
"dependency_graph_edges": 0,
}
for repository in service.list_repositories():
runs = service.list_analysis_runs(repository.id)
latest_run = next((run for run in runs if run.status == "completed"), None)
facts = service.list_observed_facts(repository.id, latest_run.id) if latest_run else []
chunks = service.list_content_chunks(repository.id, latest_run.id) if latest_run else []
candidate_counts = {
"abilities": 0,
"capabilities": 0,
"features": 0,
"evidence": 0,
}
candidate_names: list[str] = []
if latest_run is not None:
try:
graph = service.candidate_graph(repository.id, latest_run.id)
except NotFoundError:
graph = None
if graph is not None:
candidate_counts = candidate_graph_counts(graph)
candidate_names = [
ability.name
for ability in graph.abilities
][:5]
ability_map = service.ability_map(repository.id)
approved_counts = approved_graph_counts(ability_map)
graph_metrics = {"node_count": 0, "edge_count": 0}
try:
dependency_graph = service.dependency_graph_elements(repository.id)
graph_metrics = {
"node_count": int(dependency_graph["metrics"]["node_count"]),
"edge_count": int(dependency_graph["metrics"]["edge_count"]),
}
except (NotFoundError, ValueError):
pass
snapshot = (
service.store.get_snapshot(latest_run.snapshot_id)
if latest_run is not None and latest_run.snapshot_id is not None
else None
)
doc_presence = document_presence(snapshot.source_path if snapshot else "")
issues = dataset_assessment_issues(
fact_count=len(facts),
chunk_count=len(chunks),
candidate_counts=candidate_counts,
approved_counts=approved_counts,
graph_metrics=graph_metrics,
doc_presence=doc_presence,
candidate_names=candidate_names,
)
repositories.append(
{
"repository_id": repository.id,
"name": repository.name,
"status": repository.status,
"latest_analysis_run_id": latest_run.id if latest_run else None,
"latest_analysis_run_status": latest_run.status if latest_run else None,
"facts": len(facts),
"content_chunks": len(chunks),
"candidate_counts": candidate_counts,
"approved_counts": approved_counts,
"dependency_graph": graph_metrics,
"documents": doc_presence,
"candidate_ability_names": candidate_names,
"issues": issues,
}
)
totals["repositories"] += 1
totals["facts"] += len(facts)
totals["content_chunks"] += len(chunks)
totals["candidate_abilities"] += candidate_counts["abilities"]
totals["candidate_capabilities"] += candidate_counts["capabilities"]
totals["candidate_features"] += candidate_counts["features"]
totals["candidate_evidence"] += candidate_counts["evidence"]
totals["approved_abilities"] += approved_counts["abilities"]
totals["approved_capabilities"] += approved_counts["capabilities"]
totals["approved_features"] += approved_counts["features"]
totals["approved_evidence"] += approved_counts["evidence"]
totals["dependency_graph_nodes"] += graph_metrics["node_count"]
totals["dependency_graph_edges"] += graph_metrics["edge_count"]
return {
"schema_version": "repo-scoping-dataset-assessment/v1",
"summary": totals,
"repositories": repositories,
}
def candidate_graph_counts(graph) -> dict[str, int]:
capabilities = [
capability
for ability in graph.abilities
for capability in ability.capabilities
]
return {
"abilities": len(graph.abilities),
"capabilities": len(capabilities),
"features": sum(len(capability.features) for capability in capabilities),
"evidence": sum(len(capability.evidence) for capability in capabilities),
}
def approved_graph_counts(ability_map) -> dict[str, int]:
capabilities = [
capability
for ability in ability_map.abilities
for capability in ability.capabilities
]
return {
"scope": 1 if ability_map.scope else 0,
"abilities": len(ability_map.abilities),
"capabilities": len(capabilities),
"features": sum(len(capability.features) for capability in capabilities),
"evidence": sum(len(capability.evidence) for capability in capabilities),
}
def document_presence(source_path: str) -> dict[str, bool]:
if not source_path:
return {
"INTENT.md": False,
"SCOPE.md": False,
"README": False,
"CLAUDE.md": False,
"AGENTS.md": False,
}
root = Path(source_path)
return {
"INTENT.md": (root / "INTENT.md").is_file(),
"SCOPE.md": (root / "SCOPE.md").is_file(),
"README": any(root.glob("README*")),
"CLAUDE.md": (root / "CLAUDE.md").is_file(),
"AGENTS.md": (root / "AGENTS.md").is_file(),
}
def dataset_assessment_issues(
*,
fact_count: int,
chunk_count: int,
candidate_counts: dict[str, int],
approved_counts: dict[str, int],
graph_metrics: dict[str, int],
doc_presence: dict[str, bool],
candidate_names: list[str],
) -> list[str]:
issues: list[str] = []
if fact_count and not candidate_counts["capabilities"]:
issues.append("facts-without-candidate-capabilities")
if chunk_count and doc_presence.get("SCOPE.md") and not candidate_counts["capabilities"]:
issues.append("scope-text-unused-for-lower-hierarchy")
if fact_count and not graph_metrics["node_count"]:
issues.append("facts-with-empty-dependency-graph")
if approved_counts["abilities"] == 0 and graph_metrics["node_count"] == 0:
issues.append("approved-hierarchy-missing-and-no-graph-fallback")
if any("repo-seed" in name.lower() for name in candidate_names):
issues.append("template-readme-contamination")
return issues
def dataset_assessment_markdown(report: dict[str, object]) -> str:
lines = ["# Repo-Scoping Dataset Assessment", ""]
summary = report["summary"]
lines.extend(
[
f"- Repositories: {summary['repositories']}",
f"- Facts: {summary['facts']}",
f"- Candidate hierarchy: {summary['candidate_abilities']} abilities / "
f"{summary['candidate_capabilities']} capabilities / "
f"{summary['candidate_features']} features / "
f"{summary['candidate_evidence']} evidence",
f"- Approved hierarchy: {summary['approved_abilities']} abilities / "
f"{summary['approved_capabilities']} capabilities / "
f"{summary['approved_features']} features / "
f"{summary['approved_evidence']} evidence",
f"- Dependency graph: {summary['dependency_graph_nodes']} nodes / "
f"{summary['dependency_graph_edges']} edges",
"",
"| Repo | Run | Facts | Chunks | Candidate | Approved | Graph | Issues |",
"| --- | ---: | ---: | ---: | --- | --- | --- | --- |",
]
)
for item in report["repositories"]:
candidate = item["candidate_counts"]
approved = item["approved_counts"]
graph = item["dependency_graph"]
lines.append(
f"| {item['name']} | {item['latest_analysis_run_id'] or '-'} | "
f"{item['facts']} | {item['content_chunks']} | "
f"{candidate['abilities']}/{candidate['capabilities']}/"
f"{candidate['features']}/{candidate['evidence']} | "
f"{approved['abilities']}/{approved['capabilities']}/"
f"{approved['features']}/{approved['evidence']} | "
f"{graph['node_count']}/{graph['edge_count']} | "
f"{', '.join(item['issues']) or '-'} |"
)
return "\n".join(lines) + "\n"
def legacy_auto_approval_records_markdown(records) -> str:
if not records:
return "No legacy trusted auto-approval records found.\n"
lines = ["# Legacy Trusted Auto-Approval Records", ""]
for record in records:
lines.extend(
[
(
f"- repo={record.repository_id}:{record.repository_name} "
f"run={record.analysis_run_id} decision={record.review_decision_id}"
),
f" status={record.analysis_run_status} scanner={record.scanner_version or 'unknown'}",
f" approved_abilities={record.current_approved_ability_count}",
f" next={record.recommended_next_step}",
]
)
return "\n".join(lines) + "\n"
def self_assess_command(
args: argparse.Namespace,
parser: argparse.ArgumentParser,
) -> int:
service = service_from_args(args)
source_path = Path(args.source_path).expanduser().resolve()
if not source_path.is_dir():
parser.error(f"source path does not exist or is not a directory: {source_path}")
repository = self_assessment_repository(service, args.repo, source_path)
summary = service.analyze_repository(
repository.id,
source_path=str(source_path),
use_llm_assistance=not args.no_llm,
agentic_review=args.agentic_review,
trusted_auto_approve=False,
)
if summary.analysis_run.status != "completed":
parser.error(summary.analysis_run.error_message or "analysis failed")
artifact = export_assessment_artifact(
service,
repository.id,
summary.analysis_run.id,
role="challenger",
outcome="challenger",
reviewer="self-assess",
)
comparison = compare_assessment_to_golden(load_json(args.golden), artifact)
if args.assessment_output:
write_text(args.assessment_output, artifact_json(artifact))
report = (
comparison_json(comparison)
if args.format == "json"
else comparison_markdown(comparison)
)
if args.comparison_output:
write_text(args.comparison_output, report)
else:
print(report, end="" if report.endswith("\n") else "\n")
if args.fail_on_regression and comparison["status"] == "regression":
return 1
return 0
def export_assessment_command(
args: argparse.Namespace,
parser: argparse.ArgumentParser,
) -> int:
service = service_from_args(args)
repositories = selected_repositories(service, args)
if not repositories:
parser.error("no repositories matched the requested target")
if len(repositories) > 1:
parser.error("assessment export requires exactly one repository")
repository = repositories[0]
try:
artifact = export_assessment_artifact(
service,
repository.id,
args.analysis_run,
role=args.role,
outcome=args.outcome,
reviewer=args.reviewer,
summary=args.summary,
)
except (NotFoundError, ValueError) as exc:
parser.error(str(exc))
content = artifact_json(artifact)
if args.output:
write_text(args.output, content)
else:
print(content, end="")
return 0
def service_from_args(args: argparse.Namespace) -> RegistryService:
settings = Settings()
database_path = Path(args.database_path or settings.database_path)
checkout_root = args.checkout_root or settings.checkout_root
database_path.parent.mkdir(parents=True, exist_ok=True)
store = RegistryStore(database_path)
store.initialize()
llm_extractor = None
no_llm = getattr(args, "no_llm", True)
if not no_llm and settings.llm_enabled and settings.llm_provider:
adapter = create_llm_connect_adapter(settings.llm_provider, model=settings.llm_model)
llm_extractor = LLMCandidateExtractor(adapter)
return RegistryService(
store,
ingestion=GitIngestionService(checkout_root),
llm_extractor=llm_extractor,
)
def selected_repositories(
service: RegistryService,
args: argparse.Namespace,
) -> list[Repository]:
repositories = service.list_repositories()
if getattr(args, "all", False):
return repositories
repo = str(args.repo)
if repo.isdigit():
try:
return [service.get_repository(int(repo))]
except NotFoundError:
return []
return [repository for repository in repositories if repository.name == repo]
def self_assessment_repository(
service: RegistryService,
repo: str,
source_path: Path,
) -> Repository:
selected = selected_repositories(service, argparse.Namespace(repo=repo, all=False))
if selected:
return selected[0]
if repo.isdigit():
raise NotFoundError(f"repository {repo} was not found")
return service.register_repository(
name=repo,
url=str(source_path),
description="Self-scoping assessment target.",
)
def write_text(path: str | Path, content: str) -> None:
target = Path(path)
target.parent.mkdir(parents=True, exist_ok=True)
target.write_text(content, encoding="utf-8")
def rebuild_summary_line(
service: RegistryService,
result: CharacteristicRebuildResult,
args: argparse.Namespace,
) -> str:
graph = (
service.candidate_graph(result.repository.id, result.analysis_run.id)
if result.analysis_run.status == "completed"
else None
)
remaining_review = 0
if graph is not None:
remaining_review = sum(
1
for ability in graph.abilities
for capability in ability.capabilities
if capability.status == "candidate"
)
candidate_source = "deterministic" if args.no_llm else "configured"
return (
f"repo={result.repository.id}:{result.repository.name} "
f"latest_analysis_run={result.analysis_run.id} "
f"candidate_source={candidate_source} "
f"dry_run={result.dry_run} "
f"cleared_approved={result.cleared_approved} "
f"approved_superseded={result.previous_counts} "
f"candidates={result.candidate_counts} "
f"remaining_review_queue={remaining_review}"
)
if __name__ == "__main__":
raise SystemExit(main())