mempalace.project_scanner

Source: mempalace/project_scanner.py

project_scanner.py — Detect projects and people from real signal.

For a codebase with build manifests or git history, this beats regex-based entity detection by a wide margin: the project's own name is already written down in package.json / pyproject.toml / Cargo.toml / go.mod / pom.xml / Gradle manifests, and the people who worked on it are in git log.

This module is used as the primary signal in mempalace init. The regex detector in entity_detector.py stays as a fallback for prose-only folders (notes, research, writing).

Public: scan(root) -> (projects, people) to_detected_dict(projects, people) -> {people: [...], projects: [...], uncertain: []}

Classes

class ProjectInfo

confidence

def confidence(self) -> float

to_signal

def to_signal(self) -> str

class PersonInfo

confidence

def confidence(self) -> float

to_signal

def to_signal(self) -> str

Functions

find_git_repos

def find_git_repos(root: Path, max_depth: int = MAX_DEPTH) -> list[Path]

Return git repo roots under root (including root itself if it's a repo).

scan

def scan(root: str | os.PathLike) -> tuple[list[ProjectInfo], list[PersonInfo]]

Scan root for projects and people. Returns (projects, people) sorted.

to_detected_dict

def to_detected_dict(projects: list[ProjectInfo], people: list[PersonInfo], project_cap: int = 15, people_cap: int = 15) -> dict

Convert scan results into the dict shape produced by entity_detector.detect_entities.

discover_entities

def discover_entities(project_dir: str | os.PathLike, languages: tuple = ('en',), prose_file_cap: int = 10, project_cap: int = 15, people_cap: int = 15, llm_provider: object = None, show_progress: bool = True, corpus_origin: dict | None = None) -> dict

Top-level entity discovery: real signals first, prose detection second.

Returns the same dict shape as entity_detector.detect_entities so it plugs into confirm_entities unchanged.

Order of signal preference:

1. Package manifests (package.json, pyproject.toml, Cargo.toml, go.mod, pom.xml, Gradle manifests) → canonical project names
2. Git commit authors → real people with real commit counts
3. Claude Code conversation dirs (~/.claude/projects/) → per-session project names (pulled from each session's cwd metadata)
4. Regex entity detection on prose files → supplementary names only mentioned in docs/notes (not code)
5. Optional LLM refinement pass — reclassifies ambiguous candidates using the caller-supplied provider
6. Optional corpus-origin persona filter — when the corpus is identified as AI-dialogue, candidates whose name matches an agent_persona_name are moved to an agent_personas bucket instead of being reported as people.

Passing llm_provider enables phase-2 refinement. The caller is responsible for constructing the provider (llm_client.get_provider) and confirming availability. Refinement is blocking-interactive: progress prints to stderr; Ctrl-C returns partial results.

Passing corpus_origin enables corpus-origin persona reclassification. The expected shape is the dict written by mempalace init to <palace>/.mempalace/origin.json (see corpus_origin.py).