vitya
ca669d96e1
Copies source (no node_modules, dist, .tasks, .wiki, __pycache__) for: - projects-meta-mcp v2.25.0 (TypeScript/Node) - wiki-graph v0.3.1 (TypeScript/Node) - interns-mcp v0.3.3 (Python/FastMCP) .gitignore: exclude lib build artefacts (node_modules, dist, .venv, __pycache__, *.pyc) bootstrap.ps1: add MCP build step — npm install+build for TS servers, venv+pip for Python Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
"""Grep audit intern — deterministic matrix of N paths x M patterns. No LLM call."""
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from __future__ import annotations
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import json
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import re
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from pathlib import Path
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from typing import Any, Callable, Literal
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from .base import Intern, InternResponse
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class GrepAudit(Intern):
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id = "grep_audit"
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description = (
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"Deterministic grep matrix over N paths x M patterns (substring or regex). "
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"No LLM call, no endpoint cost. Returns markdown table (default) or JSON."
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)
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def run(
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self,
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paths: list[str],
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patterns: list[str | dict[str, Any]],
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output: Literal["table", "json"] = "table",
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case_sensitive: bool = True,
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**_kwargs: Any,
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) -> InternResponse:
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compiled = _compile_patterns(patterns, case_sensitive)
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rows: list[dict[str, Any]] = []
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matches_total = 0
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for p in paths:
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try:
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text = Path(p).read_text(encoding="utf-8", errors="replace")
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except (FileNotFoundError, PermissionError, IsADirectoryError, OSError) as exc:
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rows.append(
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{
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"path": p,
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"matches": {c["name"]: None for c in compiled},
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"error": str(exc),
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}
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)
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continue
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row_matches: dict[str, bool] = {}
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for c in compiled:
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hit = bool(c["matcher"](text))
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row_matches[c["name"]] = hit
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if hit:
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matches_total += 1
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rows.append({"path": p, "matches": row_matches})
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if output == "json":
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rendered = json.dumps({"rows": rows}, ensure_ascii=False, indent=2)
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else:
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rendered = _render_table(rows, [c["name"] for c in compiled])
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return InternResponse(
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text=rendered,
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usage={
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"files_scanned": len(paths),
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"patterns_evaluated": len(compiled),
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"matches_total": matches_total,
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},
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)
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def _compile_patterns(
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patterns: list[str | dict[str, Any]], case_sensitive: bool
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) -> list[dict[str, Any]]:
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out: list[dict[str, Any]] = []
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for p in patterns:
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if isinstance(p, str):
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out.append({"name": p, "matcher": _substring_matcher(p, case_sensitive)})
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continue
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name = p.get("name", p["pattern"])
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if p.get("regex"):
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flags = 0 if case_sensitive else re.IGNORECASE
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compiled = re.compile(p["pattern"], flags)
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out.append({"name": name, "matcher": compiled.search})
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else:
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out.append(
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{"name": name, "matcher": _substring_matcher(p["pattern"], case_sensitive)}
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)
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return out
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def _substring_matcher(needle: str, case_sensitive: bool) -> Callable[[str], bool]:
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if case_sensitive:
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return lambda text: needle in text
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lowered = needle.lower()
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return lambda text: lowered in text.lower()
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def _render_table(rows: list[dict[str, Any]], names: list[str]) -> str:
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header = "| Path | " + " | ".join(names) + " |"
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sep = "|" + "---|" * (len(names) + 1)
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lines = [header, sep]
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for row in rows:
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cells: list[str] = []
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for n in names:
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v = row["matches"].get(n)
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cells.append("⚠️" if v is None else ("✅" if v else "❌"))
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lines.append(f"| `{row['path']}` | " + " | ".join(cells) + " |")
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return "\n".join(lines)
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