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Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/loulanyue/awesome-claude-notes --skill content-hash-cache-patternコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
SKILL.md を表示中
SOC 職業分類に基づく
| name | content-hash-cache-pattern |
| description | Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation. |
| source_path | skills/content-hash-cache-pattern/SKILL.md |
| origin | ECC |
Cache expensive file processing results (PDF parsing, text extraction, image analysis) using SHA-256 content hashes as cache keys. Unlike path-based caching, this approach survives file moves/renames and auto-invalidates when content changes.
--cache/--no-cache CLI optionUse file content (not path) as the cache key:
import hashlib
from pathlib import Path
_HASH_CHUNK_SIZE = 65536 # 64KB chunks for large files
def compute_file_hash(path: Path) -> str:
path.is_file():
FileNotFoundError()
sha256 = hashlib.sha256()
(path, ) f:
:
chunk = f.read(_HASH_CHUNK_SIZE)
chunk:
sha256.update(chunk)
sha256.hexdigest()
Why content hash? File rename/move = cache hit. Content change = automatic invalidation. No index file needed.
from dataclasses import dataclass
@dataclass(frozen=True, slots=True)
class CacheEntry:
file_hash: str
source_path: str
document: ExtractedDocument # The cached result
Each cache entry is stored as {hash}.json — O(1) lookup by hash, no index file required.
import json
from typing import Any
def write_cache(cache_dir: Path, entry: CacheEntry) -> None:
cache_dir.mkdir(parents=True, exist_ok=True)
cache_file = cache_dir / f"{entry.file_hash}.json"
data = serialize_entry(entry)
cache_file.write_text(json.dumps(data, ensure_ascii=False), encoding="utf-8")
def read_cache(cache_dir: Path, file_hash: str) -> CacheEntry | None:
cache_file = cache_dir / f"{file_hash}.json"
if not cache_file.is_file():
return None
try:
raw = cache_file.read_text(encoding="utf-8")
data = json.loads(raw)
return deserialize_entry(data)
except (json.JSONDecodeError, ValueError, KeyError):
return None # Treat corruption as cache miss
Keep the processing function pure. Add caching as a separate service layer.
def extract_with_cache(
file_path: Path,
*,
cache_enabled: bool = True,
cache_dir: Path = Path(".cache"),
) -> ExtractedDocument:
"""Service layer: cache check -> extraction -> cache write."""
if not cache_enabled:
return extract_text(file_path) # Pure function, no cache knowledge
file_hash = compute_file_hash(file_path)
# Check cache
cached = read_cache(cache_dir, file_hash)
if cached is not None:
logger.info("Cache hit: %s (hash=%s)", file_path.name, file_hash[:12])
return cached.document
# Cache miss -> extract -> store
logger.info("Cache miss: %s (hash=%s)", file_path.name, file_hash[:12])
doc = extract_text(file_path)
entry = CacheEntry(file_hash=file_hash, source_path=str(file_path), document=doc)
write_cache(cache_dir, entry)
return doc
| Decision | Rationale |
|---|---|
| SHA-256 content hash | Path-independent, auto-invalidates on content change |
{hash}.json file naming | O(1) lookup, no index file needed |
| Service layer wrapper | SRP: extraction stays pure, cache is a separate concern |
| Manual JSON serialization | Full control over frozen dataclass serialization |
Corruption returns None | Graceful degradation, re-processes on next run |
cache_dir.mkdir(parents=True) | Lazy directory creation on first write |
# BAD: Path-based caching (breaks on file move/rename)
cache = {"/path/to/file.pdf": result}
# BAD: Adding cache logic inside the processing function (SRP violation)
def extract_text(path, *, cache_enabled=False, cache_dir=None):
if cache_enabled: # Now this function has two responsibilities
...
# BAD: Using dataclasses.asdict() with nested frozen dataclasses
# (can cause issues with complex nested types)
data = dataclasses.asdict(entry) # Use manual serialization instead
--cache/--no-cache options