Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/affaan-m/ecc --skill content-hash-cache-patternAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
Content-Hash File Cache Pattern
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.
When to Activate
- Building file processing pipelines (PDF, images, text extraction)
- Processing cost is high and same files are processed repeatedly
- Need a
--cache/--no-cacheCLI option - Want to add caching to existing pure functions without modifying them
Core Pattern
1. Content-Hash Based Cache Key
Use file content (not path) as the cache key:
python
import hashlib
from pathlib import Path
_HASH_CHUNK_SIZE = 65536 # 64KB chunks for large files
def compute_file_hash(path: Path) -> str:
"""SHA-256 of file contents (chunked for large files)."""
if not path.is_file():
raise FileNotFoundError(f"File not found: {path}")
sha256 = hashlib.sha256()
with open(path, "rb") as f:
while True:
chunk = f.read(_HASH_CHUNK_SIZE)
if not chunk:
break
sha256.update(chunk)
return sha256.hexdigest()Why content hash? File rename/move = cache hit. Content change = automatic invalidation. No index file needed.
2. Frozen Dataclass for Cache Entry
python
from dataclasses import dataclass
@dataclass(frozen=True, slots=True)
class CacheEntry:
file_hash: str
source_path: str
document: ExtractedDocument # The cached result3. File-Based Cache Storage
Each cache entry is stored as {hash}.json — O(1) lookup by hash, no index file required.
python
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 miss4. Service Layer Wrapper (SRP)
Keep the processing function pure. Add caching as a separate service layer.
python
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 docKey Design Decisions
| 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 |
Best Practices
- Hash content, not paths — paths change, content identity doesn't
- Chunk large files when hashing — avoid loading entire files into memory
- Keep processing functions pure — they should know nothing about caching
- Log cache hit/miss with truncated hashes for debugging
- Handle corruption gracefully — treat invalid cache entries as misses, never crash
Anti-Patterns to Avoid
python
# 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 insteadWhen to Use
- File processing pipelines (PDF parsing, OCR, text extraction, image analysis)
- CLI tools that benefit from
--cache/--no-cacheoptions - Batch processing where the same files appear across runs
- Adding caching to existing pure functions without modifying them
When NOT to Use
- Data that must always be fresh (real-time feeds)
- Cache entries that would be extremely large (consider streaming instead)
- Results that depend on parameters beyond file content (e.g., different extraction configs)
