content-hash-cache-pattern
OfficialCache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.
What this skill does
When applied, it prepends a system prompt before your request is sent — no extra calls and no change to how you are billed beyond the added tokens.
---
name: content-hash-cache-pattern
description: Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.
origin: ECC
---
# 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-cache` CLI 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 result
```
### 3. 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.mkdiUse this skill
Add a "skill" field with the skill’s ID to your chat completion request. It is applied server-side before your prompt is sent — no extra calls.
{
"model": "gpt-4o-mini",
"skill": "imp-82c22553-4447-4983-8382-a305f83e5249",
"messages": [{ "role": "user", "content": "…" }]
}Install the skill, enable it in your dashboard and (optionally) limit it to specific models. It then applies automatically to every matching request — with no "skill" field to send each time.
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