feat(skills): add content-hash-cache-pattern skill

SHA-256 content-hash based file caching with service layer
separation for expensive processing pipelines.

skills/content-hash-cache-pattern/SKILL.md

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+---
+name: content-hash-cache-pattern
+description: Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.
+---
+
+# 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.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
+```
+
+### 4. 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 doc
+```
+
+## Key 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 instead
+```
+
+## When to Use
+
+- File processing pipelines (PDF parsing, OCR, text extraction, image analysis)
+- CLI tools that benefit from `--cache/--no-cache` options
+- 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)