Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Jamkris/everything-gemini-code --skill content-hash-cache-patterngit clone --depth 1 https://github.com/Jamkris/everything-gemini-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jamkris/everything-gemini-code/content-hash-cache-pattern)<a href="https://agentmods.dev/skills/jamkris/everything-gemini-code/content-hash-cache-pattern"><img src="https://agentmods.dev/badge/skills/jamkris/everything-gemini-code/content-hash-cache-pattern/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jamkris/everything-gemini-code/content-hash-cache-pattern"><img src="https://agentmods.dev/badge/skills/jamkris/everything-gemini-code/content-hash-cache-pattern.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.01281 |
| Opus 5 | $0.00015 | $0.00641 |
| Sonnet 5 | $0.00006 | $0.00256 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
content-hash-cache-pattern scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to content-hash-cache-pattern — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 Use
- 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:
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
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.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 162 lines · 30 tokens per session scan A 134309146bfd
content-hash-cache-pattern is a skill published in the GitHub repository Jamkris/everything-gemini-code (87 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 1,281 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to content-hash-cache-pattern, differing in 6 lines, and is treated as a copy.
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