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 ils15/pantheon-legacy --skill cache-strategygit clone --depth 1 https://github.com/ils15/pantheon-legacyWrote 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/ils15/pantheon-legacy/cache-strategy)<a href="https://agentmods.dev/skills/ils15/pantheon-legacy/cache-strategy"><img src="https://agentmods.dev/badge/skills/ils15/pantheon-legacy/cache-strategy/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/ils15/pantheon-legacy/cache-strategy"><img src="https://agentmods.dev/badge/skills/ils15/pantheon-legacy/cache-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00025 | $0.01843 |
| Opus 5.5 | $0.00010 | $0.00737 |
| Sonnet 5.5 | $0.00005 | $0.00369 |
| Haiku 4.5 | $0.00003 | $0.00184 |
Grade A, and why
cache-strategy 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cache Strategy — Architecture Patterns
Use this skill for cache architecture decisions. Covers Redis patterns, CDN strategies, TTL management, cache invalidation, and session stores. Used by Demeter during schema design and Hermes during implementation.
Cache Patterns
1. Read-Through Cache
The cache sits between the application and the database. On a cache miss, the cache loads the data from the database.
import redis
import json
from typing import Optional
class ReadThroughCache:
def __init__(self, redis_client: redis.Redis, ttl: int = 300):
self.redis = redis_client
self.ttl = ttl
async def get(self, key: str, loader) -> Optional[dict]:
"""Get from cache. If miss, load from source and cache."""
cached = await self.redis.get(key)
if cached:
return json.loads(cached)
# Cache miss — load from source
data = await loader()
if data:
await self.redis.setex(key, self.ttl, json.dumps(data))
return data
When to use: Read-heavy workloads (products, reviews, user profiles)
2. Write-Through Cache
Data is written to both the cache and the database simultaneously.
class WriteThroughCache:
async def set(self, key: str, data: dict, saver):
"""Write to cache and database simultaneously."""
# Write to database first
await saver(data)
# Then update cache
await self.redis.setex(key, self.ttl, json.dumps(data))
async def delete(self, key: str, deleter):
"""Delete from both cache and database."""
await deleter()
await self.redis.delete(key)
When to use: Data that must be consistent (user accounts, orders)
3. Write-Behind (Write-Back) Cache
Data is written to the cache first, then asynchronously flushed to the database.
import asyncio
from collections import OrderedDict
class WriteBehindCache:
def __init__(self, flush_interval: int = 5):
self.write_queue = OrderedDict()
self.flush_interval = flush_interval
async def set(self, key: str, data: dict):
"""Write to cache only. Flush to DB asynchronously."""
self.write_queue[key] = data
if len(self.write_queue) >= 100:
await self._flush()
async def _flush(self):
"""Flush all pending writes to database."""
batch = dict(self.write_queue)
self.write_queue.clear()
# Batch write to database
await self._batch_save(batch)
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.
- 1mo ago First seen · 274 lines · 25 tokens per session scan A b8afc15814ee
cache-strategy is a skill published in the GitHub repository ils15/pantheon-legacy (12 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 1,843 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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