← Skills
THE IDEA
Teaches an AI to write Arkiv data cheaply — batch with mutateEntities, size payloads and attributes, tune TTL, update vs recreate.
The problem today
- Looping single writes collides on the nonce and costs far more than batching — the obvious first instinct.
- Payload size, attribute count, and TTL all drive cost, and none of it is obvious from the API.
- Expiry reads as a limitation unless you know to use it as a cost feature.
What it would do
- Batch everything: mutateEntities (up to 1000 ops/tx) instead of loops.
- Right-size payloads and attributes; keep only what you query as an attribute.
- Pick TTL deliberately and extend instead of recreating when cheaper.
WHY IT’S A BETTER EXPERIENCE
- Frames expiry as cost-efficiency (the on-message framing), not a missing "store forever".
- Cuts real GLM spend on any write-heavy workload.
FEASIBILITY — AND WHY THIS ISN’T HAND-WAVING
Medium to build
Knowledge skill grounded in measured testnet costs. Largely covered today by write-safety + expiration-design; full value at mainnet pricing.
Grounded in
- mutateEntities batching and the nonce-collision pitfall are confirmed from the MCP guidance + SDK.
- Per-write cost is measurable on Braga (verified via the live probe).
- Deferred per second-model audit: thin until pricing is real; parts already live in existing skills.
PAIRS WITH