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O-ALL-WANT
lihowfun
A practical framework that integrates proven GitHub patterns for agent memory, skills, and reusable context.
Open lihowfun/O-ALL-WANT → Manually confirmed as a workspace.
Built for one agent
ships CLAUDE.md and nothing for other agents.
In its own words
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Why choose when you can have it all? Kids choose. Builders want the whole stack.
20-second pitch
OAW installs a small, local harness into your repo:
- Context router:
CLAUDE.mdtells the agent which lane to read instead of loading every doc. - Durable memory:
.agents/memory.mdcarries decisions, bugs, and findings across sessions. - Compiled wiki:
docs/raw/notes become searchabledocs/knowledge/pages. - Repeatable skills:
.agents/skills/keeps recurring workflows from becoming random prompt improv.
Measured result: lane routing loads ~3k tokens per task lane instead of ~22.6k for the whole project — 86-87% less startup context.
Read
CLAUDE.mdfirst, thenAI_CONTEXT.md. I'm building [describe your project]. Use OAW's architecture as the harness, map this project's real state intoAI_CONTEXT.md, then tell me which repeated workflows belong in.agents/skills/.
Why are you here?
This is a harness for unapologetically greedy agentic coders.
If you hop between AI coding platforms and treat token efficiency like a competitive sport, you probably know the heartbreak: the chat gets long, the agent gets stupid, the session resets, and suddenly you are paying premium tokens just to re-explain the repo before any real work happens. You do not even reach the hard part before seeing You have hit your limit.
OAW is my answer to that waste.
Quoting 17% of the README. Read the rest on GitHub ↗
Read from the repository's git tree on 2026-08-17.