BuildAll

About

The problem it solves ## The problem this solves Crypto execution is still fragmented and high-friction: - Every chain has different conventions, tooling, and protocol surfaces - Most users still need to manage wallet complexity directly - Agent behavior is often inconsistent because docs are scattered and non-standard BuildAll solves this by providing a **seamless, standardized skill layer** where agents can: - read one index and follow predictable command contracts - safely simulate before write actions - use deterministic playbooks for common tasks - bridge across ecosystems with preflight + validation guardrails In practice, this makes onchain tasks **faster, safer, and easier to automate** through chat. ### Product theory This is designed as an agent primitive: if a blockchain has a `SKILL.md`, an agent can learn it and act. Current phase: - optimized for crypto-native users who already operate wallets Future phase: - wallet requirements can be abstracted behind `walletskills.md`-style flows so non-crypto-native users can complete blockchain tasks from chat without deep wallet/tooling knowledge. The goal is a high-quality UX: **trust-aware, chat-native, multi-chain execution**. Use of AI tools and agents ## Use of AI tools and agents This system is intended for **any OpenClaw agent**. Design principles: - no required package download for consumption of skills docs - hosted, referenceable markdown endpoints - trust-minimized behavior via explicit validation/simulation policies How agents work together: 1. Agent loads `onboarding/SKILL.md` first 2. Agent collects missing user execution data 3. Agent follows capability-specific `SKILL.md` + `playbooks/SKILL.md` 4. Agent uses preflight/validation tools for cross-chain operations 5. Agent executes only after simulation + explicit user confirmation

How it's made

Challenges we ran into ## Challenges encountered ### 1) Local OpenClaw setup + tool wiring Getting OpenClaw fully usable locally required coordination across environment setup, CLI/tool auth, and runtime config consistency. How this was addressed: - standardized `.env.example` and per-chain config keys - validated each change with typecheck/test/build loops - pushed changes incrementally to keep deployment/debugging tight ### 2) Skill format design for agent readability A major hurdle was making docs both human-readable and machine-actionable. How this was addressed: - adopted a strict `SKILL.md` structure per capability - added onboarding-first policy and deterministic preflight flows - created intent → command playbooks so agents don’t improvise unsafe paths ### 3) Multi-chain safety and bridge reliability Bridge routes and cross-chain execution are error-prone without verified targets. How this was addressed: - added verified bridge registry files - added runtime validation commands (`bridge.validateAddress`) - added deterministic planning (`bridge.preflight`) before execution