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Project AGENTS.md

Scope

These instructions apply to the whole repository.

Canonical Surfaces

The self-contained skill/ package is the canonical AI-agent bundle.

Within that bundle:

  1. skill/SKILL.md is the human and agent entrypoint and carries the inline machine-readable skill metadata.
  2. skill/SKILL.md and the bundled markdown references are the canonical execution surface for the published skill.

Keep the canonical skill slug stable as spot-advanced-swap-orders.

Use Spot Advanced Swap Orders as the human-facing display title for the skill and hosted distribution surfaces.

The repository README.md is the exception and may use broader protocol-level branding.

Keep skill/SKILL.md and config.json as the sync inputs for the inline metadata consumed by the self-contained skill package.

Optimize retrieval with frontmatter description and opening text before changing the skill slug again.

Sync Rules

When changing behavior, docs, packaging, or metadata that affects agent consumption, update all affected surfaces in the same change.

This commonly includes skill/SKILL.md, config.json, skill/, README.md, index.html, package.json, and derived metadata.

The canonical skill npm package name is @orbs-network/spot-skill.

Build Requirement

Run npm run build after every change made in the repo.

Treat that build as the normal sync boundary for derived skill metadata.

QA Workflow

When the user asks for qa:

  1. Run ./script/qa-security.zsh and format the result with emojis.
  2. Include the qa-security result as a single emoji-prefixed line in the final report whenever qa-security ran during the task, with verdict, confidence, and summary.
  3. Treat qa as a local dev task that validates full skill inference from the bundled skill docs.
  4. The default qa flow is two sequential TWAP orders, not one mixed order or a single-shot market order.
  5. Unless the user overrides scope or shape, place a first order that is a 2-chunk stop-loss from wrapped native to USDC, wait for that order to reach a final state, then place a second order that is a 2-chunk take-profit from USDC back to native.
  6. For each default order, size input.maxAmount to about $10 of that leg's input token, use exactly 2 equal chunks so input.amount = input.maxAmount / 2 is about $5 per chunk, and set epoch = 60.
  7. For the default stop-loss leg, set output.triggerLower to effectively infinite output-token units so the order is immediately eligible for QA, and set output.triggerUpper = 0.
  8. For the default take-profit leg, set output.triggerUpper = 1 wei so the order is immediately eligible for QA, and set output.triggerLower = 0.
  9. Unless the user overrides tokens, use wrapped native input and USDC output on the first order, then USDC input and native output on the second order, on each supported chain.
  10. Use the $chain skill and its environment for local EVM context, signer-managed Foundry execution, address resolution, balances, token metadata, wrapping, approvals, and transaction sending.
  11. Do not use helper surfaces unless the user explicitly asks to test those surfaces.
  12. If the skill bundle is insufficient, report the gap instead of falling back silently.
  13. Do not query, reference, or use any orders from before this run as examples for any purpose.
  14. Honor user scope modifiers such as just ethereum; otherwise run on all supported chains in parallel.
  15. Do not probe a chain first; run the supported-chain set in parallel once.
  16. For prerequisite onchain transactions such as wrap or approve, fan out across chains with parallel.
  17. In qa, when approval is needed, always use a standing max approval such as approve(..., maxUint256) rather than an exact input.maxAmount approval.
  18. In qa, do not send approval-reset or zero-allowance cleanup transactions before, between, or after the default order legs unless the user explicitly asks for them.
  19. Do not use cast send --async in qa; each branch should surface the tx hash and final receipt directly so retries remain unambiguous.
  20. Do not use zsh arithmetic for wei or token-amount sizing in qa.
  21. Use a safer exact tool such as bc or cast for amount math.
  22. Execute the intended two-order flow, poll every 5 seconds until each order reaches a final state.
  23. Report a table with the run summary, choices, skill files, sufficiency, ambiguity, any retries or inline fixes or double takes taken, and final order states.
  24. A qa run passes only if both requested E2E orders complete and you can explain decisions from the active QA surface without unreported fallback.