whiteboard AGENTS.md

whiteboard AGENTS.md is an instructions file for Codex, OpenCode from kamiazya/whiteboard. It costs 4,038 tokens per session, scanned A, original, Apache-2.0.

Project instructions for Whiteboard, a software project, that define how developers should choose tests and develop changes. They require test-driven development (TDD), meaning writing a failing test before the code that makes it pass.

In plain words
What is it for?
Use them when changing Whiteboard code to select the right test environment, write the red test first, make a small fix, and preserve verified browser behavior with end-to-end tests.
Why use it?
They provide a consistent way to test features and bugs at the layer where problems occur, then verify the result in a real browser or app.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/kamiazya/whiteboard/agents-md
Clone the repo
git clone --depth 1 https://github.com/kamiazya/whiteboard

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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README.md
[![agentmods](https://agentmods.dev/badge/instructions/kamiazya/whiteboard/agents-md.svg)](https://agentmods.dev/instructions/kamiazya/whiteboard/agents-md)
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Per session 4,038 This file is loaded in full into every session.
When invoked 4,038 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.04038 $0.04038
Opus 5 $0.02019 $0.02019
Sonnet 5 $0.00808 $0.00808
Haiku 4.5 $0.00404 $0.00404

Measured today against content hash 312781727a29, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

whiteboard AGENTS.md 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 today.

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.

AGENTS.md · 285 lines

How it starts

The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Project Instructions

Use this repo's standard development loop for every feature, bug fix, or refactor:

  1. Start with the smallest failing test at the nearest layer.
  2. Make the smallest patch that turns it green.
  3. Manually verify the real behavior in a running app or browser.
  4. Lock the verified user flow into canvas-viewer-browser/web-browser or a broader E2E test when browser-mode is not enough.

Test Layer Selection

Start with the smallest failing test at the nearest layer, and do not jump to broad E2E when a smaller failing test can isolate the bug. Reach for a property or model-based test (fast-check) over an example-only one when the change touches a parser/serializer, a state machine with time/TTL/revocation semantics, a concurrent store, a CRDT or other mergeable structure, or a rounding/normalization transform with an algebraic invariant. Prefer example and browser tests for UI wiring, one-off integrations, and anything with no clean invariant to state.

Everything that follows from that choice is the test-layer-selection skill: which project serves which layer and the command for each, the per-layer numRuns budget, and the property disciplines — mutation-check every new property, answer a vacuous one with a denser generator rather than more runs, never pin a seed, and never build an oracle out of the code it is judging. Coverage ledgers are .claude/rules/coverage-ledger.md, which is path-scoped and loads itself.

Required Workflow

1. Write the red test first

  • Reproduce the bug or target behavior before changing production code.
  • Keep the first failing case as small and local as possible.

2. Turn it green locally

Run the narrowest project first:

pnpm test --project mcp-node
pnpm test --project canvas-viewer-jsdom
pnpm test --project canvas-viewer-browser

After the targeted test passes, run the broader suite that covers the touched area.

3. Manually verify the real behavior

  • For UI work, open the real screen and confirm the behavior directly.
  • Prefer Chrome DevTools MCP or Playwright MCP when available to inspect DOM, console, network, and visible behavior.
  • If the changed flow is represented by a project skill under ./skills/*, read the relevant SKILL.md and dogfood the real MCP/skill flow instead of verifying only through mocks.
  • While dogfooding, collect friction, awkward prompts, missing affordances, and ideas for follow-up improvements.
  • Record every still-open dogfooding finding as a whiteboard document issue (see the ticketing skill). When it is fixed, change it to type: note with a RESOLVED — name and say what fixed it — do not delete it. What the document accumulated is the measurements, and those are what stop it being investigated again.
  • If runtime behavior disagrees with the test, treat runtime as the source of truth and fix the test or implementation.

Read the full file on GitHub · 285 lines

Changes

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.

  1. today Changed · +1 lines · +29 tokens per session 312781727a29
  2. yesterday Changed · -166 lines · -2,371 tokens per session 3cb00c63c9a3
  3. 5d ago First seen · 450 lines · 6,380 tokens per session scan A 757434374135

Subscribe to this mod's changes

whiteboard AGENTS.md is an instructions file published in the GitHub repository kamiazya/whiteboard (6 stars, last pushed today), licensed Apache-2.0. It adds 4,038 tokens to every session, about $0.0202 per session on Opus 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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