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.
npx agentmods add commands/bladeofgod/flutter-ai-harness/plan-figmagit clone --depth 1 https://github.com/bladeofgod/flutter-ai-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/bladeofgod/flutter-ai-harness/plan-figma)<a href="https://agentmods.dev/commands/bladeofgod/flutter-ai-harness/plan-figma"><img src="https://agentmods.dev/badge/commands/bladeofgod/flutter-ai-harness/plan-figma.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.00559 |
| Opus 5 | $0.00010 | $0.00280 |
| Sonnet 5 | $0.00004 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
plan-figma 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 5d ago.
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.
What it actually says
把 Figma 设计转换为结构化规划输入，并按仓库任务卡约定形成可执行计划。不实现代码，也不修改设计稿。本命令不是 Figma 任务卡的唯一生产方式。
设计输入标准化
- 确认至少提供一个 Figma URL 或 node-id。
- 加载
figma-to-flutter和flutter-layoutsSkill。 - 读取
CLAUDE.md、docs/architecture.md、设计 Token、现有组件、Route、API 和附近测试。 - 使用项目
.mcp.json中的figma本地 MCP 读取每个节点；连接不可用时报告 Figma Desktop、Dev Mode 或 Server 前置条件并停止。 - 提取层级、Auto Layout、尺寸、间距、字体、颜色、组件 Variant、交互、资源和响应式行为。
- 新增视觉值前，先反查已有代码 Token 和组件。
- 补齐设计稿无法表达的工程维度：数据归属、状态、导航、加载/错误/空状态、无障碍、埋点、持久化和平台行为。
- 把设计事实、工程推断、待决问题、节点来源、资源与授权信息整理为
design-context.md的内容；此阶段不创建任务卡。
统一规划
- 遵守
CLAUDE.md的任务卡生命周期，使用architect确定架构、依赖和任务边界。 - 将标准化设计内容写入唯一且能概括范围的
docs/figma/<context-slug>-design-context.md。 - 任务卡直接写入
docs/tasks/，并显式引用设计输入文件；不得在docs/tasks/下创建 Figma 快照或其他子目录，也不得另行定义任务卡格式。 - 每张卡必须引用准确的 Figma 节点、设计输入文档和相关代码路径，并区分设计事实与工程推断。
存在设计 Token 时不得写无解释的裸视觉值。资源授权和导出参数未确认前，不得生成正式 Asset。
创建全部产物后运行 make harness-check。最后汇报任务顺序、设计缺口、Token/组件新增项和待决问题。停在实现前等待用户 Review。
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.
- 5d ago First seen · 29 lines · 19 tokens per session scan A 95eb9ec4f249
plan-figma is a command published in the GitHub repository bladeofgod/flutter-ai-harness (113 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 559 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.