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 skills add midFang/ai-agent-skills-workflow --skill feature-preparegit clone --depth 1 https://github.com/midFang/ai-agent-skills-workflowWrote 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/skills/midfang/ai-agent-skills-workflow/feature-prepare)<a href="https://agentmods.dev/skills/midfang/ai-agent-skills-workflow/feature-prepare"><img src="https://agentmods.dev/badge/skills/midfang/ai-agent-skills-workflow/feature-prepare/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/midfang/ai-agent-skills-workflow/feature-prepare"><img src="https://agentmods.dev/badge/skills/midfang/ai-agent-skills-workflow/feature-prepare.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00084 | $0.00681 |
| Opus 5 | $0.00042 | $0.00341 |
| Sonnet 5 | $0.00017 | $0.00136 |
| Haiku 4.5 | $0.00008 | $0.00068 |
Grade A, and why
feature-prepare scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Feature Prepare
目的
把自然语言需求整理成可执行 feature 规格。初版保持轻量，不做复杂 rubric、依赖图或证据目录。
输出放在要开发的业务项目根目录：
features/<版本>/
feature_list.json
specs/
FEAT-001.md
tasks/
FEAT-001-TASK.md
输入
需要：
- 功能需求描述
- 业务项目路径
- 版本名；如果用户没给，用当前日期或一个短 slug
feature_list.json
初版字段：
{
"features": [
{
"feature_id": "FEAT-001",
"title": "笔记详情页",
"status": "ready",
"spec_file": "specs/FEAT-001.md",
"task_file": "tasks/FEAT-001-TASK.md",
"branch": "",
"worktree_path": "",
"commit": "",
"ai_verify_result": "",
"block_reason": ""
}
]
}
状态：
draft, ready, needs_info, in_progress, ai_verified, apk_built, human_verified, merged, integration_verified, worktree_removed, blocked
spec.md
每个 feature 的 spec 初版只要求三块：
# FEAT-001 <标题>
## 目标
<这个功能要解决什么>
## 主要行为
- <入口/操作/跳转/状态变化>
## 验收标准
- <什么结果算完成>
如果需求是页面开发，主要行为里可简要写页面入口、主要区块和关键交互。不要把所有视觉细节都写死，除非用户明确要求。
TASK.md
生成空执行日志：
# FEAT-001 TASK
## 当前状态
ready
## 修改记录
## AI 自证验证
- 验证计划：
- 执行命令：
- 结果：
- 证据：
- 不能验证的部分：
## 用户验证
## 阻塞/风险
## 下一步
- 使用 `$feature-worktree FEAT-001` 开始开发。
人工审核点
生成后提醒用户主要审核 spec.md：
- 目标是否正确
- 主要行为是否漏掉关键入口/交互
- 验收标准是否认可
用户不需要逐个审核 TASK，TASK 是后续执行日志。
完成回复
报告：
feature_list.json路径- spec 路径
- task 路径
- 哪些 feature 是
ready - 哪些 feature 需要补信息
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 123 lines · 84 tokens per session scan A 7b66be147331
feature-prepare is a skill published in the GitHub repository midFang/ai-agent-skills-workflow (2 stars, last pushed 2mo ago), licensed MIT. It adds 84 tokens to every session and 681 once invoked, about $0.0004 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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