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 skills/iceymoss/go-hichat-api/specnpx skills add iceymoss/go-hichat-api --skill specgit clone --depth 1 https://github.com/iceymoss/go-hichat-apiWrote 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/iceymoss/go-hichat-api/spec)<a href="https://agentmods.dev/skills/iceymoss/go-hichat-api/spec"><img src="https://agentmods.dev/badge/skills/iceymoss/go-hichat-api/spec.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 | $0.00035 | $0.00577 |
| Opus 5 | $0.00017 | $0.00289 |
| Sonnet 5 | $0.00007 | $0.00115 |
| Haiku 4.5 | $0.00003 | $0.00058 |
Grade A, and why
spec 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
生成功能需求规范文档。
输入
用户用一句话描述想做什么（可以很粗糙）。
步骤
1. 需求访谈（必须做）
用 AskUserQuestion 逐个确认：
- 用户故事：谁在什么场景下做什么操作，期望什么结果？
- 触发方式：页面入口、API 调用、定时任务、webhook？
- 核心流程：happy path 一步步描述
- 异常场景：失败、超时、重复操作、并发？
- 数据：新增或修改哪些数据？输入输出？
- 边界：明确不做什么
- 优先级：MVP 包含哪些？哪些后续迭代？
每个问题等用户回答后再问下一个。"不确定"的标注为待定。
2. 代码调研（自动执行）
用 subagent 调研，不要让用户操心：
- 已有的相似功能实现模式
- 相关数据模型结构
- 路由注册方式
- 需要复用的现有代码
3. 生成 Spec
写入 docs/specs/{功能名}.md：
# {功能名}
## 状态
- 创建日期: {日期}
- 状态: 草稿
## 目标
（一句话说明要解决什么问题）
## 非目标
- ...
## 用户故事
作为 {角色}，我想要 {操作}，以便 {价值}
## 核心流程
1. ...
## 异常处理
| 场景 | 处理方式 |
|------|---------|
## 技术设计
### 数据模型
（表名、字段、类型）
### API 接口
| 方法 | 路径 | 说明 |
### 实现步骤（每步可独立 commit）
1. [ ] 数据模型
2. [ ] 控制器 + 路由
3. [ ] 前端页面
### 参考的现有模式
- {文件路径} — 参考了什么
## 测试计划
- [ ] ...
## 待定事项
- ...
## MVP 范围
4. 让用户审阅
生成后告诉用户：
- 请审阅 specs/{功能名}.md
- 确认 MVP 范围和待定事项
- 确认后用 /tdd 逐步开发
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 · 94 lines · 35 tokens per session scan A 079e0b2607cb
spec is a skill published in the GitHub repository iceymoss/go-hichat-api (41 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 577 once invoked, about $0.0002 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…