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 w693847022/memory_service --skill requirement-confirmationgit clone --depth 1 https://github.com/w693847022/memory_serviceWrote 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/w693847022/memory_service/requirement-confirmation)<a href="https://agentmods.dev/skills/w693847022/memory_service/requirement-confirmation"><img src="https://agentmods.dev/badge/skills/w693847022/memory_service/requirement-confirmation/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/w693847022/memory_service/requirement-confirmation"><img src="https://agentmods.dev/badge/skills/w693847022/memory_service/requirement-confirmation.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 | $0.00030 | $0.01154 |
| Opus 5 | $0.00015 | $0.00577 |
| Sonnet 5 | $0.00006 | $0.00231 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
requirement-confirmation 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
需求澄清与确认技能
参数规范
| 参数类型 | 说明 | 执行模式 |
|---|---|---|
feature_id |
已存在的功能ID | 更新模式:读取已有需求进行澄清 |
fix_id |
已存在的修复ID | 更新模式:读取已有问题描述进行澄清 |
<需求描述> |
自然语言描述(会自动判断feature或fix) | 创建模式:新建记录并澄清需求 |
| 无参数 | - | 拒绝执行 |
注意: 整合使用时,由主技能传入 feature_id 或 fix_id
前置条件
可选前置条件 (整合模式)
- 已完成项目确认 (
project_id已知) - 已完成相关性探索 (推荐,可减少提问)
执行模式
- 整合模式: 使用主流程提供的
exploration_result和ambiguous_points - 独立模式: 自主进行相关性探索
⚠️ 重要指令
DO NOT ENTER PLAN MODE - 此技能要求直接执行,不进入计划模式
所有memory_mcp的操作使用子代理来处理,减少主窗口上下文
参数类型判断(创建模式)
当收到自然语言描述时,在开始需求澄清前需要判断是 feature 还是 fix:
-
关键词判断:
- 如果描述中包含 "bug"、"报错"、"修复"、"出错"、"问题"、"缺陷" 等词 → fix
- 如果描述中包含 "新增"、"添加"、"功能"、"开发"、"实现" 等词 → feature
- 包含两者都不明显的关键词 → 进入步骤2
-
模糊时询问用户:
无法自动判断这是 Feature(功能开发)还是 Fix(Bug修复)。 请问这是: 1. Feature - 新功能开发或功能优化 2. Fix - Bug修复 -
根据用户选择设置
record_type,然后继续对应的需求澄清流程
阶段 1: 需求澄清
目标: 基于相关性探索结果,通过多轮提问,明确功能需求细节
流程:
-
分析用户提供的功能描述
-
结合相关性探索结果(如果有),识别:
- 已明确的事项(跳过)
- 仍需澄清的模糊点
- 边界条件、依赖关系
-
每轮提问 2-4 个针对性问题
-
用户回答后,进行信息整合后思考模糊点,如果有回到步骤3
提问原则:
- 优先利用历史信息: 如果相似记录中已有明确信息,不再重复询问
- 聚焦差异点: 针对当前需求与已有记录的差异进行提问
- 渐进式深入: 先问核心问题,再问细节
澄清完成标准:
- 功能边界清晰
- 输入输出明确
- 技术约束确认
阶段 2: 需求确认
目标: 确认需求并记录
流程:
- 输出需求摘要供用户确认:
## 需求确认
**类型**: <feature|fix>
**名称**: <名称>
**描述**: <一句话描述>
**详细需求/问题**:
- <需求点1>
- <需求点2>
- ...
-
要求用户确认需求/问题
- 如果用户要求修改则根据用户修改后重新确认
-
更新对应记录(feature 或 fix):
- 如果是创建模式:根据
record_type新建对应记录,返回 item_id - 如果是更新模式:更新已有记录的 content
- content: 确认的需求/问题摘要
- 如果是创建模式:根据
-
建立note[<item_id>-requirements] 记录完整需求/问题描述
- 在对应条目中增加这个note的关联
-
更新development-log note
- 记录需求澄清的关键问题和用户确认
- 记录需求程序流程已经完成
输出
item_id: <feature_id|fix_id>
confirmed_requirements: <确认的需求/问题对象>
完成展示
展示创建/更新的记录ID:
record:
- item_id
- summary
- content
note:
- note_id:summary (requirements)
- note_id:summary (development-log updated)
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.
- 8d ago First seen · 154 lines · 30 tokens per session scan A 3a4650b136ab
requirement-confirmation is a skill published in the GitHub repository w693847022/memory_service (1 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 1,154 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 2026-08-31.
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…