BC wechat-archive-to-ima
把用户微信 PC 端已经落地到本地的聊天附件（图片、视频、文档）做系统化归档， 按联系人归类，并同步上传到腾讯 ima 知识库（OpenAPI）或本地 Obsidian 库。 适用场景：(1) 用户说"帮我整理微信里跟某人的所有资料/文件/图片"； (2) 用户说"把微信聊天里的合同、发票、会议纪要归档到知识库"； (3) 用户说"微信收到的文件太多找不到，帮我分类"； (4) 用户给了 ima 的 ClientID / APIKey，要求把本地资料批量入库； (5) 用户要对已归档的微信资料做摘要、合同条款提取、待办清单、客户画像分析； (6) 用户说"帮我把微信里所有人的附件都整理到电脑"； (7) 用户说"微信存下来的图片是 .dat 打不开，帮我解出来"（已支持：自动推导 code 并解密）。 **不适用**：解密微信加密数据库（message_*.db / contact.db）、读取用户未在 PC 端打开过的消息、 远程操作手机微信、抓内存密钥、导出聊天正文 —— 这些一律不做， 遇到这类要求直接说明边界并给替代方案。
把用户微信 PC 端已经落地到本地的聊天附件（图片、视频、文档）做系统化归档， 按联系人归类，并同步上传到腾讯 ima 知识库（OpenAPI）或本地 Obsidian 库。 适用场景：(1) 用户说"帮我整理微信里跟某人的所有资料/文件/图片"； (2) 用户说"把微信聊天里的合同、发票、会议纪要归档到知识库"；…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary"
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 52 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1717 tokens
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 472: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 52 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 4 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.