Research uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
npx skills add https://github.com/lingxling/awesome-skills-cn --skill auto-research
When implementing tasks, Claude Code can encounter uncertainties — design choices, algorithm details, API usage, or best practices. This skill provides an explicit-consent research path, presents findings, and waits for user approval before writing code.
The skill supports web research and an optional ChatGPT consultation. It never sends
conversation context, files, browser state, or credentials to a third party without the
user's explicit approval of the exact, redacted text.
Step 1: Propose the research boundary — State the source to use, the exact query or
redacted prompt, whether any local/workspace text would leave the machine, and the likely
cost. Wait for the user to approve that exact boundary.
Step 2: Research — After approval, use web search or a browser session the user has
explicitly selected and authorized. Use a pinned, user-configured browser automation
connector; do not install packages automatically, use @latest, or access browser cookies,
other tabs, saved passwords, or sessions.
Step 3: Present — Distill findings into concise options with sources, presented to the user.
Step 4: Await Approval — Do NOT write code until the user says "go ahead" or picks an option.
Step 5: Implement — Once approved, execute with confidence.
Do not treat ?, ??, or another shorthand as consent. First propose a minimal prompt,
for example: 请评估这个已脱敏的方案的正确性、完整性和可改进之处:<text>.
Explicitly identify every piece of text that would be sent. Only after the user confirms
the exact prompt may you open the selected ChatGPT session, submit that prompt, and present
the response. Do not include conversation history by default.
Redact secrets, personal data, proprietary code, customer data, and internal URLs before
proposing the prompt. If safe redaction is not possible, do not submit it.
If browser automation is necessary, the user must separately authorize the selected browser
profile and connector version. Restrict the session to the consultation tab. Do not inspect,
reuse, export, or rely on cookies from other tabs or profiles.
User: PyTorch 中自定义 ADMM 优化器怎么设计?
Claude: 我可以搜索公开资料,或将以下已脱敏问题发给 ChatGPT:
“如何设计 PyTorch 自定义 ADMM 优化器?请比较可行模式。”
不会发送工作区文件或对话历史。是否允许?
User: 允许发送这段文字
Claude: [Opens only the authorized consultation tab, submits the approved prompt]
Claude: GPT suggests approach A with these pros/cons. Proceed?
User: 行
Claude: [Implements code]
User: ?? ADMM convergence criteria best practices
Claude: I can search public sources for the exact redacted query
“ADMM convergence criteria best practices”. No workspace files or conversation
history will be sent. May I send that text to WebSearch and fetch the results?
User: Yes, send that query
Claude: [WebSearch + WebFetch → finds Boyd et al. paper, extracts criteria]
Claude: Boyd recommends ||r|| < ε·max(||Ax||, ||Bz||, ||c||). Use this?
User: Yes
Claude: [Implements]
page.fill() for instant text injection instead of keyboard.type()| Problem | Solution |
|---------|----------|
| ChatGPT shows login page | Let the user log in themselves; do not handle cookies or credentials |
| The prompt contains sensitive context | Redact it or use local reasoning instead |
| Browser automation is unavailable | Use web search or stop and ask the user for a different approved method |
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Take lingxling/auto-research from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.