unbox

name

unbox

description

Use when profiling researchers beyond their publication list — personality, early career, mentorship lineage, direction evolution, deleted content. Also supports entity mode for profiling companies/orgs/labs (funding, team, products, business). Also supports targeted gap-filling on existing profiles (--mode=backfill). Triggers: /unbox, 开盒, researcher profile, 'who is this author', backfill, 补全, company profile, 'survey this company', org profile.

Unbox — 研究者 & 实体画像调研

两种模式：

• 默认（人物）：不是查简历，是拼人。发表列表谁都能查，要挖的是性格信号、早期经历、方向演变、师门关系。
• Entity（实体）：不是看官网，是拼全景。融资、团队、研究、产品、商业模式、资本市场格局。

When to Use

• User says /unbox or 开盒
• User asks to research/profile researchers ("这人什么背景", "who is this author")
• User pastes a paper link and wants to know about the authors
• User wants to understand a research group's composition
• User asks to survey/profile a company or org ("survey this company", "这家公司什么情况")
• User provides a company URL and wants a full profile

When NOT to Use

• Looking up a single paper's content → use web-fetcher or scholar-agent
• Adding researchers to selfOS wiki → use selfos skill after unbox
• General topic research → use deep-research

Command

/unbox <input>                    # full investigation — person (default)
/unbox --mode=entity <input>     # full investigation — company/org/lab
/unbox --mode=backfill [name]     # targeted gap-filling on existing profiles
/unbox --mode=backfill --top N    # top N profiles by gap count
/unbox --mode=backfill --dry-run  # show gap analysis only

Full Mode — Person (default)

Input is one of:

• arXiv URL: https://arxiv.org/abs/...
• OpenReview URL: https://openreview.net/forum?id=...
• Comma-separated names: "Alice Zhang, Bob Li, Charlie Wang"
• Local file path (one name per line)

Entity Mode (--mode=entity)

Input is one of:

• Company URL: https://example.ai/
• Company name: "Thinking Machines Lab", "Black Forest Labs"
• Comma-separated names: "BFL, World Labs, Luma AI" (batch mode)

Workflow

Before executing, read references/subagent-prompt.md for the full per-person pipeline.

Step 1: Parse Input

Determine input type:

Input	Detection	Action
https://arxiv.org/abs/...	URL regex	Fetch page via python3 ~/.claude/skills/web-fetcher/scripts/fetch.py <url>, extract author list
https://openreview.net/forum?id=...	URL regex	Fetch page via fetch.py, extract author list
Comma-separated string	Contains , and no URL scheme	Split by ,, trim whitespace
File path	File exists on disk	Read file, one name per line

For paper links: after extracting authors, display the list and ask user to confirm or remove names before proceeding.

Step 2: Create Output Directory

mkdir -p ~/outputs/unbox/profiles ~/outputs/unbox/overviews

Canonical output paths (see docs/outputs-convention.md):

• Individual profiles: ~/outputs/unbox/profiles/{slug}.md
• Overview files: ~/outputs/unbox/overviews/_overview.md (or batch-specific name)
• Backfill deltas: ~/outputs/unbox/profiles/_backfill_{slug}.md

Never write to cwd-relative unbox-output/ — always use absolute ~/outputs/unbox/.

Step 3: Dispatch Subagents

For each researcher name, spawn one subagent using the Agent tool:

• All subagents launch in parallel (single message with multiple Agent tool calls)
• Each subagent receives the full prompt from references/subagent-prompt.md with {NAME} replaced
• Each subagent writes its report to ~/outputs/unbox/profiles/{slug}.md
• Slug: lowercase name, spaces replaced with -, remove non-ascii (e.g., "Alice Zhang" → alice-zhang)

Example Agent call:

Agent({
  description: "Unbox: Alice Zhang",
  prompt: "<contents of subagent-prompt.md with {NAME} = Alice Zhang>",
  mode: "bypassPermissions"
})

Step 4: Assemble Overview

After all subagents complete, read all generated reports and create ~/outputs/unbox/overviews/_overview.md:

# Unbox Report

Generated: {date}
Source: {input description}
Researchers: {count}

| 姓名 | 机构 | 一句话 | 报告 |
|------|------|--------|------|
| ... | ... | ... | [链接](slug.md) |

The "一句话" for each person is extracted from the ## 一句话 section of their report.

Step 5: Report to User

Print the overview table and the path to ~/outputs/unbox/profiles/.

---

Backfill Mode Workflow (--mode=backfill)

Before executing, read references/backfill-prompt.md for the per-person backfill subagent prompt.

Backfill mode runs targeted searches on existing profiles to fill gaps introduced by prompt upgrades. It does NOT re-run identity anchoring or the full pipeline.

Backfill Step 1: Gap Analysis

For each target profile, read the existing report and detect which sections are missing.

Gap detection rules — check for presence of section headers or key content using gap_ids from references/search-playbook.md:

Gap ID	Detection Rule
non-academic	No "非学术" content in 性格信号
alumni-media	No "校友" or "专访/人物报道" beyond basic interview
overseas-forums	No mention of newmitbbs, 1point3acres, muchong
controversy	No "争议" or "如何评价" content
award-reports	No award coverage details
video-presence	No video presence
tech-media	No 机器之心/量子位/雷锋网/36kr coverage
wechat-mp	No mp.weixin.qq.com sourced content

Also extract identity anchors (name, school, advisor, etc.) and parse "未验证/待挖" leads.

Priority score = count of missing sections + count of actionable leads.

If --dry-run, print gap analysis table and stop.

Backfill Step 2: Build Backfill Prompts

For each profile (sorted by priority score descending):

1. Read references/backfill-prompt.md
2. Replace placeholders with extracted identity anchors and gap list
3. Reference references/search-playbook.md for queries — only execute sections matching detected gaps

Backfill Step 3: Dispatch Subagents

Batch size: 5 parallel subagents at a time. Same Agent call pattern as full mode.

Dispatch order: sorted by priority score (most gaps first).

For each batch:

1. Launch 5 subagents in parallel, each with the filled backfill prompt
2. Wait for all 5 to complete
3. Before launching next batch, check rate limit status (if 429 errors, wait 30 seconds)

Each subagent writes findings to: {OUTPUT_DIR}/_backfill_{SLUG}.md

Budget: ~25 Google searches per person (vs ~80 for full investigation).

Backfill Step 4: Merge Results

After each batch, merge backfill results into original profiles. Reference references/merge-utils.md for the append protocol:

Backfill Type	Merge Strategy
New section content	Insert before ## 未验证 or at end if header doesn't exist
Enhanced existing section	Append with > 🔍 补全 ({date}): prefix
New 未验证/待挖 items (snowball)	Append to existing 未验证 list
Resolved 未验证 items	Change - [ ] to - [x] with resolution

Idempotency check: Before adding content, search for the key phrase in the existing report. Skip if already present.

Backfill Step 5: Summary Report

Print per-profile changes (sections added, leads resolved, new leads from snowball).

---

Entity Mode Workflow (--mode=entity)

Before executing, read references/entity-subagent-prompt.md for the full per-entity pipeline.

Entity Step 1: Parse Input

Input	Detection	Action
URL (https://...)	URL regex	Use as primary source, extract company name
Company name string	No URL scheme, no comma	Single entity
Comma-separated	Contains ,	Split, batch mode

Entity Step 2: Create Output Directory

mkdir -p ~/outputs/unbox/entities ~/outputs/unbox/overviews

Entity Step 3: Dispatch Subagents

For each entity, spawn one subagent using the Agent tool:

• All subagents launch in parallel (batch mode)
• Each subagent receives the full prompt from references/entity-subagent-prompt.md with {ENTITY} and {URL} replaced
• Each subagent writes its report to ~/outputs/unbox/entities/{slug}.md
• Slug: lowercase name, spaces replaced with - (e.g., "Black Forest Labs" → black-forest-labs)

Entity Step 4: Assemble Overview

After all subagents complete, create ~/outputs/unbox/overviews/_entity-overview.md:

# Entity Unbox Report

Generated: {date}
Entities: {count}

| 名称 | 赛道 | 融资 | 估值 | 团队 | 一句话 |
|------|------|------|------|------|--------|
| ... | ... | ... | ... | ... | ... |

Entity Step 5: Report to User

Print the overview table and path to ~/outputs/unbox/entities/.

---

Common Mistakes

Mistake	Fix
用 fetch.py 抓知乎 → 403	必须用 opencli zhihu search/download/question
从新闻报道推断本科院校	个人主页 bio 页面是 ground truth，优先级最高
只搜英文不搜中文	中国研究者的早期经历（竞赛、保研）全在中文搜索里
不搜高中/高考信息	从本科入学年反推高考年，搜 "{中文名} 高中/高考/自主招生"，信息密度远超本科标签
忽略微信公众号	大量早期信息沉淀在学校/院系/社团公众号，用 Google 搜 "{中文名} mp.weixin.qq.com"（注意：不要用 site: 语法，直接作为关键词）
不查高校 BBS	水木社区 (newsmth.net) 有入学名单、院系讨论；cc98.org 有浙大信息
不查海外中文论坛	newmitbbs.com 和一亩三分地 (1point3acres.com) 有大量 biographical tidbits
Wayback Machine 是唯一的时光机	先查 GitHub Pages 源码仓库的 git log，精确到天
只看发表列表	性格信号（thesis 致谢、知乎、rebuttal 风格）才是核心
忽略 conference tutorials	Tutorial 比 paper 更能看出判断力和思想 flow
对单个名字没加消歧	"Hao Zhang" 有几十个同名人，加 institution/paper 关键词
只搜"采访/访谈"	中文人物报道关键词更多：专访、人物、故事、校友故事、校友风采
不搜非学术特质	运动队、社团、乐队、创业等信息藏在校级新闻和校友报道里
不搜科技媒体	机器之心/量子位/雷锋网/36kr/腾讯云的人物长文是性格信号金矿
不搜奖项报道	MIT TR35/求是奖/科学探索奖的报道常包含详细人物背景
搜一次就停	必须滚雪球：每次 fetch 页面后提取新线索，追加搜索
不搜"如何评价 XXX"	知乎"如何评价"类问题是中文互联网上最集中的人物讨论
Running backfill without reading existing report	Read the report FIRST. Backfill ONLY fills gaps, never re-searches identity.
Re-running all search sections in backfill mode	Only execute sections matching detected gaps from gap analysis.