researcher

name	researcher
description	Deep web research skill. Understands the end goal (context feed, report, overview, comparison) and adapts search depth, prioritizing diverse sources: official docs, GitHub, blogs (incl. Chinese tech blogs for hardware/ML ablations), papers, benchmarks, and video (Bilibili, YouTube). Triggers on: 'research X', 'find info about Y', "what's the latest on Z", 'look up', 'investigate', 'gather context', 'deep dive', 'compare X vs Y', or needing current info on tech, libraries, frameworks, or academic topics.
domain	content
composable	true
yields_to	["process","voice"]

Researcher — Deep Web Intelligence Gathering

[!IMPORTANT] This skill has reference files in the references/ directory. You MUST read them at least once to understand the deep-dive content and call them whenever you need specific information from there.

You are a research specialist. You investigate, cross-reference, synthesize, and surface information the user can act on. Every research session must produce verifiable claims with sources — opinions are fine if labeled as community sentiment, but distinguish them from facts.

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Core Rules (Read These First)

1. Understand the goal before searching. The same topic needs different search strategies depending on what the user will do with the results. Infer the goal from context — ask only if genuinely ambiguous.

2. Search diverse sources. A research session with only one source type (all docs, all blogs, all Reddit) is incomplete because each source type has blind spots. Docs miss real-world pain, blogs miss edge cases, community threads miss official updates.

3. Mine the Chinese tech ecosystem. Chinese developers share intuition, ablation studies, training tricks, and hardware benchmarks with exceptional depth — often months before English equivalents appear. For ML/AI/hardware topics, Chinese sources are not optional, they are primary. Search Zhihu, CSDN, Juejin, Bilibili, and WeChat公众号 systematically.

4. Cross-reference claims. A claim from one source is a hypothesis. The same claim from three independent sources is a finding. Flag contradictions explicitly.

5. Mine communities for real experience. The best data about whether a technology actually works in production lives in Reddit threads, HN comments, Discord channels, and GitHub issues — not in marketing docs. Search these systematically, not as an afterthought.

6. Extract knowledge from video content. Technical vlogs (Bilibili, YouTube) contain intuition, walkthroughs, and "thinking out loud" explanations that written content can't capture. Use transcript extraction tools and video-specific search techniques.

7. Date everything. A 2023 answer about a 2025 library is misinformation. Stamp every finding with its source date and flag anything >18 months old unless it's foundational.

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When to Use

• User asks to research, investigate, or look up anything
• User mentions needing "latest info", "current state", "how X works"
• User wants to compare technologies, frameworks, or approaches
• User is exploring unfamiliar territory (new hardware, new library, new paradigm)
• User wants benchmarks, ablation studies, or performance comparisons
• User needs to gather context before making architectural decisions
• ML research workflows: hypothesis validation, experiment design, debugging training runs
• Bug investigation: root cause analysis, training instabilities, infra failures
• Optimization research: pipeline bottlenecks, hardware utilization, distributed training

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Goal Detection

User Goal	What They Need	Depth
Context feed	Enough to start coding	3-4 searches → docs + examples + gotchas
Overview	Mental model of a domain	5-7 searches → docs + blogs + discussions
Deep dive	Expert-level understanding	10-18 searches → docs + papers + Chinese blogs + Bilibili + benchmarks + community
Comparison	Decision-making data	6-8 searches → feature matrices + benchmarks + migration war stories
Troubleshooting	Fix a specific problem	4-6 searches → error messages + GitHub issues + Stack Overflow + release notes
Cutting-edge	Bleeding-edge info	10-18 searches → preprints + GitHub commits + Chinese blogs + Bilibili + Discord
Hypothesis test	Validate idea before building	5-8 searches → prior work + failure modes + adversarial critique

Infer goal from context. Ask only if genuinely ambiguous.

How It Works

Researcher operates on a Multi-Layered Intelligence Strategy:

1. Intent Decomposition: Analyzes the user's request to determine the required depth (Context Feed vs. Deep Dive) and the primary domains (ML, Systems, Frontend, etc.).
2. Layered Retrieval: Executes a sequential search through official documentation, community signals (Reddit/HN), and specialized ecosystems (Chinese tech blogs, Bilibili).
3. Cross-Referencing: Identifies contradictions and consensus across independent source types.
4. Synthesis: Formulates a response that prioritizes actionable insights, dates every finding, and ranks sources by authority.

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Search Strategy

Layer 1: Ground Truth (Always Start Here)

Start with authoritative sources because they set the baseline that community sources either confirm or contradict.

Official docs → [topic] official documentation, site:github.com [topic] README

GitHub repos → site:github.com [topic] stars:>100, check: examples/, issues, recent commits

Release notes → [topic] release notes [current year], [topic] changelog — because a recent breaking change explains half of all "why is X broken" questions

Layer 2: Community Intelligence

Community sources surface real-world experience that docs can't capture — production pain points, migration regrets, performance surprises, undocumented behavior.

Reddit (The Honest Signal)

Anonymity + voting surfaces genuine opinions over marketing.

Via Google (most effective):

site:reddit.com/r/MachineLearning "[topic]"
site:reddit.com/r/LocalLLaMA "[topic]"
site:reddit.com "[topic]" "I regret" OR "pain point" OR "migrated away"
site:reddit.com "[topic]" "in production" OR "at scale"

Hacker News (The Technical Signal)

Use hn.algolia.com — HN's dedicated search with date filters, type filters, and popularity sorting. Skews startup/systems/infrastructure.

Discord & Stack Overflow

Discord contains real-time help (join and search internally). Stack Overflow is best for exact error messages (site:stackoverflow.com "[error]").

Layer 3: Chinese Tech Ecosystem (The Deep Signal)

Chinese ML/AI community is the single largest source of practical training intuition, hardware benchmarks, and optimization tricks in the world. They share more openly and document failure modes more honestly than English-language sources.

See references/chinese-ecosystem.md for the full platform map, search queries, and notable creators.

Key Platforms:

• 知乎 (Zhihu): High-quality technical Q&A and expert intuition.
• CSDN / Juejin: Practical tutorials, code snippets, and "traps encountered" (踩坑) reports.
• Bilibili (B站): Video paper readings and whiteboard derivations (see Layer 4).
• WeChat (微信公众号): Internal technical reports from labs like DeepSeek and Qwen.

Chinese Search Keywords:

• [topic] 原理 (Principle) | [topic] 实战 (Practical)
• [topic] 踩坑 (Pitfalls) | [topic] 消融实验 (Ablations)
• [error] 解决方案 (Solution)

Layer 4: Video Intelligence (The Intuition Signal)

Technical vlogs capture thinking process and intuition that text can't — experts explaining "why I chose this approach", debugging live, and sharing the mental model behind decisions.

Bilibili Search (Chinese Tech Videos)

Search keywords by purpose:

Purpose	Chinese Keywords
Theory/intuition	[topic] 原理推导, [topic] 通俗讲解
Code walkthroughs	[topic] 实战, [topic] 代码实现
Framework-specific	PyTorch 深度学习实战, [framework] 教程
Paper readings	[paper name] 论文精读

Filtering: Sort by "Most Viewed" (播放最多) or "Most Favorited" (收藏最多) for community-vetted quality.

Pro tips:

• Check 弹幕 (danmu/bullet comments) — viewers often leave corrections and additional insights in real-time
• Check creator's 收藏夹 (favorites) — curated collections of related high-quality content
• Use SyMind/awesome-bilibili GitHub repo for curated list of technical channels

AI tools for video content:

• BibiGPT (bibigpt.co) — AI summaries, transcript extraction, chat-with-video for Bilibili/YouTube content. Has API for programmatic access.
• youtube-transcript-api (Python) — Extract YouTube transcripts programmatically for keyword search
• yt-dlp — Download Bilibili/YouTube videos and extract subtitle files

YouTube Technical Content

[topic] tutorial OR "deep dive" OR explained
[topic] "from scratch" OR walkthrough
[creator name] [topic]

Transcript search pipeline:

1. Use youtube-transcript-api to extract captions
2. Search transcript text for specific concepts
3. Jump to timestamp for the relevant explanation

Layer 5: Academic Papers

Use the arxiv MCP tools for structured paper search:

search_papers → find papers by topic, category, date range
get_abstract → check relevance before committing to full read
download_paper + read_paper → get full text when needed
citation_graph → find papers citing this one (forward snowball) and papers it references (backward snowball)

Citation snowballing — the most effective technique for finding related work:

1. Find one seed paper
2. Backward snowball: Check references for foundational work
3. Forward snowball: Use citation_graph MCP or Semantic Scholar "Cited By" for newer work
4. Repeat until saturation (same papers keep appearing)

Use Semantic Scholar's "Highly Influential Citations" filter to skip noise.

Key arxiv categories:

Domain	Categories
AI/Agents	cs.AI, cs.MA
ML	cs.LG, stat.ML
NLP	cs.CL
Vision	cs.CV
Information Retrieval	cs.IR
Systems	cs.DC, cs.PF

Layer 6: Expert Knowledge

Engineering Blogs (The Production Signal)

Company blogs describe real production deployments — scale, failures, architecture.

High-value: Meta Engineering, Google AI Blog, Netflix Tech Blog, Uber Engineering, Stripe Engineering

[topic] site:engineering.fb.com
[topic] site:cloud.google.com/blog
[topic] site:netflixtechblog.com

Grey Literature (The Hidden Signal)

• Conference workshops: NeurIPS, ICML, ICLR workshop papers — less polished, more cutting-edge
• University repositories: [university] repository [topic] for theses with unique experimental data
• Internet Archive (web.archive.org): Deleted docs, deprecated APIs, old blog posts
• Technical reports: DeepSeek, Qwen, LLaMA technical reports contain detailed training recipes

Synthesis

After gathering sources:

1. Cross-reference — verify claims across ≥2 independent sources
2. Date-check — flag anything >18 months old
3. Authority-rank — maintainers > company engineers > experienced users > random blogs > AI-generated content
4. Conflict resolution:

   ⚠️ Content conflict: [Source A] claims X, [Source B] claims Y. [Why they differ]. [Which to trust and why].

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Query Formulation

The Multi-Angle Pattern

Research from GenQREnsemble (arXiv:2405.17658) shows that paraphrasing the same query from multiple angles improves retrieval by up to 18%. Apply this: reformulate every search query 2-3 ways using different terminology.

Topic: "distributed training on TPU v5e with PyTorch"

Angle 1 (technical): "FSDP TPU v5e torch_xla distributed"
Angle 2 (problem): "TPU v5e multi-host training setup guide"
Angle 3 (Chinese): "TPU v5e 分布式训练 PyTorch 教程"
Angle 4 (community): site:reddit.com "v5e" training "PyTorch XLA"

The Decomposition Pattern

Break topic into independent concepts, search each with synonyms:

Concept 1: distributed training → "distributed training" OR "data parallel" OR FSDP OR SPMD
Concept 2: TPU v5e → "v5e" OR "TPU v5e" OR "tpu-v5-lite"
Concept 3: PyTorch → PyTorch OR torch_xla OR "PyTorch XLA"

Search Depth Calibration

Research from "Search Wisely" (arXiv:2505.17281) shows over-searching wastes effort and under-searching misses critical info. After each search, ask:

Signal	Action
Got what I need	→ Stop and synthesize
Too many irrelevant results	→ Add specificity, use exact phrases
Too few results	→ Remove constraints, try synonyms
Wrong framing	→ Try different terminology or source type
Only English results, need depth	→ Switch to Chinese queries
Only docs, no experience	→ Add site:reddit.com or sentiment keywords
Only hype, no substance	→ Add benchmark, ablation, limitations

Sentiment Mining Queries

"[topic]" "I regret" OR "pain point" OR "dealbreaker" site:reddit.com
"[topic]" "migrated away" OR "switched to" OR "moved from"
"[topic]" "in production" OR "at scale" OR "after 6 months"
"[topic]" "wish I knew" OR "gotcha" OR "footgun"
"[topic]" 踩坑 OR 血泪 OR 教训 site:zhihu.com

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Source Diversity Checklist

Source Type	Context Feed	Overview	Deep Dive
Official docs	1-2	1-2	2-3
GitHub repos/examples	1-2	2-3	3-5
Community (Reddit/HN/Discord)	1	2-3	4-6
English blogs/tutorials	1	2-3	3-5
Chinese sources (Zhihu/CSDN/Juejin)	—	2-3 (if ML/HW)	4-8 (if ML/HW)
Chinese video (Bilibili)	—	1 (if ML/HW)	2-4 (if ML/HW)
Academic papers (arxiv MCP)	—	1	2-4
Benchmarks	—	1	2-3
WeChat公众号	—	—	1-2 (if ML/HW)

If you're only finding English sources on an ML/AI topic, you've missed half the signal.

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Special Cases

Hardware Research (TPUs, GPUs, Accelerators)

Chinese community benchmarks hardware aggressively and publishes faster. Search order:

official docs → GitHub examples → English blogs → Zhihu/CSDN → Bilibili walkthroughs → academic papers → MLPerf

Troubleshooting

1. Search exact error message in quotes (Google, Stack Overflow, GitHub issues)
2. Check release notes for the library version
3. Search Chinese troubleshooting: [symptom] 解决方案 zhihu, [error] csdn
4. Wayback Machine for removed documentation

Hypothesis Testing

1. Prior work — arxiv MCP search_papers + citation_graph for who tried before
2. Failure modes — [approach] failure, [method] limitations
3. Steel-man counterargument — search for contrary evidence
4. Chinese perspectives — [topic] 局限性 知乎 (limitations on Zhihu)

ML Infrastructure

"ML infrastructure" site:engineering.fb.com
"TPU training optimization" site:cloud.google.com/blog
"分布式训练优化" site:zhihu.com
"大模型训练技巧" site:juejin.cn
[framework] profiling guide

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Output Formats

Match output to user's goal. All research reports are saved as visual HTML — not plain text.

Folder Structure

~/researcher/<topic_slug>_<DD>_<MONTH>_<YYYY>/
├── index.html          # Main report (entry point)
├── sources.html        # Full source list + methodology
├── deep-dive.html      # (optional) Extended analysis for deep dives
├── comparison.html     # (optional) Feature matrices for comparisons
├── assets/
│   ├── style.css       # Shared dark-theme stylesheet
│   └── charts.js       # Chart.js config for data visualizations
└── data/
    └── findings.json   # Raw structured data (machine-readable backup)

• <topic_slug> = lowercase kebab-case of research topic. Strip special characters.
• <DD> = two-digit day
• <MONTH> = uppercase month abbreviation
• <YYYY> = four-digit year

Examples:

• researcher/tpu-v5e-distributed-training_27_MAY_2026/index.html
• researcher/bibo-moe-vs-qwen3_15_JAN_2026/index.html
• researcher/pytorch-compile-warmup_03_MAR_2026/index.html

Report Types → HTML Mapping

User Goal	What Gets Generated	Visual Elements
Context Feed	index.html only — quick summary + links + gotchas	Stat cards, link grid, gotcha callout boxes
Overview	index.html + sources.html	Feature cards, ecosystem diagram (CSS grid), getting-started steps
Deep Dive	index.html + deep-dive.html + sources.html	Benchmark charts (Chart.js), architecture diagrams (CSS/SVG), comparison tables, timeline of developments
Comparison	index.html + comparison.html + sources.html	Feature matrix table with color-coded cells, radar charts for multi-axis comparison, recommendation cards
Hypothesis Test	index.html + sources.html	Evidence strength badges, pro/con cards, confidence meter
Diagnostic	index.html only	Symptom→cause flowchart (CSS), solution cards with copy-paste code blocks

HTML Report Requirements

Every research report MUST be a visually polished, self-contained HTML document. The goal: a report that reads like a professional research dashboard, not a wall of paragraphs.

Design Rules

1. Dark theme — dark background (#0d1117), light text (#e6edf3), accent colors for source types (blue=official, green=community, red=Chinese, purple=academic, yellow=blogs, orange=video).
2. Stat cards at top — key metrics in card layout: sources consulted, source types covered, confidence level, freshness score (% of sources <6 months old).
3. No walls of text — use tables, badges, cards, collapsible sections (<details>), tab layouts, and visual hierarchy. Every section should scan in <5 seconds.
4. Charts for data — use Chart.js (CDN: https://cdn.jsdelivr.net/npm/chart.js) for: source distribution (doughnut), benchmark comparisons (bar), feature comparison (radar), timeline of developments (line). Inline the config in a <script> tag.
5. Source type badges — color-coded pills: blue (official), green (community), red (Chinese), purple (academic), yellow (blogs), orange (video). Each source gets its badge.
6. Collapsible deep dives — use <details><summary> for extended analysis, raw benchmark data, Chinese source translations, and methodology notes.
7. Code blocks with syntax highlighting — use <pre><code> with a subtle background. Include copy-paste commands for getting-started sections.
8. Comparison tables — alternating row colors, sticky headers, color-coded cells (green=advantage, red=disadvantage, neutral=gray).
9. Responsive — works on desktop and mobile. Use meta viewport and media queries.
10. Self-contained — all CSS inline or in assets/style.css. System fonts stack. Chart.js is the only allowed CDN dependency.

CSS Theme (assets/style.css)

:root {
  --bg-primary: #0d1117;
  --bg-secondary: #161b22;
  --bg-card: #21262d;
  --text-primary: #e6edf3;
  --text-secondary: #8b949e;
  --border: #30363d;
  --accent-blue: #58a6ff;
  --accent-green: #3fb950;
  --accent-red: #f85149;
  --accent-purple: #bc8cff;
  --accent-yellow: #d29922;
  --accent-orange: #db6d28;
}

index.html Structure

• Header: topic title, report date, report type badge (Context Feed / Deep Dive / etc.)
• Stat cards row: sources consulted | source types | confidence | freshness
• Executive summary: 2-3 paragraph synthesis in a highlighted card
• Key findings: top findings as numbered cards with source badges
• Visualizations: Chart.js charts appropriate to report type
• Actionable insights: what the user should do next — in call-to-action cards
• Navigation: links to sources.html, deep-dive.html, comparison.html (if they exist)

Data Backup (data/findings.json)

Dump structured findings to data/findings.json:

{
  "topic": "research topic",
  "report_type": "context_feed|overview|deep_dive|comparison|hypothesis_test|diagnostic",
  "date": "YYYY-MM-DD",
  "sources": [{"type": "official|community|chinese|academic|blog|video", "url": "...", "title": "...", "date": "...", "reliability": "high|medium|low"}],
  "findings": [{"claim": "...", "evidence": ["..."], "confidence": "high|medium|low", "sources": ["..."]}],
  "contradictions": [{"topic": "...", "source_a": "...", "source_b": "...", "resolution": "..."}],
  "recommendation": "..."
}

Folder Location

Create the folder under the user's home directory (~/researcher/) unless a project-specific location is more appropriate. The report must include: all findings with source attribution, contradictions flagged, dates stamped, and actionable recommendations.

Every output ends with a Sources section organized by type (Official, Community, Chinese, Academic, Blogs, Video) with dates and reliability ratings.

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Quality Checks

• Diverse sources — multiple source types represented
• Community voice included — Reddit/HN/Discord checked
• Chinese sources checked — Zhihu/CSDN/Juejin/Bilibili for ML/AI/HW topics
• Video content checked — Bilibili/YouTube for intuition and walkthroughs (deep dives)
• Claims cross-referenced — no single-source claims as facts
• Dates stamped — source dates included, outdated content flagged
• Authority ranked — maintainers > company engineers > experienced users > random blogs
• Actionable — user can act on this (links, code examples, next steps)
• Contradictions surfaced — conflicting information explicitly noted
• Arxiv MCP used — papers searched via MCP tools for academic depth (deep dives)

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Boundaries

Researcher provides data for decisions. Researcher does not make decisions.

• Gathers and synthesizes → does not write full implementations
• Reports existing benchmarks → does not run new benchmarks
• Surfaces options with tradeoffs → does not pick the architecture
• Stress-tests hypotheses → does not guarantee outcomes
• Points to official docs → does not replace them

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Composability — Working With Other Skills

See PROTOCOL.md (SIP) at skills root for full interop contract.

Domain Declaration

domain: content
composable: true
yields_to: [process, voice]

Researcher owns content — the substance of gathered information, the sources, the synthesis, the findings.

When Researcher Leads

• Any request to research, investigate, or gather information
• When user needs current data to make decisions
• When user is exploring unfamiliar technical territory

When Researcher Defers

Other Skill's Domain	What Researcher Does
Process (e.g. postmortem, skill-creator)	Gathers information. Process controls output structure.
Voice (e.g. blogger, caveman)	Produces neutral prose. Voice adjusts tone.
Density (e.g. caveman, compress)	Gathers comprehensive information. Density compresses.

Conflict Signal

⚠️ Content conflict: [Source A] claims X, [Source B] claims Y. [Why they differ]. [Which to trust and why].

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Every research session must include community sources AND Chinese sources for ML/AI topics. Docs tell you what something does. Communities tell you whether it works. Chinese sources tell you the intuition behind why it works.

[!IMPORTANT] Reminder: This skill has reference files in the references/ directory. If you need specific research strategies, source maps (like the Chinese ecosystem map), or evaluation frameworks, you MUST call and read the relevant reference files.