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Updated 10-16 07:25

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Research Methodology

Search-fetch loop methodology for a single researcher agent. This skill is loaded BY researcher agents — not invoked directly. Honoring YAGNI, KISS, DRY. Be honest, be brutal, straight to the point, be concise.

Your caller (lead-researcher) assigned you: a topic, language(s), and iteration count. Follow those assignments. Search in your assigned language(s) — don't default to English unless assigned English.

Phase 1: Scope Definition

Before searching, define:

  • Key terms and concepts to investigate
  • Recency requirements (how current must information be)
  • Evaluation criteria and research depth boundaries
  • Which languages/communities are most likely to have strong signal for this topic

Phase 2: Search in Assigned Language(s)

Search in the language(s) assigned by your lead-researcher caller. Write queries in the target language — do not search in English when assigned Chinese/Russian.

Language Guidelines

Search in your assigned language using native technical terms. Every language community has unique perspectives — don't limit what you might find based on stereotypes about what each language "specializes in."

For query templates per language, read references/query-templates.md.

Query Construction

Write queries in the target language — do not search in English expecting Chinese/Russian results.

English queries:

"[topic] best practices"
"[topic] production architecture"
"[topic] vs [alternative] tradeoffs"

Chinese queries (language label: 中文):

"[topic] 最佳实践"          (best practices)
"[topic] 生产环境 架构设计"   (production architecture design)
"[topic] 解决方案 对比"      (solution comparison)
"[topic] 踩坑 经验"          (pitfalls & experience)
"[topic] 性能优化"           (performance optimization)

Russian queries (language label: русский):

"[topic] лучшие практики"           (best practices)
"[topic] архитектура решения"       (architecture solutions)
"[topic] производительность"        (performance)
"[topic] сравнение подходов"        (approach comparison)

Search Strategy

  • 8-15 targeted queries per language, not broad sweeps
  • Start with [topic] + "best practices" or "system prompt" for patterns
  • Follow with [topic] + specific sub-problems
  • Cross-reference: if three sources across languages agree, it's robust
  • Search iteratively: first-round results inform second-round queries
  • Do NOT include year in queries — prefer newest results by default
  • Use site: targeting when your lead-researcher prompt specifies elite forums to search
  • Prioritize signal quality by source tier:
    1. Elite forums (V2EX, Habr, HN, Lobste.rs, Indie Hackers, linux.do)
    2. GitHub repos, issues, code
    3. Official docs (vendor sites, framework docs)
    4. High-value analysis (arXiv, McKinsey, Deloitte, IDC)
    5. Blog posts, tutorials (secondary — verify against tier 1-4)
    6. Content farms (CSDN reposts, Baijiahao (百家号), Zen.yandex) — DEPRIORITIZE

Max 60 search tool calls total — think carefully before each one. User may request fewer.

Search-Fetch Loop

Each iteration is a complete cycle — search, fetch, and use other tools together. Not search-only or fetch-only rounds.

┌─→ ITERATION (each one contains ALL of these):
│   │
│   ├─ WebSearch (up to 3 queries — different angles/languages)
│   │       ↓
│   ├─ WebFetch relevant results (up to 3 — only what matters to the question)
│   │       ↓
│   ├─ Other tools as needed (gh search, docs lookup, etc.)
│   │       ↓
│   ├─ Extract: ideas, data, claims, new leads
│   │       ↓
│   └─ Evaluate: what did I learn? what gaps remain? what new leads appeared?
│           ↓
│   Next iteration uses refined queries informed by what was actually read
│       ↓
└── Repeat (min 3, up to 10 iterations — stop when no new signal)
Rule Detail
Complete cycles Each iteration MUST include both search AND fetch — not one or the other
Max 3 per tool per iteration Up to 3 WebSearch + up to 3 WebFetch + other tools as needed per iteration
Fetch selectively Only fetch URLs relevant to the research question — skip generic/tangential results
Snippets can suffice If a snippet gives you what you need (a version, a yes/no), no fetch needed
Follow leads Fetched pages reveal new sources — fetch those in the same or next iteration
Refine queries Each iteration's searches should be sharper than the last, informed by fetched content
Fetch before citing deeply Substantive claims require fetching the actual page — don't argue from snippets
Parallel calls Batch independent WebSearch and WebFetch calls in parallel within each iteration
Escalate on failure If WebFetch fails (JS-rendered, anti-bot), escalate per Phase 4 retrieval tools

Phase 3: GitHub Research

Prerequisite: Check gh CLI exists (which gh). If unavailable, skip to Phase 4.

Use gh CLI to find reference implementations and community patterns. Search in your assigned language(s) — use the correct terms for each language.

Layered Query Strategy (MANDATORY)

GitHub search matches on repo name + description + README. Compound queries miss repos with different word order. Always search broad-to-narrow in layers.

Layer Purpose Example
L1: Broad single-concept Catch infra tools missing niche jargon "headless browser" / "无头浏览器" / "безголовый браузер"
L2: Compound topic-specific Narrow to domain-specific projects "browser agent" / "AI代理浏览器"
L3: Adjacent categories Catch overlapping/dependent tools "stealth browser" / "反检测浏览器"
L4: Language-filtered Systems-language tools (Rust/Go/Zig) same query + --language rust

Every layer MUST run in your assigned language(s). Use the correct technical terms for each language.

Why L1 matters most: A 13k-star Rust headless browser and a 30k-star browser engine were both missed because queries started at L2. The broad query "headless browser" caught both immediately. Narrow queries are for precision after broad queries establish the landscape.

Query Templates

# L1: Broad (ALWAYS start here) — in your assigned language(s)
gh search repos "[broad-term]" --sort stars --limit 15

# L2: Compound — narrow to domain
gh search repos "[compound-term]" --sort stars --limit 10

# L3: Adjacent — catch overlapping tools
gh search repos "[adjacent-term]" --sort stars --limit 10

# L4: Language-filtered (apply to L1 broad terms)
gh search repos "[broad]" --language rust --sort stars --limit 5
gh search repos "[broad]" --language go --sort stars --limit 5
gh search repos "[broad]" --language zig --sort stars --limit 5

# Code search (cross-language)
gh search code "[pattern]" --language python --limit 10
gh search code "[pattern]" --language typescript --limit 10

Structured API Search (with star threshold)

gh api search/repositories -f q="[broad-query] stars:>100" \
  --jq '.items[:10] | .[] | {name, url: .html_url, description, stars: .stargazers_count}'

gh api search/repositories -f q="[中文关键词] stars:>50" \
  --jq '.items[:10] | .[] | {name, url: .html_url, description, stars: .stargazers_count}'

Execution: Parallel Batching

MUST batch independent gh searches into parallel Bash calls. Do NOT run sequentially.

Search Strategy

  • Broad first, narrow second — L1 establishes the landscape, L2-L4 add precision
  • Search in assigned language(s) — use correct technical terms per language
  • Parallel always — batch all independent gh calls into simultaneous Bash tool invocations
  • Search across multiple programming languages (Python, TypeScript, Go, Java, Rust) — different ecosystems have different strengths
  • Repos with zero topics only match on name + description — broad queries are the only way to find them
  • Follow leads: if a repo references another project, search for that too
  • Check repo activity: prioritize repos updated within last 6 months
  • Max 30 gh CLI calls — think before each one

GitHub research reveals what practitioners actually build, not just what they write about. Prioritize repos with recent activity and meaningful star counts.

Minimum GitHub Coverage

Each researcher agent MUST find at least 5 relevant repos OR document "fewer than 5 exist for [topic] in [language]". If the first gh search repos returns <5 results:

  • Try broader terms (L1)
  • Try adjacent terms (L3)
  • Try gh search code "[pattern]" to find repos by usage
  • Try gh api search/repositories with star threshold lowered

Phase 4: Retrieval & Deep Gathering

Use all available tools and MCP servers to gather information. Do NOT limit to a fixed set.

Step 1: Discover Available Tools

Before fetching content, scan what's available in this session:

  • Check <system-reminder> for listed MCP tools and deferred tools
  • Use ToolSearch with queries like "web", "search", "fetch", "scrape", "crawl", "browse" to find retrieval-capable tools
  • Note all built-in tools that can retrieve external content

Step 2: Classify by Capability

Group every discovered tool by what it can do:

Capability What it does
Search Find URLs/content by query (keyword, semantic, meta-search)
Fetch/Scrape Extract content from URLs (HTML→markdown, JS rendering)
Crawl Follow links, map sites, deep multi-page extraction
Browse Control a real browser (auth, interactive, JS-heavy SPAs)
Geo-target Route requests through specific countries/regions
Bypass Handle anti-bot, CAPTCHAs, rate limits
Docs Query library/framework documentation
Domain-specific Platform extractors, structured data APIs

Step 3: Use Everything, Escalate on Failure

1. Use all search-capable tools to find sources
2. Use all fetch/scrape tools to extract content
   ↓ blocked or JS-rendered?
3. Use browse-capable tools for interactive/JS content
   ↓ anti-bot or geo-restricted?
4. Use bypass/geo-targeting tools if available

Key principle: Try every available tool before concluding content is inaccessible. Different tools succeed on different sites.

Reddit Retrieval (MANDATORY escalation)

If site:reddit.com WebSearch returns empty (common due to API restrictions):

  1. Check if Parallax MCP is available (ToolSearch("parallax reddit"))
  2. If yes: use mcp__parallax__search_reddit or browse_subreddit for subreddit-specific search
  3. If no Parallax: try WebFetch on specific Reddit URLs constructed from the topic (e.g., reddit.com/r/[subreddit]/search?q=[topic])
  4. Document "Reddit inaccessible" in findings if all methods fail — do NOT silently skip

Free Open Platforms (always check)

Domain EN ZH (Chinese; 中文) RU (Russian; русский)
Academic arXiv, bioRxiv, SSRN, PubMed Central, Semantic Scholar, CORE, Unpaywall, OpenAlex CNKI (Chinese name: 中国知网), Wanfang Data (万方数据), Baidu Scholar (百度学术), CQVIP (维普) eLibrary.ru, CyberLeninka, Math-Net.ru
Code/Packages GitHub, GitLab, npm, PyPI, crates.io, pkg.go.dev Gitee, OSChina (Chinese name: 开源中国) GitFlic
Docs/Standards MDN, DevDocs, W3C, IETF RFCs Chinese national standards (GB) GOST standards
Archives/Cache archive.org, Google Cache, Common Crawl web.archive.org (works for .cn) web.archive.org
Q&A Stack Overflow, Stack Exchange, Quora Zhihu (Chinese name: 知乎), SegmentFault, CSDN Q&A Habr Q&A, CyberForum.ru, sql.ru
Forums/Discussion Reddit ⚠️, HN, Lobsters, Tildes, IndieHackers, Product Hunt V2EX, Baidu Tieba (百度贴吧), NodeSeek, Hostloc, 52pojie (吾爱破解) Habr, OpenNET.ru, LOR (linux.org.ru), 4PDA, iXBT
Dev Blogs dev.to, Medium, Hashnode Juejin (Chinese name: 掘金), CSDN blogs, InfoQ CN Tproger, Habr blogs, vc.ru/dev
Data/Datasets Kaggle, HuggingFace, Papers With Code Tianchi (Chinese name: 天池), ModelScope (Chinese name: 魔搭) Kaggle RU community
Patents/Legal Google Patents, USPTO, WIPO CNIPA (China National Intellectual Property Administration) Rospatent (Russian abbreviation: ФИПС)
Competitive Prog LeetCode, HackerRank LeetCode CN, Luogu (Chinese name: 洛谷), AcWing Codeforces, e-olymp
Cloud/Infra Docs AWS, GCP, Azure docs Alibaba Cloud (阿里云, Chinese brand name), Tencent Cloud (腾讯云, Chinese brand name) docs Yandex Cloud docs

These are freely accessible without any tools — just WebFetch the URL. Always check relevant platforms before escalating to paid/bypass tools.

Phase 5: Synthesis & Cross-Reference

  • Verify information across multiple independent sources and languages
  • Check publication dates for currency
  • Identify consensus vs. controversial approaches
  • Note conflicting information or community debates
  • Distinguish stable best practices from experimental approaches
  • Evaluate pros/cons, maturity, security implications, performance characteristics

When findings conflict across languages: Present the tension — different communities optimize for different constraints. A pattern proven at Chinese scale may differ from one optimized for Russian algorithmic elegance or English ecosystem breadth.

Completion Self-Check (MANDATORY before reporting)

Before finalizing your report, verify coverage against the requested sections:

Requested section Covered? Sources (tier) If NO, why
[each section from prompt] Y/N elite/GitHub/official/blog [reason]

If any section is "NO" and iterations remain, run 1-2 more targeted iterations to fill it. If iterations exhausted, document the gap explicitly in the report.

Also verify:

  • At least 1 elite forum source included
  • At least 1 GitHub repo/code reference
  • Every case study has a source URL
  • Every stat/metric has a source URL

Phase 6: Report

For report structure and formatting, read references/report-template.md.

Reports are saved to ./report/YYMMDD-<topic>.md in the current working directory.

MANDATORY: Every claim MUST cite its source. Use inline links [text](url) next to each claim. End the report with a ## Sources section listing all URLs used. No claim without a source — if you can't cite it, don't include it.

Quality Standards

  • Accuracy: Verified across multiple sources and languages
  • Currency: Prioritize last 12 months unless historical context needed
  • Completeness: Cover all requested aspects
  • Actionability: Practical, implementable recommendations
  • Attribution: Every claim cites source with URL. End with full Sources section.
  • Concision: Sacrifice grammar for brevity. List unresolved questions at end.