| name | deep-gather |
| description | Deep internet gathering — the search-fetch loop, query templates, multi-source/GitHub patterns, and retrieval tools. ONE job: collect raw sources and data from the internet on an assigned topic/language. Run standalone to deep-gather a topic, or as the GATHER-DATA step of lead-researcher's research loop. Does NOT reason, steer, or synthesize — that is lead-researcher's brain. Triggers: 'deep gather', 'gather sources/data on X', 'collect everything about Y'. |
Deep Gather
Internet gathering engine — the search-fetch loop that collects, cross-references, and reports raw sources and data. One job: gather from the internet. It does NOT run hypothesis reasoning or loop steering — the goal-directed reasoning (REASONING/EXPAND/CHECK) is lead-researcher's brain.
| Used | How |
|---|
| Standalone | Invoke directly to deep-gather a topic → returns collected sources + data |
| GATHER-DATA step | lead-researcher's research loop calls it each iteration (via gatherer agents) |
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 (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 (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:
- Elite forums (V2EX, Habr, HN, Lobste.rs, Indie Hackers, linux.do)
- GitHub repos, issues, code
- Official docs (vendor sites, framework docs)
- High-value analysis (arXiv, McKinsey, Deloitte, IDC)
- Blog posts, tutorials (secondary — verify against tier 1-4)
- Content farms (CSDN reposts, 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.
Keyword Variants & Length Cascade (long → short)
GitHub matches your tokens against repo name + description + README — no semantics. A repo is invisible if its text doesn't contain words close to yours. Two ways to miss:
- Different word — the author (or another searcher) picked a synonym you didn't try → generate variants.
- Too many words — your compound phrase is more specific than any repo's name/description → cascade from long to short.
For every concept, build a variant set, then a length cascade. Drop words long → short — each drop widens the net.
| Step | Action | Example (concept: "headless browser") |
|---|
| Variants | Synonyms + the different words an author/user might use | headless browser, browser automation, browserless, puppeteer alternative, 无头浏览器, автоматизация браузера |
| Cascade | Same concept, drop words longest → shortest | ai headless browser automation → headless browser automation → headless browser → browser automation → browser |
| Rule | Why |
|---|
| Never rely on a long compound alone | It is the most brittle query — if the long form is all you run, you miss repos with different word order or terser names |
| Always include the shortest core noun | It casts the widest net and catches zero-topic repos — this is exactly L1 |
| Cover synonyms | Authors and users disagree on words (headless vs browserless, agent vs bot); a missed synonym = a missed field |
| Run the full set in parallel | Longer forms add precision, shorter forms guarantee coverage — batch them, cost is the same |
Query Templates
gh search repos "[broad-term]" --sort stars --limit 15
gh search repos "[compound-term]" --sort stars --limit 10
gh search repos "[adjacent-term]" --sort stars --limit 10
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
gh search code "[pattern]" --language python --limit 10
gh search code "[pattern]" --language typescript --limit 10
Issue & PR Search (MANDATORY — same priority as repo search)
Issues and PRs surface what web search cannot: real breakage reports, workarounds, migration pain, active development signals, and community health.
gh search issues "[topic] broken" --sort updated --limit 10
gh search issues "[topic] blocked" --sort updated --limit 10
gh search issues "[topic] alternative" --sort updated --limit 10
gh search issues "[topic] migration" --sort updated --limit 10
gh search issues "[error-message-or-symptom]" --sort reactions --limit 10
gh search prs "[topic] support" --sort updated --limit 10
gh search prs "[topic] add" --sort updated --limit 10
gh search commits "[topic]" --sort committer-date --limit 10
gh search issues --repo [owner/repo] "bug OR broken OR error" --sort updated --limit 5
gh search prs --repo [owner/repo] --state merged --sort updated --limit 5
What each surfaces that web search misses:
| Command | Signal | Example find |
|---|
gh search issues "[tool] broken" | Recent breakage, reliability | "XHS signature changed, all requests 403" |
gh search issues "[tool] blocked" | Anti-bot changes, access issues | "TikTok msToken expired after 2hrs" |
gh search issues --sort reactions | Community-validated pain points | Most upvoted = most common problems |
gh search prs --state merged | Active maintenance signal | Recent merges = actively maintained |
gh search prs "[feature] add" | New capabilities being developed | "Add comment scraping support" |
gh search commits --sort committer-date | Recency of development | Last commit date = maintenance status |
Issue/PR queries MUST be in assigned language(s):
gh search issues "[工具名] 失败 OR 报错 OR 无法" --sort updated --limit 10
gh search issues "[工具名] 替代 OR 迁移" --sort updated --limit 10
gh search issues "[тема] ошибка OR сломано" --sort updated --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 45 gh CLI calls — budget: ~15 repo/code, ~15 issues/PRs, ~15 targeted/health-checks
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 gatherer 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
Each gatherer agent MUST also run at least 3 issue/PR searches to surface:
- Recent breakage or reliability problems (
gh search issues "[topic] broken OR error" --sort updated)
- Community-validated pain points (
gh search issues "[topic]" --sort reactions)
- Active maintenance signals (
gh search prs --repo [top-repo] --state merged --sort updated)
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):
- Check if Parallax MCP is available (
ToolSearch("parallax reddit"))
- If yes: use
mcp__parallax__search_reddit or browse_subreddit for subreddit-specific search
- If no Parallax: try
WebFetch on specific Reddit URLs constructed from the topic (e.g., reddit.com/r/[subreddit]/search?q=[topic])
- Document "Reddit inaccessible" in findings if all methods fail — do NOT silently skip
Free Open Platforms (always check)
| Domain | EN | ZH (中文) | RU (русский) |
|---|
| Academic | arXiv, bioRxiv, SSRN, PubMed Central, Semantic Scholar, CORE, Unpaywall, OpenAlex | CNKI (中国知网), Wanfang (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 (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 (知乎), SegmentFault, CSDN Q&A | Habr Q&A, CyberForum.ru, sql.ru |
| Forums/Discussion | Reddit ⚠️, HN, Lobsters, Tildes, IndieHackers, Product Hunt | V2EX, 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 (掘金), CSDN blogs, InfoQ CN | Tproger, Habr blogs, vc.ru/dev |
| Data/Datasets | Kaggle, HuggingFace, Papers With Code | Tianchi (天池), ModelScope (魔搭) | Kaggle RU community |
| Patents/Legal | Google Patents, USPTO, WIPO | CNIPA (国家知识产权局) | Rospatent (ФИПС) |
| Competitive Prog | LeetCode, HackerRank | LeetCode CN, Luogu (洛谷), AcWing | Codeforces, e-olymp |
| Cloud/Infra Docs | AWS, GCP, Azure docs | Alibaba Cloud (阿里云), Tencent Cloud (腾讯云) 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:
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.
