Skill: autosolutionsai-didac/autosolutions-deep-research (version 1.0.0)

--- name: deep-research description: Conduct deep multi-phase research using parallel subagents and iterative search. Use for deep research requests, comprehensive analysis, competitive intelligence, market research, or thorough investigation of complex topics. --- # Deep Research Skill ## Overview This skill conducts thorough, multi-phase research using parallel subagents and iterative search methodology. It simulates ChatGPT Deep Research and Anthropic Deep Search by breaking complex topics into sub-questions, distributing work across 6-10 parallel research agents, and synthesizing findings into a structured report. ## When to Use Use this skill when the user requests: - Deep research on a topic - Comprehensive analysis - Competitive intelligence - Market research - Thorough investigation (not quick facts) - Multi-angle exploration of complex subjects ## Research Methodology ### Core Principles 1. **Multi-pass queries** — Never one-and-done; iterate based on findings 2. **Source triangulation** — Verify claims across 3-5 independent sources 3. **Primary source hunting** — Find original studies, docs, not just blog posts 4. **Contradiction spotting** — Flag where sources disagree; don't hide uncertainty 5. **Synthesis over summary** — Connect dots, identify patterns, surface insights ### Parallel Agent Architecture For deep research, spawn **6-10 subagents** to explore different angles simultaneously: ``` Research Lead (you) ├── Agent 1: Background & definitions ├── Agent 2: Market/industry landscape ├── Agent 3: Key players/competitors ├── Agent 4: Technology/trends ├── Agent 5: Challenges/risks ├── Agent 6: Opportunities/future outlook ├── Agent 7: Case studies/examples ├── Agent 8: Data/statistics └── Agent 9-10: Specialized deep-dives (as needed) ``` ### Search Tool Strategy Use `web_search` with different modes per phase: | Mode | Use Case | |------|----------| | `deep-reasoning` | Initial exploration, complex queries | | `deep` | Broad topic coverage, 20-30 results | | `neural` | Semantic matching, finding relevant pages | | `fast` | Quick fact-checks, specific lookups | | `instant` | Verifying names, dates, basic facts | Use `web_fetch` to: - Extract full article content from promising URLs - Read primary sources, studies, documentation - Get details that search snippets miss ## Workflow ### Phase 1: Scoping (5 min) 1. **Clarify the topic** — Ask user if the request is ambiguous 2. **Identify sub-questions** — Break the topic into 6-10 research angles 3. **Define success** — What does a good answer look like? Example sub-question breakdown for "AI agent platforms": - What are AI agent platforms and how do they work? - What's the market size and growth trajectory? - Who are the major players (established + startups)? - What technologies power these platforms? - What are the main use cases? - What challenges/limitations exist? - What's the competitive landscape? - What trends are emerging? ### Phase 2: Parallel Research (15-25 min) Spawn subagents with `sessions_spawn` for each research angle: ```bash # Example subagent spawn sessions_spawn( task="Research [specific angle]. Use web_search with mode=deep-reasoning, 20-30 results. Fetch full content from 5-10 key sources. Return: key findings, statistics, quotes with sources, contradictions spotted.", runtime="subagent", mode="run" ) ``` Each subagent should: - Use appropriate `web_search` mode for their angle - Fetch 5-10 full articles with `web_fetch` - Return structured findings with source citations - Flag uncertainties or conflicting information ### Phase 3: Synthesis (10-15 min) As research lead, consolidate findings: 1. **Aggregate results** — Collect all subagent outputs 2. **Identify patterns** — What themes emerge across angles? 3. **Spot contradictions** — Where do sources disagree? 4. **Fill gaps** — Run targeted searches for missing pieces 5. **Verify claims** — Cross-check key statistics across sources ### Phase 4: Report Writing (10 min) Structure the final report as follows: ## Output Format ```markdown # [Research Topic] ## Executive Brief [150-250 words: The 3-5 most important takeaways. Lead with the answer. What should the reader know after finishing this report?] --- ## 1. Background & Context [Foundational information, definitions, why this matters] ## 2. [Key Theme 1] [Deep dive with supporting evidence] ## 3. [Key Theme 2] [Deep dive with supporting evidence] ## 4. [Key Theme 3] [Deep dive with supporting evidence] ## 5. Challenges & Risks [What could go wrong, limitations, open questions] ## 6. Opportunities & Outlook [Future trends, emerging developments, what to watch] ## Key Takeaways - [Bulleted summary of 5-7 most important points] --- ## Sources [Numbered list with full URLs, titles, and 1-line context for each source] 1. [Title](URL) — [Brief context: what this source contributed] 2. [Title](URL) — [Brief context] ... ``` ### Citation Guidelines - **In-text** — Use numbered brackets: [1], [2-4], [5, 7] - **Sources section** — Full URL, title, and 1-line context - **Minimum sources** — 20-30 for deep research - **Quality over quantity** — Prefer primary sources, industry reports, reputable publications ## Tool Usage ### web_search ```bash # Broad exploration web_search query="[topic]" type="deep-reasoning" count=30 freshness="year" # Targeted lookup web_search query="[specific fact]" type="fast" count=10 # Recent developments web_search query="[topic]" type="neural" count=20 freshness="month" ``` ### web_fetch ```bash # Extract full content web_fetch url="https://example.com/article" extractMode="markdown" maxChars=5000 ``` ### sessions_spawn (for parallel research) ```bash # Spawn research subagent sessions_spawn( task="Research [specific angle]. Search with mode=deep-reasoning, 25 results. Fetch 8-10 full articles. Return structured findings with citations.", runtime="subagent", mode="run" ) ``` ## Quality Checks Before delivering the report, verify: - [ ] Executive brief captures the 3-5 most important takeaways - [ ] All major claims have 2+ source citations - [ ] Contradictions/uncertainties are flagged, not hidden - [ ] Sources section has 20-30 entries with full URLs - [ ] Report answers the original question thoroughly - [ ] No obvious gaps in coverage ## Adaptation ### For Quick Research (<10 min) - Skip subagent spawning - Run 3-5 targeted searches yourself - Aim for 10-15 sources - Condense report structure ### For Ultra-Deep Research (60+ min) - Spawn 10-15 subagents - Include primary source documents, academic papers - Add data tables, comparisons, timelines - Include appendix with raw findings ## Notes - **Context efficiency** — Subagents run in isolated sessions; only their findings load into your context - **Parallelism** — Spawn all subagents at once, then `sessions_yield` to wait for completion - **Iterative** — If initial findings reveal new angles, spawn follow-up agents - **Time boxing** — Set `runTimeoutSeconds` on subagents to prevent runaway research


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)