Deep Research Skill

Conduct rigorous, multi-part research on a complex topic producing a report grounded entirely in confirmed sources. No design assumptions before research is complete. All claims backed by source links.

Skill Files

This skill is split across files — read the relevant ones before proceeding:

File	Purpose
security.md	Full template and agent prompts for security research mode
techtrend.md	Full template and agent prompts for tech trend / ecosystem research mode
report-template.html	HTML report template — use when user requests an HTML output

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Step 1: Detect Mode

Determine research mode from the query before reading any template:

Mode	Trigger keywords	Template to read
security	security, threat, CVE, attack, defense, vulnerability, exploit, risk, malware	Read security.md
techtrend	trend, forecast, ecosystem, landscape, technology, hardware, market, adoption	Read techtrend.md
Ambiguous	Neither clearly applies	Ask: "Is this a security analysis or a technology trend/ecosystem research?"

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Step 2: Read the Template

After detecting mode, read the appropriate template file in full before writing the research plan or launching agents. The template files contain:

• The 5-part structure for that mode
• Per-part research questions and source guidance
• Agent prompt scaffolding
• Lessons learned specific to that mode

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Step 3: Execute

Follow the execution steps in the template. The core workflow is the same for both modes:

1. Write RESEARCH_PLAN.md in a new {topic}_{YYYYMM}/ folder
2. Launch 5 parallel background agents (one per part)
3. Acknowledge each agent as it completes with a key findings summary
4. After all 5 complete: read all raw files, compile RESEARCH_REPORT.md
5. If HTML output requested: use report-template.html as the base

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Universal Rules (apply to both modes)

Source quality

• Specs/products: Official vendor docs, press releases, spec sheets
• CVEs/security: NVD, MITRE, vendor advisories, Black Hat/DEF CON/USENIX papers
• Academic: arXiv, NeurIPS/ICLR/CVPR/ACL proceedings, OpenReview
• Market data: Gartner, IDC, Forrester, MarketsandMarkets, Crunchbase
• Regulatory: EUR-Lex, NIST, CISA, Federal Register, national AI laws
• Benchmarks: MLCommons/MLPerf, HuggingFace leaderboards, official vendor disclosures
• Do NOT cite: Wikipedia, unattributed blogs, secondary summaries

Agent instructions (every agent must)

1. Fetch and READ actual URLs — do not rely on training data alone
2. Note publication dates — distinguish confirmed vs. announced vs. speculative
3. Save raw output to {folder}/raw_research/XX_topic.md
4. Target 2,000+ words with real data, tables, and source URLs

File structure

{topic}_{YYYYMM}/
├── RESEARCH_PLAN.md
├── RESEARCH_REPORT.md
├── report.html            # optional, if HTML requested
└── raw_research/
    ├── 01_*.md
    ├── 02_*.md
    ├── 03_*.md
    ├── 04_*.md
    └── 05_*.md

Common errors to avoid

1. Wrong platform ID: Fetch the actual product website before writing the plan
2. Shallow agents: Anchor every agent with 3–5 specific URLs to fetch first
3. Premature design (security mode): Do not write Part 4 before Parts 1–3 are reviewed
4. Fixed dimensions (techtrend mode): Parts 2–4 are defined per-topic in the plan, not preset
5. Blocked sources: Chinese sources behind auth walls — search for equivalent open-web sources
6. Context length: Raw research files can be 5,000–7,000 words each — read them carefully

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Example Invocations

# Security (auto-detected)
/deepresearch security for personal AI endpoint agents including OpenClaw and Claude Code
/deepresearch supply chain attacks on npm packages
/deepresearch quantum-safe cryptography for financial services

# Tech trend (auto-detected)
/deepresearch endpoint LLM ecosystem — hardware, models, runtimes, applications
/deepresearch autonomous vehicle software stack trends and 2030 forecast
/deepresearch edge AI chip market landscape

# With HTML output
/deepresearch endpoint LLM ecosystem output: html