cyberelf/deepresearch

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 | | law-policy.md | Full template and agent prompts for law, policy, regulatory, and compliance 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 | | law-policy | law, policy, regulation, compliance, legal requirement, statutory, retention, audit trail, recordkeeping, regulator, licensee, service provider, data residency, data protection, privacy, telecom law, cybersecurity law | Read law-policy.md | | Ambiguous | None clearly applies, or multiple modes are plausible | Ask: "Is this a security analysis, technology trend/ecosystem research, or law/policy compliance 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 all 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 all 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
• Law/policy: official gazettes, government legal portals, regulator websites, ministry publications, court/tribunal decisions, official consultation papers
• 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. Legal status confusion (law-policy mode): never mix binding law, proposed rules, regulator guidance, unofficial translations, and vendor summaries without labeling them
6. Blocked sources: Chinese sources behind auth walls — search for equivalent open-web sources
7. 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

# Law/policy (auto-detected)
/deepresearch data residency laws for financial SaaS in Singapore, Indonesia, and Malaysia
/deepresearch firewall log retention compliance requirements in Thailand and Turkiye
/deepresearch EU AI Act obligations for enterprise AI coding assistants

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