Agent skill
deep-research
Fast research that beats plain websearch — discovers what exists before searching specifics (Landscape Scan), catches recent releases within days/weeks (Recency Pulse + upstream supply chain), and runs parallel queries for multi-angle coverage. Good for everyday research and current-info questions. Use when user requests research, comparison, or "what's the latest on X". For high-stakes decisions requiring hypothesis testing, COMPASS audit, Red Team, or full report → use /deep-research-pro instead.
Install this agent skill to your Project
npx add-skill https://github.com/ThepExcel/agent-skills/tree/main/deep-research
SKILL.md
Deep Research
Better than plain websearch. Faster than full research pipelines.
What makes it better:
- Landscape Scan — discovers what exists before searching specifics (avoids blind spots)
- Recency Pulse — catches releases from last 7-30 days, searches upstream providers
- Parallel search — 2-3 queries at once, multiple angles simultaneously
For rigorous research (hypotheses, COMPASS audit, Red Team, full report) → /deep-research-pro
Step 1: CLASSIFY
| Type | When | Do |
|---|---|---|
| A | Single fact | Search → answer directly |
| B | Multi-fact / comparison | SCAN → RECENCY → SEARCH → Synthesize |
| C | Judgment / recommendation | B + flag uncertainty + note limitations |
Tiers: Quick (5-10 sources) · Standard (10-20 sources)
Step 2: LANDSCAPE SCAN (skip for Type A)
Map what exists before searching specifics. Never use known names in scan queries — you'll miss things that exist but you don't know about yet.
❌ "DeepSeek Qwen performance 2026" ← only finds what you already know
✅ "China open source LLM list 2026" ← discovers the full landscape
Queries (parallel):
WebSearch: "[topic] landscape overview [current year]"
WebSearch: "top [topic] list [current year]"
WebSearch: "[topic] all options [current year]"
Extract entity names → split into Discovered (new) vs Confirmed (updated).
Step 3: RECENCY PULSE (mandatory for tech/AI topics)
Yearly searches miss releases from last week. Downstream product news lags upstream by weeks.
Map supply chain first: Who makes the underlying tech? → Search them directly.
WebSearch: "[topic] latest news [current month] [current year]"
WebSearch: "[upstream provider] latest release [current month] [current year]"
Example — researching "Microsoft Copilot": Upstream = OpenAI + Anthropic → search both directly, don't rely on Microsoft announcements alone.
Flag anything from last 7-30 days as RECENT or BREAKING.
Step 4: SEARCH
Run queries in parallel (single message, multiple tool calls):
WebSearch: "[topic] [current year]"
WebSearch: "[topic] limitations problems"
WebSearch: "[topic] vs alternatives comparison"
Stop when: 3 consecutive searches add <10% new info (saturation) or sources converge on same answer.
URL fallback (403/blocked):
curl -s --max-time 60 "https://r.jina.ai/https://example.com"
Claim confidence:
- C1 (key claims) — need 2+ sources + confidence note:
HIGH / MEDIUM / LOW - C2 (supporting) — citation required
- C3 (common knowledge) — cite only if contested
Never state C1 without citing [N]. If no source found → say so.
Step 5: SYNTHESIZE + SAVE
For each key finding, answer:
- แล้วยังไง (So what)? — why does this matter?
- ต้องทำอะไร (Now what)? — action to take?
If sources conflict: flag explicitly — "Source A says X, Source B says Y — likely because [reason]."
Always save output:
research/[topic-slug]-[YYYY-MM-DD].md
When to Ask vs Just Do
| Ask | Just do |
|---|---|
| Topic too broad → "อยากเน้นมุมไหนคะ?" | Choose search queries |
| Interesting sub-topic found | Format output |
| Sources conflict on key point | Type A questions |
Related Skills
/deep-research-pro— Full pipeline: hypotheses, QUEST queries, COMPASS audit, Red Team, formal report/boost-intel— Stress-test a research finding before making a decision/generate-creative-ideas— Cross-industry creative research (no web search needed)
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