deep-research

SKILL.md

Deep Research

Better than plain websearch. Faster than full research pipelines.

What makes it better:

  1. Landscape Scan — discovers what exists before searching specifics (avoids blind spots)
  2. Recency Pulse — catches releases from last 7-30 days, searches upstream providers
  3. 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)
Weekly Installs
53
GitHub Stars
16
First Seen
Jan 23, 2026
Installed on
opencode48
codex47
gemini-cli46
cursor44
github-copilot43
kimi-cli43