Research Skill

General research -> human-readable structured report.

Input: topic (natural language) + optional depth (--quick / default / --deep) Output: .hypercore/research/[NN].topic_summary.md

<trigger_conditions>

Trigger	Action
`/research AI agent framework comparison`	Technical comparison research
`/research --deep Korea SaaS market trends`	Deep market research
`/research --quick WebSocket vs SSE`	Fast technical comparison
"Research X for me"	Clarify topic, then execute

If ARGUMENT is missing, ask immediately: "What topic should I research?"

</trigger_conditions>

<depth_levels>

Mode	quick	standard (default)	deep
Query count	3-5	5-10	10-15
Agents	researcher 2 + explore 0-1	researcher 3-4 + explore 0-1	researcher 4-5 + explore 1 + MCP
Iterative pass	No	No	Yes
Minimum sources	5	10	20+
Report size	500-1000 chars	1500-3000 chars	3000-6000 chars

</depth_levels>

<topic_classification>

Type	Keywords	Channels
Technical comparison	vs, compare	WebSearch + explore(gh)
Market/trend	market, trend	WebSearch + Firecrawl
Competitor analysis	competitor, alternatives	WebSearch + GitHub MCP
Academic/concept	principle, paper	WebSearch(arXiv) + WebFetch
Internal project	our code	explore + Grep
Library/package	`package@version`	Delegate to docs-fetch

</topic_classification>

<mandatory_reasoning>

Mandatory Sequential Thinking

Always use sequential-thinking for Phase 1 (query strategy design).
For deep mode, also use sequential-thinking in Phase 3 (gap analysis and second-pass query planning).
Include current year (2026) in recency-sensitive query sets.
Do not produce conclusions without explicit structured reasoning.

</mandatory_reasoning>

<parallel_agent_execution>

Use Agent Teams first when 3+ workers are needed.
Fallback to parallel Task calls when Agent Teams is unavailable.
quick mode (<=2 workers) may run direct parallel tasks.

</parallel_agent_execution>

Phase	Task	Tool
0	Parse input + detect MCP + classify topic	ToolSearch
1	Build search strategy	Sequential Thinking (2 steps)
2	Parallel collection	researcher + explore + MCP
3	Gap analysis + second-pass collection (deep only)	analyst -> researcher
4	Build report	general-purpose
5	Save + return concise summary	Write

Phase 1 requirements

Define 3-5 core research questions
Define scope (time/region/language)
Generate bilingual query set when useful
Assign channels/agents intentionally

Phase 4 writing principles

Conclusion first (pyramid principle)
Every key claim must include source URL
Progressive disclosure (summary -> detail)
Use comparison tables where relevant

<report_template>

# [Topic] Research Report

> Date: YYYY-MM-DD | Depth: quick/standard/deep | Sources: N reviewed, M cited

## Executive Summary
[250-400 chars, conclusion first]

## 1. Research Scope
### 1.1 Background
### 1.2 Scope
### 1.3 Method

## 2. Key Findings
### 2.1 [Finding 1]
Core: [one-line summary]
Details: ...
Source: [Title](URL)

## 3. Comparative Analysis (if needed)
| Criteria | A | B | C |
|------|---|---|---|

## 4. Trends and Implications

## 5. Conclusion and Recommendations

## References
- [Title](URL)

</report_template>

Item	Required
ARGUMENT	Ask immediately if missing
Strategy	Sequential-thinking trace for query strategy
Sources	quick 5+, standard 10+, deep 20+
Recency	Include year/date awareness in source checks
Output	Executive summary + sources + recommendations
Save	`.hypercore/research/[NN].*.md`

Forbidden:

Claims without sources
Comparison conclusions without comparison evidence
Exiting without saving output