/research-explorer

name: research-explorer
description: Use when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.

Research Explorer

Overview

Research-topic exploration SKILL. Takes a broad direction, performs multi-dimensional web research with the agent's own WebSearch / WebFetch tools, and produces three structured Markdown deliverables. **Single stage, full quality from the start.** No Python runtime, no LLM SDK.

When to Use

• User says "I want to research X" without a specific topic.
• User wants to know "what are the hot topics in X".
• User needs help narrowing a broad field into 5–10 candidate topics.
• User asks for "research landscape overview".

When NOT to Use

• User already has a specific research question → use `literature-survey` or `paper-writer`.
• User wants a quick fact-check → use WebSearch directly.

Workflow

Step 1 — Understand the direction

Confirm with the user:

• **Direction** — the broad area of interest (e.g., "federated learning", "NLP for healthcare").
• **Constraints** — theory vs. applied, specific methods, target venue, compute budget, time horizon.
• **Language** — default English in conversation; reports in English unless the user requests otherwise.

Step 2 — Set up the run directory

DIRECTION="<direction>"
SLUG=$(python3 -c "import re,hashlib,sys; t=sys.argv[1]; n=re.sub(r'[\\s_]+','-',re.sub(r'[^\\w\\s-]','',t.lower().strip())).strip('-')[:40].rstrip('-'); h=hashlib.sha1(t.encode()).hexdigest()[:8]; print(f'{n}-{h}')" "$DIRECTION")
TS=$(date +%Y-%m-%d_%H%M%S)
RUN=output/research-explorer/$SLUG/$TS

mkdir -p "$RUN"
ln -sfn "$TS" "output/research-explorer/$SLUG/latest"

In commands below `$RUN` = `output/research-explorer/<slug>/latest`.

Step 3 — Multi-dimensional exploration

Run **WebSearch** across the following dimensions (one query per dimension, more if returns are thin):

1. **Hot topics** — "<direction> 2024 2025 hot topics" / "recent advances". 2. **Open problems** — "<direction> open problems" / "challenges". 3. **Surveys** — "<direction> survey 2024" / "<direction> review". 4. **Benchmarks** — "<direction> benchmark" / "<direction> evaluation dataset". 5. **Applications** — "<direction> applications" / "<direction> industry use cases". 6. **Cross-field** — "<direction> + <adjacent field>" (pick 1–2 adjacent fields). 7. **Recent breakthroughs** — papers from the last 6–12 months at top venues.

For each kept candidate, **WebFetch** the abstract URL to extract canonical title / authors / year / venue. Persist intermediate notes to `$RUN/search_notes.md` after every dimension so the work resumes cleanly.

Step 4 — Produce the three deliverables

Write these in `$RUN/`:

4.1 `research_exploration.md`

Structured analysis containing:

• **Direction recap & constraints**.
• **Landscape map** — main subfields and the relationships between them.
• **5–10 candidate topics**, each with:
• Title (specific enough to be a paper title).
• Motivation (why this matters now).
• Innovation angle (what would be new).
• Feasibility score (low / medium / high) with a brief justification (data availability, compute requirements, prior work density).
• Risk / open question.
• **Recommendation** — which 1–3 the user should pursue and why.

4.2 `topic_matrix.md`

A hierarchical Markdown outline of the topic space:

# <Direction>
## Subfield A
### Topic A.1
### Topic A.2
## Subfield B
### Topic B.1

This file is consumable by the `mindmap-render` skill to produce a visual mindmap.

4.3 `literature_pre_survey.md`

A pre-survey table of **20–30 representative works** discovered above, with columns: title, authors, year, venue, URL, one-sentence relevance note. Every entry must have a URL the agent fetched in this session.

Step 5 — Optional handoff

If the user picks a topic, suggest the next skill:

• For a paper: the `paper-writer` skill (using the chosen topic).
• For a survey: the `literature-survey` skill.
• For an experiment package: the `experiment-suite` skill.
• For a visual topic map: the `mindmap-render` skill consuming `topic_matrix.md`.

Cross-skill data flow (path convention)

A downstream skill can locate this exploration via the slug:

• `output/research-explorer/<slug>/latest/topic_matrix.md`
• `output/research-explorer/<slug>/latest/literature_pre_survey.md`

If the user picks one topic from the matrix, downstream skills compute their own slug from the **topic** (not the original direction), so the slug paths diverge from this skill onward — which is correct.

Important rules

• **No LLM SDK in this skill.** Just a procedure + this `SKILL.md`.
• **Candidates are suggestions, not guaranteed novel** — the user must verify originality before committing.
• **Feasibility scores are heuristic** — flag uncertainty explicitly when relevant.
• Every literature entry must have a URL fetched in this session; no memory-only entries.