research-explorer
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.
Profile →npx skills add ai4s-research/ai4s-skills --skill research-explorer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash DIRECTION="<direction>" SLUG=$(python3 -c "import
- Overview
- When to Use
- When NOT to Use
- Workflow
- Step 1 — Understand the direction
- Step 2 — Set up the run directory
- Step 3 — Multi-dimensional exploration
- Step 4 — Produce the three deliverables
- Step 5 — Optional handoff
- Cross-skill data flow (path convention)
- Important rules
mkdir -p "$RUN" ln -sfn "$TS" "output/research-explorer/$SLUG/latest"
What does the research-explorer skill do?
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.
How do I install it?
Run `npx skills add ai4s-research/ai4s-skills --skill research-explorer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ai4s-research/ai4s-skills, a repository with 163 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.