--- name: Research Explorer slug: research-explorer category: AI Engineering description: Research Explorer helps turn a broad research direction into candidate topics with feasibility and innovation scoring. It also gathers a 20–30 paper pre-survey when the user needs a research landscape overview. github: "https://github.com/ai4s-research/ai4s-skills/tree/main/skills/research-explorer" language: Python stars: 204 forks: 19 install: "npx degit https://github.com/ai4s-research/ai4s-skills/tree/main/skills/research-explorer ~/.claude/skills/research-explorer" installs_to: ~/.claude/skills/research-explorer source_path: skills/research-explorer/SKILL.md collection_size: 7 category_size: 3101 collection_url: "https://dirskills.com/collections/ai4s-research/ai4s-skills" added: 2026-09-05T05:29:14.886Z last_synced: 2026-09-05T05:29:14.886Z canonical_url: "https://dirskills.com/skills/research-explorer" --- # Research Explorer Research Explorer helps turn a broad research direction into candidate topics with feasibility and innovation scoring. It also gathers a 20–30 paper pre-survey when the user needs a research landscape overview. **Install:** ```bash npx degit https://github.com/ai4s-research/ai4s-skills/tree/main/skills/research-explorer ~/.claude/skills/research-explorer ``` ## README # 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="" 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 `$RUN` = `output/research-explorer//latest`. ### Step 3 — Multi-dimensional exploration Run **WebSearch** across the following dimensions (one query per dimension, more if returns are thin): 1. **Hot topics** — " 2024 2025 hot topics" / "recent advances". 2. **Open problems** — " open problems" / "challenges". 3. **Surveys** — " survey 2024" / " review". 4. **Benchmarks** — " benchmark" / " evaluation dataset". 5. **Applications** — " applications" / " industry use cases". 6. **Cross-field** — " + " (pick 1–2 adjacent fields). 7. **Recent breakthroughs** — papers from the last 6–12 months at top venues. For each kept candidate, **WebFetch** the abstract URL to extract canonical title / authors / year / venue. Persist intermediate notes to `$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: ``` # ## 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//latest/topic_matrix.md` - `output/research-explorer//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.