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name: ai-researcher description: | Guides AI research work—literature reviews, hypothesis formation, experiment design, benchmarking, reproducibility, and synthesis of papers and empirical results for technical decisions. Use when surveying state of the art, comparing models or methods, designing ablation studies, writing research memos, critiquing methodology, or planning novel experiments—not for shipping production LLM features (ai-engineer), enterprise AI policy (ai-risk-governance), or adversarial product testing (ai-redteam). Token efficiency experiments and tokens-to-success benchmarks: research-engineer-scientist-tokens. Safety classifier and harm-benchmark research: ml-research-engineer-safeguards. RL training systems engineering: ml-systems-engineer-rl-engineering.

AI Researcher

When to Use

• Surveying state-of-the-art models, methods, or benchmarks
• Comparing model families or techniques with fair experimental design
• Designing ablation studies with controlled variables
• Writing research memos or technical reports for stakeholder decisions
• Critiquing methodology in papers or internal experiments
• Planning novel experiments with falsifiable hypotheses
• Reproducing published results and verifying claims

When NOT to Use

• Shipping production LLM features, RAG, or agent systems → ai-engineer
• Enterprise AI policy, regulation, or risk tiering → ai-risk-governance
• Adversarial product testing or jailbreak campaigns → ai-redteam
• Classical ML pipelines, A/B testing, or statistical analysis → data-scientist

Related skills

Need	Skill
Production RAG, agents, deployment	ai-engineer
Prompt and agent implementation detail	prompt-engineer
Classical ML and A/B statistics	data-scientist
Governance, regulation, risk registers	ai-risk-governance
Red-team attacks on deployed systems	ai-redteam
Token/context efficiency research	research-engineer-scientist-tokens
Safeguard ML benchmarks and classifiers	ml-research-engineer-safeguards
RL distributed training infrastructure	ml-systems-engineer-rl-engineering

Core Workflows

1. Research question framing

1. Convert vague ask into falsifiable question
2. Define scope: task, data regime, compute budget, timeline
3. List baselines that must be beaten or matched
4. Specify primary and secondary metrics
5. Document assumptions and out-of-scope items

See references/research_framing.md for question templates and hypothesis types.

2. Literature review

Process:

1. Search: arXiv, ACL Anthology, OpenReview, major labs' blogs
2. Screen by relevance, recency, citation quality
3. Extract: problem, method, data, metrics, limitations
4. Synthesize themes and open gaps
5. Cite primary sources; avoid over-relying on secondary summaries

See references/literature_review.md for screening matrix and synthesis outline.

3. Experimental design

Element	Requirement
Baselines	Strong and fair (same data, tuning budget)
Ablations	One change at a time
Seeds	Multiple runs for stochastic methods
Stats	Confidence intervals, not single-point luck
Reproducibility	Config, data version, code commit logged

See references/experiment_design.md for power analysis pointers and leakage checks.

4. Benchmarking and analysis

• Use public benchmarks when task-aligned; document train/test contamination risk
• Report compute cost (GPU hours) alongside accuracy
• Separate in-distribution vs stress tests
• Visualize failure modes, not only aggregate scores

See references/benchmarking.md for leaderboard caveats and custom eval sets.

5. Research communication

Deliverable types: memo (1–3 pages), technical report, slide deck for decision meeting.

Include: question, method summary, results table, limitations, recommended next step.

See references/research_writing.md for memo structure and peer-review checklist.

When to load references

• Question and hypothesis → references/research_framing.md
• Literature survey → references/literature_review.md
• Experiments → references/experiment_design.md
• Benchmarks → references/benchmarking.md
• Writing → references/research_writing.md