ai-researcher
Skill8 starsChanged 6 months agoMITView on GitHub
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.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- rade-nugroho/Agentic-Enteprises-Skillword for word, first seen May 20, 2026
Other versions1 project uses a version of this.
Word for word · 1
- rade-nugroho/Agentic-Enteprises-Skill★ 9100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository197 other files it gives its agents.
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 54
Document assumptions and out-of-scope items
- Distinctive: line 62
Search: arXiv, ACL Anthology, OpenReview, major labs' blogs
- Distinctive: line 84
Use public benchmarks when task-aligned.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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
- Convert vague ask into falsifiable question
- Define scope: task, data regime, compute budget, timeline
- List baselines that must be beaten or matched
- Specify primary and secondary metrics
- Document assumptions and out-of-scope items
See references/research_framing.md for question templates and hypothesis types.
2. Literature review
Process:
- Search: arXiv, ACL Anthology, OpenReview, major labs' blogs
- Screen by relevance, recency, citation quality
- Extract: problem, method, data, metrics, limitations
- Synthesize themes and open gaps
- 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
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