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daemon-blockint-techdaemon-blockint-tech/Agentic-Enteprises-Skill/ai-researcher/SKILL.md

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.

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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
  1. Distinctive:

    Document assumptions and out-of-scope items

    line 54
  2. Distinctive:

    Search: arXiv, ACL Anthology, OpenReview, major labs' blogs

    line 62
  3. Distinctive:

    Use public benchmarks when task-aligned.

    line 84

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

NeedSkill
Production RAG, agents, deploymentai-engineer
Prompt and agent implementation detailprompt-engineer
Classical ML and A/B statisticsdata-scientist
Governance, regulation, risk registersai-risk-governance
Red-team attacks on deployed systemsai-redteam
Token/context efficiency researchresearch-engineer-scientist-tokens
Safeguard ML benchmarks and classifiersml-research-engineer-safeguards
RL distributed training infrastructureml-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

ElementRequirement
BaselinesStrong and fair (same data, tuning budget)
AblationsOne change at a time
SeedsMultiple runs for stochastic methods
StatsConfidence intervals, not single-point luck
ReproducibilityConfig, 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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