research-engineer-scientist-tokens

Original：🇺🇸 English

Translated

Guides research engineering and science on LLM tokens—hypotheses about context use, tokenization, compression, and inference efficiency; rigorous benchmarks (tokens per task, quality–cost Pareto); ablation design; instrumentation and reproducible logs; and research memos that inform product decisions. Use when designing token-efficiency experiments, measuring context utilization, comparing compression or routing methods, analyzing tokenizer effects, or writing technical reports on token/cost trade-offs—not for phased cost roadmaps and owners (ai-token-improvement-plan-engineer), production context pipeline implementation (ai-context-engineer), single-prompt edits (prompt-engineer), general non-token AI research (ai-researcher), or shipping features (ai-engineer).

13installs

Sourcedaemon-blockint-tech/agentic-enteprises-skill

Added on2026-05-20

NPX Install

npx skill4agent add daemon-blockint-tech/agentic-enteprises-skill research-engineer-scientist-tokens

SKILL.md Content

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Research Engineer / Research Scientist, Tokens

When to Use

Frame research questions on tokens, context length, or inference cost
Design experiments with baselines, ablations, and statistical rigor
Build benchmarks for tokens-per-successful-task, effective context, cache leverage
Measure tokenizer and formatting effects on length and model behavior
Evaluate compression, summarization, routing, or distillation for token savings
Analyze long-context phenomena (needle, lost-in-middle, attention budget)
Write research memos with reproducible methods and honest limitations
Translate findings into actionable thresholds for engineering and product

When NOT to Use

Executive token reduction program with phased rollout →
```
ai-token-improvement-plan-engineer
```
Implement context packing, compaction code paths →
```
ai-context-engineer
```
Rewrite one production prompt →
```
prompt-engineer
```
General literature survey unrelated to tokens →
```
ai-researcher
```
Production RAG/agent deployment →
```
ai-engineer
```
Classical ML without LLM token focus →
```
data-scientist
```

Related skills

Need	Skill
General research methodology	`ai-researcher`
Cost improvement program / roadmap	`ai-token-improvement-plan-engineer`
Production context assembly	`ai-context-engineer`
Prompt wording and eval harness	`prompt-engineer`
RAG and agent runtime build	`ai-engineer`
Statistical testing and cohort analysis	`data-scientist`
Adversarial robustness of compressed context	`ai-redteam`
Commercial AI architecture	`applied-ai-architect-commercial-enterprise`

Core Workflows

1. Research framing (tokens)

Hypothesis, metrics, baselines, budget.

See
references/research_framing_tokens.md
.

2. Measurement and instrumentation

Token accounting, logging, fair comparison.

See
references/measurement_instrumentation.md
.

3. Experiment design and ablations

Controls, sweeps, power, stopping rules.

See
references/experiment_design_ablations.md
.

4. Context, tokenization, and long-context

Tokenizer, placement, window effects.

See
references/context_tokenization_longcontext.md
.

5. Compression and efficiency methods

Summarization, routing, distillation research.

See
references/compression_efficiency_methods.md
.

6. Reproducibility and research reporting

Memos, artifacts, handoff to engineering.

See
references/reproducibility_reporting.md
.

Outputs

Pre-registration / experiment plan — hypothesis, metrics, stop criteria
Results table — mean ± CI; tokens and quality side by side
Pareto chart narrative — quality vs tokens at operating points
Ablation appendix — what mattered, what did not
Research memo — conclusion, limits, recommended next build
Artifact bundle — configs, seeds, eval scripts, hashed datasets

Principles

Report tokens and quality together — never optimize one without the other
Match tokenizer and model — counts from the deployment tokenizer/API
Control confounds — temperature, system prompt, tool schemas held fixed across arms
Pre-register primary metric — avoid p-hacking across slice metrics
Separate science from rollout — research recommends;
```
ai-token-improvement-plan-engineer
```
owns program