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Research algorithms
Turns a research paper’s mechanism into a practical algorithm evaluation, with explicit assumptions, a simple baseline and a testable implementation boundary.
academic-paper-algorithm-translation
Workflow agents using this skill
When is it useful?
Use it when a promising paper appears relevant, but you need to know whether its results transfer to your application and data.
What does it help you do?
- Explains inputs, data structures and computational cost.
- Checks paper assumptions against real production data.
- Compares a simple baseline with an independently testable implementation.
Try asking this
Evaluate this paper’s cache algorithm against our current LRU cache. Replay the same request trace for both caches. Report hit rate, memory use and the workloads where the proposed algorithm performs worse.
Detailed documentation
Read the skill’s original instructions, examples and checks in their authored language.
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Technical Paper & Algorithm Translation
Research papers and technical blog posts can offer powerful algorithmic breakthroughs (e.g., context-compaction heuristics, scheduling algorithms, graph layout passes, cardinality estimators, or hybrid search ranking), but academic prose and research code require careful translation before production use.
Workflow
- Locate the concrete mechanism behind the terminology (Feynman Principle):
- Strip away mathematical notation inflation and buzzwords; restate the core algorithm in plain data structures, inputs, outputs, and step-by-step operations:
- What is the exact state representation?
- What happens on each update or query step?
- What are the time and space complexities ($O(\cdot)$) on our actual data sizes?
- Strip away mathematical notation inflation and buzzwords; restate the core algorithm in plain data structures, inputs, outputs, and step-by-step operations:
- Audit the paper's assumptions and evaluation setup:
- Check where academic assumptions diverge from production reality:
- Did the paper evaluate on static, clean in-memory datasets while our system handles concurrent, dirty, streaming updates?
- Does the quality gain depend on an expensive pre-computation step, specific model family, or hyperparameter tuning that is fragile in practice?
- Check where academic assumptions diverge from production reality:
- Inspect companion research repositories critically:
- If the authors published a GitHub repo, treat it as a reference specification, rarely as production-ready dependency code:
- Research repos often hardcode file paths, omit error handling, pin outdated dependencies, or lack licenses.
- Verify whether key equations in the paper match the actual lines of code in the repo (often crucial normalization constants or boundary conditions live only in the code).
- If the authors published a GitHub repo, treat it as a reference specification, rarely as production-ready dependency code:
- Distill into a minimal, testable engineering design:
- Compare the paper's approach against a strong, simple baseline (e.g., standard BM25, LRU/LFU, greedy heuristic) on our actual problem scale.
- If the algorithmic improvement is justified, specify a clean, self-contained module with deterministic unit tests and invariant checks rather than importing a fragile research script.
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SKILL.md