research-lookup
by K-Dense-AIresearch-lookup is a research-lookup skill for current, source-backed answers from web and academic search backends. It routes queries to parallel-cli search, the Parallel Chat API, or Perplexity sonar-pro-search to help with papers, citations, technical evidence, and fact verification. Use it when freshness and source quality matter.
This skill scores 78/100, which means it is a solid listing candidate for Agent Skills Finder. Directory users get a clearly triggerable research-lookup workflow with explicit backend routing, but should still expect some adoption caveats because the repo is mostly a single skill file plus README and does not include an install command or supporting scripts in the repository evidence provided.
- Explicit use cases and triggers for current research, literature review, citations, and technical information lookup.
- Operationally clear backend routing across parallel-cli search, Parallel Chat API, and OpenRouter-backed academic search, reducing guesswork for agents.
- Strong skill body with many headings, constraints, and workflow details; no placeholder markers were present.
- No install command and no support files/scripts are shown in the repo evidence, so setup may require manual interpretation.
- Trust depends on external services and API keys (PARALLEL_API_KEY, OPENROUTER_API_KEY), which may limit immediate use.
Overview of research-lookup skill
What research-lookup does
research-lookup is a research-lookup skill for getting current, source-backed answers from web and academic search backends instead of relying on a static prompt. It is built for people who need up-to-date papers, technical evidence, citations, or quickly verified claims.
Who it fits best
Use research-lookup if you regularly do Web Research, literature checks, competitive technical scanning, or fact verification where freshness matters. It is a strong fit for analysts, researchers, engineers, and writers who need a repeatable research-lookup guide rather than ad hoc prompting.
Why it is different
The main value is backend routing. The skill prefers parallel-cli search for fast, general research, can escalate to the Parallel Chat API for deeper synthesis, and can use Perplexity sonar-pro-search for academic paper searches. That makes the research-lookup skill more useful than a generic “search the web” prompt when query type and source depth change the right tool.
What to watch before install
The tradeoff is dependency and API exposure: parallel-cli is required, and query text may be sent to api.parallel.ai; academic searches may also use OPENROUTER_API_KEY. If you need offline-only research or strict local-only data handling, this is probably not the right skill.
How to Use research-lookup skill
Install and environment setup
For research-lookup install, add the skill to your Claude Code environment from the repo path, then confirm the required backend is available. In practice, expect to set PARALLEL_API_KEY for deep research routing and OPENROUTER_API_KEY only if you want academic paper search through the OpenRouter path.
Start with the right input
The skill works best when your request includes: topic, time window, source preference, and output format. A weak prompt is “find research on batteries.” A stronger research-lookup usage prompt is: “Find 2023–2025 peer-reviewed studies on solid-state battery degradation, prioritize review papers, and return 8 citations with one-line relevance notes.”
Practical workflow
Begin with a focused question, then refine based on what the first search returns. If the topic is broad, ask for a narrowed evidence slice first; if the topic is niche, specify domain terms, methods, or accepted source types. This helps the skill choose between fast search, deep synthesis, and academic lookup without overusing the slower path.
Files to read first
Start with scientific-skills/research-lookup/SKILL.md to understand routing behavior and constraints, then README.md for the simplest usage examples. If you are adapting the skill for another workflow, read any command examples closely and mirror the input style rather than copying the wording.
research-lookup skill FAQ
Is research-lookup only for academic papers?
No. The research-lookup skill covers general current research and technical verification too. It is strongest when the result must be recent, sourceable, and better than a normal chat answer.
When should I not use it?
Do not use it for static knowledge, local project facts, or tasks that do not benefit from live sources. Also avoid it if your process cannot send query text to external services or you do not want API-backed retrieval.
Is it beginner-friendly?
Yes, if you can state a clear question. Beginners get the best results when they include date range, subject area, and the kind of evidence they want. Without that, research-lookup usage tends to become broad and noisy.
How is it different from a normal prompt?
A normal prompt depends on model memory and general reasoning. research-lookup adds retrieval discipline, backend selection, and research-oriented source targeting, which is why it is more reliable for current or citation-heavy work.
How to Improve research-lookup skill
Give the tool decision-ready context
The biggest quality gain comes from better query framing. Include the exact concept, preferred sources, and the kind of answer you need: summary, citations, comparison table, or evidence check. For example, “Compare 2024 studies on retrieval-augmented generation evaluation, prioritize peer-reviewed sources, and flag conflicting findings.”
Reduce ambiguity before the first lookup
Common failure mode: asking for a broad topic with no boundaries. Improve research-lookup results by naming the method, domain, population, date range, or evaluation criterion. “Recent research on batteries” is weak; “2022–2025 papers on lithium-metal battery dendrite suppression in solid electrolytes” is actionable.
Iterate from source quality, not just answer quality
After the first pass, ask for better source selection, missing counterevidence, or a tighter academic subset. If the output is too web-heavy, request peer-reviewed sources; if it is too academic, request practitioner or standards sources. That makes the research-lookup guide more useful on the second run than on the first.