exa-search
The exa-search skill provides web search and URL content extraction optimized for scientific and technical research using Exa's semantic and keyword indexing. Use this skill when users need to find current information on a topic, research scholarly sources with optional academic filtering, or fetch and batch-extract content from specific URLs including academic PDFs. It excels at prioritizing peer-reviewed journals, preprints, and institutional sources over general web results through category and domain filtering options.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/exa-search && cp -r /tmp/exa-search/skills/exa-search ~/.claude/skills/exa-searchSKILL.md
# Exa Web Toolkit A skill for web-powered research tasks backed by [Exa](https://exa.ai): web search and URL extraction. Exa's index combines high-quality keyword and semantic retrieval, which makes it well-suited to scientific, technical, and conceptual queries. ## Routing — pick the right capability Read the user's request and match it to one of the capabilities below. Read the corresponding reference file for detailed instructions before running commands. | User wants to... | Capability | Where | |---|---|---| | Look something up, research a topic, find current info | **Web Search** | `references/web-search.md` | | Fetch content from a specific URL (webpage, article, PDF) | **Web Extract** | `references/web-extract.md` | | Install or authenticate | **Setup** | Below | ### Decision guide - **Default to Web Search** for topic lookups, research questions, or "what is X?" queries. When the topic is scientific or technical, pass `--category "research paper"` to bias toward scholarly sources, and/or an academic `--include-domains` allowlist. See `references/web-search.md` for the two-pass academic strategy. - **Use Web Extract** when the user provides a URL or asks you to read/fetch a specific page. Prefer this over the built-in WebFetch for batch extraction (multiple URLs in one call) and for academic PDFs. ### Academic source priority For technical or scientific queries, prefer academic and scientific sources: - Peer-reviewed journal articles and conference proceedings over blog posts or news - Preprints (arXiv, bioRxiv, medRxiv) when peer-reviewed versions aren't available - Institutional and government sources (NIH, WHO, NASA, NIST) over commercial sites - Primary research over secondary summaries Two levers to steer Exa toward scholarly content: 1. `--category "research paper"` biases retrieval toward scholarly sources. 2. `--include-domains` with a scholarly allowlist (arxiv.org, nature.com, pubmed.ncbi.nlm.nih.gov, etc.) restricts the domain pool. Combine both for strictly academic results. See `references/web-search.md` for the full pattern. When citing academic sources, include author names and publication year where available (e.g., [Smith et al., 2025](url)) in addition to the standard citation format. If a DOI is present, prefer the DOI link. --- ## Setup This skill uses the [`exa-py`](https://github.com/exa-labs/exa-py) Python SDK. The scripts in `scripts/` declare their dependencies via PEP 723 inline metadata, so you can run them directly with `uv run` without a separate install step: ```bash uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" --help ``` If you prefer a persistent install: ```bash uv pip install "exa-py>=1.14.0" ``` ### Authentication All commands read the API key from the `EXA_API_KEY` environment variable. Get your Exa API key at [dashboard.exa.ai/api-keys](https://dashboard.exa.ai/api-keys). First, check if a `.env` file exists in the project root and contains `EXA_API_KEY`. If so, load it: ```bash dotenv -f .env run -- uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" "your query" ``` If `dotenv` isn't available, install it: `uv pip install python-dotenv[cli]`. If there's no `.env`, export the key for the session: ```bash export EXA_API_KEY="your-key" ``` Verify by running any script with `--help` — it will exit cleanly if the key is set and auth-check runs only when a real query is made. ### Tracking header Every script in this skill sets the `x-exa-integration` request header to `k-dense-ai--scientific-agent-skills` so Exa can attribute usage from the K-Dense AI scientific-agent-skills repo to this integration. Do not remove or rename this header when adapting the scripts. --- ## Files in this skill - `SKILL.md` — this file (routing and setup) - `references/web-search.md` — detailed web search reference with academic strategy - `references/web-extract.md` — URL content extraction reference - `scripts/exa_search.py` — CLI wrapper around `client.search_and_contents` - `scripts/exa_extract.py` — CLI wrapper around `client.get_contents`
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.