Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Lord1Egypt/scientific-agent-toolkit --skill parallel-webgit clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/parallel-web)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/parallel-web"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/parallel-web/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/parallel-web"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/parallel-web.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00197 | $0.01312 |
| Opus 5 | $0.00098 | $0.00656 |
| Sonnet 5 | $0.00039 | $0.00262 |
| Haiku 4.5 | $0.00020 | $0.00131 |
Grade C, and why
parallel-web scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://parallel.ai/install.sh | bash Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://parallel.ai/install.sh | bash This is a copy
91% identical to parallel-web — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Web Toolkit
A unified skill for all web-powered tasks: searching, extracting, enriching, and researching — with academic and scientific sources as the default priority.
Routing — pick the right capability
Read the user's request and match it to one of the capabilities below. For web search, extract, enrichment, and deep research, read the corresponding reference file for detailed instructions.
| 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 |
| Add web-sourced fields to a list of companies/people/products | Data Enrichment | references/data-enrichment.md |
| Get an exhaustive, multi-source report (user says "deep research", "exhaustive", "comprehensive") | Deep Research | references/deep-research.md |
| Install or authenticate parallel-cli | Setup | Below |
| Check status of a running research/enrichment task | Status | Below |
| Retrieve completed research results by run ID | Result | Below |
Decision guide
- Default to Web Search for a single lookup, research question, or "what is X?" query. It's fast and cost-effective. When the query touches a scientific or technical topic, include academic domains (see
references/web-search.md) to surface peer-reviewed and preprint sources alongside general results. - 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 tool. Particularly useful for extracting full text from academic PDFs, preprint servers, and journal articles.
- Use Data Enrichment when the user has multiple entities (a CSV, a list of companies/people/products, or even a short inline list) and wants to find or add the same kind of information for each one. The key signal is a repeated lookup across a set of items — e.g., "find the CEO for each of these companies" or "get the founding year for Apple, Stripe, and Anthropic." Even if the user doesn't say "enrich," use
parallel-cli enrichwhenever the task is the same query applied to multiple entities. Do NOT use Web Search in a loop for this — the enrichment pipeline handles batching, parallelism, and structured output automatically. - Use Deep Research only when the user explicitly asks for deep, exhaustive, or comprehensive research. It is 10-100x slower and more expensive than Web Search — never default to it. Deep research is especially valuable for literature reviews and multi-paper synthesis.
- If
parallel-cliis not found when running any command, follow the Setup section below.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 104 lines · 197 tokens per session scan C b66fe8a4c0b4
parallel-web is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (2 stars, last pushed 3mo ago), licensed MIT. It adds 197 tokens to every session and 1,312 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). It is 91% identical to parallel-web, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
decoupler
Use for any task involving the decoupler library — inferring biological activity/enrichment scores from omics data (bulk, single-cell, spatial). Triggers on estimating transcription factor (TF) activity, pathway activity, or gene-set enrichment from an AnnData/DataFrame; running ulm, mlm, ora, gsea, gsva, aucell…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-phylogenetics
Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 2 maximum-likelihood inference with model selection and bootstraps, FastTree for large datasets, then visualize with ETE3 or FigTree. Use when reconstructing trees from sequences (FASTA) for…
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…
alterlab-pufferlib
Scales reinforcement learning with PufferLib — high-throughput parallel training (PuffeRL), vectorized environments, and native multi-agent systems achieving 2-10x speedups over standard implementations. Use when scaling RL to millions of steps per second, running vectorized or multi-agent setups, building custom…
alterlab-shap
Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing…