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 silverstein/claude-scientific-skills-desktop --skill perplexity-searchgit clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktopWrote 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/silverstein/claude-scientific-skills-desktop/perplexity-search)<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/perplexity-search"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/perplexity-search/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/silverstein/claude-scientific-skills-desktop/perplexity-search"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/perplexity-search.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 | $0.00099 | $0.03171 |
| Opus 5 | $0.00049 | $0.01586 |
| Sonnet 5 | $0.00020 | $0.00634 |
| Haiku 4.5 | $0.00010 | $0.00317 |
Grade A, and why
perplexity-search scanned grade A with 0 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 9d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to perplexity-search — 6 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 — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perplexity Search
Overview
Perform AI-powered web searches using Perplexity models through LiteLLM and OpenRouter. Perplexity provides real-time, web-grounded answers with source citations, making it ideal for finding current information, recent scientific literature, and facts beyond the model's training data cutoff.
This skill provides access to all Perplexity models through OpenRouter, requiring only a single API key (no separate Perplexity account needed).
When to Use This Skill
Use this skill when:
- Searching for current information or recent developments (2024 and beyond)
- Finding latest scientific publications and research
- Getting real-time answers grounded in web sources
- Verifying facts with source citations
- Conducting literature searches across multiple domains
- Accessing information beyond the model's knowledge cutoff
- Performing domain-specific research (biomedical, technical, clinical)
- Comparing current approaches or technologies
Do not use for:
- Simple calculations or logic problems (use directly)
- Tasks requiring code execution (use standard tools)
- Questions well within the model's training data (unless verification needed)
Quick Start
Setup (One-time)
-
Get OpenRouter API key:
- Visit https://openrouter.ai/keys
- Create account and generate API key
- Add credits to account (minimum $5 recommended)
-
Configure environment:
# Set API key export OPENROUTER_API_KEY='sk-or-v1-your-key-here' # Or use setup script python scripts/setup_env.py --api-key sk-or-v1-your-key-here -
Install dependencies:
uv pip install litellm -
Verify setup:
python scripts/perplexity_search.py --check-setup
See references/openrouter_setup.md for detailed setup instructions, troubleshooting, and security best practices.
Basic Usage
Simple search:
python scripts/perplexity_search.py "What are the latest developments in CRISPR gene editing?"
What ships with it
6 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.
- 9d ago First seen · 442 lines · 99 tokens per session scan A ea97eabf2243
perplexity-search is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 99 tokens to every session and 3,171 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to perplexity-search, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
bio-differential-expression-batch-correction
Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against…
bio-ortholog-inference
Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when…
bioprobench
Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.
alphafold2
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency…
audit-dataset
Audit tabular datasets before analysis or training for schema drift, missing values, duplicate rows or IDs, target imbalance, and entity or group leakage across splits using pure-stdlib helpers.
bio-alignment-msa-parsing
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.