Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/arcinstitute/sragent/claude-skill)<a href="https://agentmods.dev/skills/arcinstitute/sragent/claude-skill"><img src="https://agentmods.dev/badge/skills/arcinstitute/sragent/claude-skill/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/arcinstitute/sragent/claude-skill"><img src="https://agentmods.dev/badge/skills/arcinstitute/sragent/claude-skill.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 | $0.00084 | $0.03754 |
| Opus 5 | $0.00042 | $0.01877 |
| Sonnet 5 | $0.00017 | $0.00751 |
| Haiku 4.5 | $0.00008 | $0.00375 |
Grade A, and why
sragent 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.
How it starts
The opening of the file, as written. The whole thing — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SRAgent: Sequence Read Archive Data and Publication Retrieval
Overview
SRAgent is an agentic workflow system for working with the NCBI Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) databases. It automates literature discovery, metadata extraction, and manuscript retrieval for genomics datasets.
Setup Instructions
1. Install SRAgent
SRAgent requires Python ≥3.11. Check to see if SRAgent is already installed:
which SRAgent
If SRAgent is not installed, follow the instructions below.
Install using uv:
# Clone the repository
git clone https://github.com/ArcInstitute/SRAgent.git
cd SRAgent
# Create and activate virtual environment with uv
uv venv
source .venv/bin/activate
# Install the package
uv pip install .
Verify installation:
SRAgent --help
2. Configure environment variables
The following environment variables are required:
OPENAI_API_KEY=sk-openai-...- Needed to use OpenAI models
ANTHROPIC_API_KEY=sk-ant-...- Needed to use Claude models
DYNACONF- Needed to switch between Claude and OpenAI models
[email protected]- Needed for using the Entrez API
NCBI_API_KEY=your-ncbi-key- Optional for increased rate limits when using the Entrez API
CORE_API_KEY=your-core-key- Optional for paper downloads from the CORE API
GCP_PROJECT_ID=your-project-id- Needed for using Google BigQuery
GOOGLE_APPLICATION_CREDENTIALS=/path/to/key.json- Needed for using Google BigQuery
Prompt the user to provide the environment variables if they are not already set as environment variables: export MY_SECRET_VAR=my-secret-value.
3. Configure Settings
SRAgent uses a settings file (settings.yml) to configure models and behavior.
The default configuration works for most users, but you can customize it.
Option A: Use Default Settings
No action needed - SRAgent ships with sensible defaults.
Option B: Custom Settings File
See ./references/example-settings.yml for an example settings file that you can modify as needed.
What ships with it
5 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 · 485 lines · 84 tokens per session scan A f1c09445f521
sragent is a skill published in the GitHub repository ArcInstitute/SRAgent (181 stars, last pushed 1mo ago), licensed MIT. It adds 84 tokens to every session and 3,754 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…