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 agentmods add skills/aiming-lab/autoresearchclaw/biology-biopythonnpx skills add aiming-lab/AutoResearchClaw --skill biology-biopythongit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWhat 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 | $0.00040 | $0.00931 |
| Opus 5 | $0.00020 | $0.00465 |
| Sonnet 5 | $0.00008 | $0.00186 |
| Haiku 4.5 | $0.00004 | $0.00093 |
Grade A, and why
biology-biopython 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 yesterday.
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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Biopython Bioinformatics Best Practice
Sequence Manipulation
- Create sequences:
from Bio.Seq import Seq; seq = Seq("ATGCGA") - Complement:
seq.complement(); Reverse complement:seq.reverse_complement() - Transcription:
seq.transcribe()(DNA to RNA) - Translation:
seq.translate()(DNA/RNA to protein) - GC content:
from Bio.SeqUtils import gc_fraction; gc_fraction(seq) - Molecular weight:
from Bio.SeqUtils import molecular_weight
File Parsing (SeqIO)
- Read FASTA:
for rec in SeqIO.parse("file.fasta", "fasta"): ... - Read GenBank:
for rec in SeqIO.parse("file.gb", "genbank"): ... - Read single record:
rec = SeqIO.read("file.fasta", "fasta") - Write sequences:
SeqIO.write(records, "output.fasta", "fasta") - Convert formats:
SeqIO.convert("input.gb", "genbank", "output.fasta", "fasta") - Index large files:
idx = SeqIO.index("large.fasta", "fasta")for random access
BLAST Operations
- Online BLAST:
from Bio.Blast import NCBIWWW; result = NCBIWWW.qblast("blastn", "nt", seq) - Parse results:
from Bio.Blast import NCBIXML; records = NCBIXML.parse(result) - Local BLAST: run via subprocess, parse XML output with NCBIXML
- Always set
Entrez.emailbefore any NCBI access - Filter results by e-value (typically < 1e-5) and coverage
NCBI Database Access (Entrez)
- Always set email:
Entrez.email = "[email protected]" - Search:
handle = Entrez.esearch(db="pubmed", term="query") - Fetch records:
handle = Entrez.efetch(db="nucleotide", id="ID", rettype="fasta") - Use API key for higher rate limits (10 req/s vs 3 req/s)
- Respect NCBI rate limits; add delays between batch requests
Phylogenetics (Bio.Phylo)
- Read trees:
from Bio import Phylo; tree = Phylo.read("tree.nwk", "newick") - Draw trees:
Phylo.draw(tree)orPhylo.draw_ascii(tree) - Supported formats: newick, nexus, phyloxml
- Traverse clades:
for clade in tree.find_clades(): ... - Calculate distances:
tree.distance(clade1, clade2)
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.
- yesterday First seen · 66 lines · 40 tokens per session scan A 82978a1df8d1
biology-biopython is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,287 stars, last pushed 13d ago), licensed MIT. It adds 40 tokens to every session and 931 once invoked, about $0.0002 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
clawmobile-trace-induction
Record or summarize a ClawMobile demonstration and save a validated reusable skill candidate draft.
grounded-review
Review a research report draft with a structured scoring rubric, run a bounded repair loop when needed, and produce the final deliverable report.
one-report
Run the full One-Report pipeline from one input file through grounding, research, evidence-rich report drafting, final review quality-gating, and export, while reusing existing skills, preserving current contracts, and enforcing strict downstream skill fidelity for every grounded unit.
grounded-research-lit
Run focused literature and web research from a grounded note. Use when a grounded note already exists and you want targeted research results, opened-link evidence, deeper per-paper analysis materials, optional downloaded literature, and a two-stage literature output (litinitial.md then refined lit.md).
grounded-summary
Create a rich, evidence-preserving research report draft from a grounded note and its follow-up literature result. This is the main report-writing stage of the middle pipeline, not a compression memo.
remote-input
Download remote content (arxiv papers, YouTube videos, Bilibili videos) to local storage and route to downstream grounding pipeline. Use when user provides a URL instead of a local file path.