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 PKU-YuanGroup/OpenAI4S --skill bio-database-access-remote-homologygit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-database-access-remote-homology)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-remote-homology"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-remote-homology/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/pku-yuangroup/openai4s/bio-database-access-remote-homology"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-remote-homology.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 | $0.00132 | $0.05267 |
| Opus 5 | $0.00066 | $0.02634 |
| Sonnet 5 | $0.00026 | $0.01053 |
| Haiku 4.5 | $0.00013 | $0.00527 |
Grade A, and why
bio-remote-homology scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz This is a copy
95% identical to bio-remote-homology — 12 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: NCBI BLAST+ 2.15+, HMMER 3.4+, MMseqs2 15+, DIAMOND 2.1+, HH-suite3 3.3+, Foldseek 9+
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
<tool> --versionthen<tool> --helpto confirm flags - Python:
pip show <package>then introspect signatures
If a flag is unrecognized or behavior changes, introspect with --help and adapt the example to match the installed version rather than retrying.
Remote Homology
"Find homologs my BLAST missed" -> Standard BLAST detects similarity reliably down to ~35% pairwise identity (the "twilight zone", Rost 1999 Protein Eng 12:85). Below that, profile methods (PSSMs, HMMs) and structure-aware methods (Foldseek) recover homologs that pairwise alignment misses.
This skill covers the decision: which method, when, against what database. The competition has shifted substantially since 2015: PSI-BLAST is no longer the de-facto standard; MMseqs2 and DIAMOND have replaced BLAST in most large-scale workflows; Foldseek (van Kempen et al. 2024 Nat Biotechnol 42:243) detects homologs no sequence method can reach by searching with a 3Di structural alphabet derived from AlphaFold/ESMFold predictions.
- CLI:
psiblast,jackhmmer,hmmsearch,hhblits,mmseqs,diamond,foldseek - Python:
Bio.SearchIOfor output parsing; tool-specific clients exist but subprocess is preferred - Web: HHpred (HHblits webserver), Foldseek webserver, ColabFold for paired structure search
Required Setup
# Install via conda
conda install -c bioconda hmmer mmseqs2 diamond hhsuite foldseek
# BLAST+ (separate)
conda install -c bioconda blast
# Verify
hmmsearch -h | head -3 # HMMER 3.4+
mmseqs version # MMseqs2 15+
diamond --version # DIAMOND 2.1+
hhblits -h | head -3 # HH-suite3 3.3+
foldseek --version # Foldseek 9+
Decision matrix: which method when
| Question | Best tool | Why | Sensitivity / Speed |
|---|---|---|---|
| Quick all-vs-all proteome | MMseqs2 or DIAMOND | 100-10,000x faster than BLAST at comparable sensitivity | Highest throughput, near-BLAST sensitivity |
| Identify distant protein homolog (single query) | jackhmmer | Iterative HMM; usually beats PSI-BLAST | Higher sensitivity than PSI-BLAST |
| Distant homology where structure available | Foldseek | 3Di alphabet finds homologs sequence misses | Finds hits PSI-BLAST/HMMER cannot |
| Profile-profile comparison (PDB70 / Pfam) | HHblits + HHsearch | Profile vs profile is most sensitive when target also has profile | Best sensitivity for very-deep homology |
| Domain assignment | hmmscan against Pfam-A | Curated, calibrated thresholds | Standard practice |
| Metagenomic protein clustering | MMseqs2 easy-cluster |
Scales to >1B sequences | Production-grade |
| ORF search vs metagenome | DIAMOND blastx --frameshift |
Frameshift-aware; long reads | Best for noisy long reads |
| Structure-aware homology (no AF2 prediction available) | Foldseek + ProstT5 | Predicts 3Di alphabet from sequence via PLM | Skip the AF2 step |
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.
- 9d ago First seen · 368 lines · 132 tokens per session scan A d6b54c4b8077
bio-remote-homology is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 132 tokens to every session and 5,267 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to bio-remote-homology, differing in 12 lines, and is treated as a copy.
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