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 zongtingwei/Bioclaw_Skills_Hub --skill long-read-genomicsgit clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_HubWrote 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/zongtingwei/bioclaw_skills_hub/long-read-genomics)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/long-read-genomics"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/long-read-genomics/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/zongtingwei/bioclaw_skills_hub/long-read-genomics"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/long-read-genomics.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 | $0.00030 | $0.00874 |
| Opus 5 | $0.00015 | $0.00437 |
| Sonnet 5 | $0.00006 | $0.00175 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
long-read-genomics 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 13d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long-Read Genomics
Version Compatibility
Reference examples assume recent stable releases of the preferred tools, especially long-read and the other tools listed below.
Before using code or command patterns, verify installed versions match the environment:
- Python:
python -c "import <module>; print(<module>.__version__)" - CLI:
<tool> --version - If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.
Overview
Workflow for nanopore or PacBio long-read QC, alignment, polishing, methylation-aware analysis, and structural variant discovery.
When To Use This Skill
- use when the dataset is nanopore or PacBio long-read sequencing
- use when structural variants, phasing, polishing, or long-read methylation are part of the task
- use when long-read-specific QC and alignment assumptions must be respected
Quick Route
- If the input is raw or minimally processed data, start with validation and QC before any modeling.
- If the input is already processed, skip directly to the first workflow step that matches the user goal.
- If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.
Progressive Disclosure
- Read
references/technical_reference.mdwhen you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance. - Keep
SKILL.mdas the main execution path and load the reference file only when the task or failure mode needs the extra detail.
Default Rules
- Prefer Python-first workflows unless the task explicitly requires something else.
- Keep intermediate and final outputs separated.
- Record software versions, reference builds, and key parameters when they affect interpretation.
- Favor reproducible tables and figures over one-off interactive-only outputs.
Expected Inputs
- long-read FASTQ or raw data
- reference genome
- sample metadata
Expected Outputs
- aligned long-read files
- polished consensus or assembly updates
- long-read variant summaries
What ships with it
2 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.
- 13d ago First seen · 137 lines · 30 tokens per session scan A 61db16ce6195
long-read-genomics is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 30 tokens to every session and 874 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.
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