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Take a selected FastMCP bug from a reproducible failure to a focused fix. Check compatibility, run required validation, prepare a pull request, and follow automated checks and review feedback after publication.
Analyze marker-gene sequencing data to study microbial communities. Supports FASTQ quality checks and, with primers, metadata, and taxonomy resources, workflows that produce sequence variant tables, taxonomic assignments, diversity measurements, and plots.
Start shotgun metagenomics workflows for mixed-community DNA reads in FASTQ files. Check read quality, remove host sequences, and profile organisms and biological functions using supported tools and databases, producing tables and visual summaries when results are available.
Configure and troubleshoot Claude Code within BB. Inspect provider settings, adjust context and sandbox options, enable Chrome integration, and manage Fast service selection while checking how changes affect the current conversation.
Read, query, filter, and write genomic files with pysam in Python. Extract regions, summarize variants, and count aligned bases while handling coordinate conventions, file indexes, headers, and the reference sequences needed to decode CRAM files.
Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.
Analyze pooled CRISPR screens from FASTQ reads or guide-count tables using MAGeCK. Validate inputs, assess library and replicate quality, and rank enriched or depleted genes with effect sizes and false discovery rates, which estimate false-positive proportions.
Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.
Translate FastGPT documentation from Chinese into natural English for North American readers. Follow the shared product glossary, preserve document structure and code, synchronize navigation files, and check for missing translations or English pages that need updates.
Assess FastMCP vulnerability reports by preserving evidence, reproducing claims, and identifying which component owns the failed protection. Establish affected versions and realistic impact, review proposed fixes, and recommend how the report should be handled.
Guide Nexent backend design, implementation, debugging, and review across database schemas, FastAPI endpoints, services, and configuration. Check existing interfaces, consult references for each affected layer, and verify successful and failed behavior while preserving public contracts.
Investigate fast-agent session files and logs to diagnose unexpected endings, tool loops, and mismatched tool responses. Inspect conversation history, summarize model and tool execution times, and connect sub-agent traces with the corresponding main-session activity.
Fix Fastly Compute Rust crate `cargo test` failures with "ld: symbol(s) not found" errors for Fastly SDK symbols like `_version_set`, `_body_new`, `_req_send` on native host builds. Use when: (1) `cargo test` on a Fastly Compute crate fails at link time with errors mentioning libfastly and symbols with names like `fastly::http::response::handle::...`, (2) You added a `#[cfg(test)]` module to a file in a Fastly Compute binary crate and the tests never seem to run or fail to link, (3) Existing `#[cfg(test)]` modules in src/main.rs or its sibling modules appear dead despite being present in the source tree, (4) `cargo test --lib` works but `cargo test` fails.
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Automate lab data tasks in Benchling using its Python SDK and REST API. Import and export sequences, inspect sample inventory, and work with electronic lab notebooks and workflow tasks while checking schemas, permissions, and operation results.
Process DNA, RNA, and protein sequences with Biopython, convert biological files, and retrieve NCBI records. Supports batch workflows for sequence comparison, searches for similar sequences, protein structure analysis, and evolutionary trees.
Keep FastGPT build and test workflows aligned across GitHub Actions and Forgejo Actions. Adapt registry destinations, credentials, proxy settings, and platform-specific steps so the corresponding workflows support the internal network environment.
Develop and review FastGPT data migration tasks for automatic system upgrades. Define safe reruns, resume points, progress reporting, and failure handling, while checking task order and whether upgrades must block application startup.
Write unit tests for FastGPT code, covering normal behavior, edge cases, and errors. It guides test placement, limits mocks that replace real dependencies, and checks TypeScript errors and coverage, the share of code exercised by tests.
Strengthen existing tests with fast-check by checking properties across generated inputs. Find bugs that example tests miss and reduce failing inputs to small, reproducible cases. Property testing is available; the other listed workflows remain planned.
Plan or run bulk RNA-seq processing from sequencing reads to count matrices, tables of expression counts across samples. Check sample metadata, reference consistency, read mapping, and quantification quality, and produce summaries for downstream analysis.
Organize ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag inputs and route them to suitable analysis workflows. Covers initial quality checks and preparation for finding peaks, genomic regions with concentrated signal, and comparing them across samples.
Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.
Upgrade the chat app’s fast or good model slot across Rust backend code and the frontend. Updates model definitions, selection logic, generated types, and display names, then checks compilation and consistency.
Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.