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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.

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• Upgrade the default chat model
• Replace the smart mode model
• Align backend definitions with frontend model labels

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.

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• Checking missing values and outliers in experimental tables
• Examining sequence lengths and quality scores in FASTQ files
• Inspecting microscopy image dimensions and metadata

Build tests with fast-check for TypeScript and JavaScript or Hypothesis for Python. Generate varied inputs to check rules code should always satisfy, discover edge cases, and simplify failing inputs into small examples for debugging.

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• Checking that encoding and decoding restore the original data
• Testing rules that sorting must preserve
• Generating inputs with complex constraints

Use when building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-verified v3 gotchas — zero speculation.

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FastAPI-specific delta on top of python-foundation:sqlalchemy-patterns: async engine and AsyncSession lifecycle, get_db dependency injection, async-safe lazy loading rules, async Alembic env.py wiring. Used by fastapi-architect (model definitions) and alembic-specialist (column type finalization and migration generation). Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Manage async database sessions with AsyncSession and async_sessionmaker. - Inject sessions into routes via the get_db() dependency. - Apply async-safe lazy loading (lazy="selectin" or "raise"; never sync lazy loads). - Integrate async Alembic env.py for migration autogeneration. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - FastAPI routing and Pydantic schemas — see fastapi-plugin:fastapi-conventions. - Alembic migration execution (that's alembic-specialist's job) — this skill covers definitions.

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Fastify (Node.js 高速 HTTP サーバーフレームワーク, v5) の API リファレンス。 `fastify.register`, encapsulation, `fastify-plugin`, `decorateRequest` / `decorateReply`, `onRequest` / `preHandler` / `onSend` フック, JSON Schema + Ajv, Type Providers (TypeBox / Zod), `fastify.inject`, `FST_ERR_*`, find-my-way ルーティング, fastify-cli, v4→v5 移行。

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Bring a Remotion branch up to date with the official main branch while preserving local changes and published history. Handle temporary change storage, merges, conflicts, validation, and a normal push when requested.

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• Merge the latest main into a feature branch
• Fast-forward a local main branch
• Update a pull request branch while preserving published history

Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.

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• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.

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• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

Work with genomic regions in Polars tables to find overlaps, nearby regions, covered bases, and gaps. Read and write bioinformatics files, query data with SQL, and check coordinate conventions when joining datasets or migrating from bioframe.

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• Finding variants that overlap genomic regions
• Merging regions and measuring covered bases
• Summarizing sequencing read depth

Review FastGPT pull requests in stages, starting with the intended change and key execution paths. Check security, performance, tests, and compatibility, then organize findings by severity into a review report and line-level comments.

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• Checking a pull request before merging
• Reasoning through normal, edge, and error cases
• Assessing API and database compatibility risks

Translate selected FastGPT interface text files from Simplified Chinese into every supported target locale. Check product terminology and interface context, preserve JSON structure and placeholders, and validate the translated files for consistency.

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• Translating a selected interface text file into all supported locales
• Updating several translation files together
• Translating every namespace from completed Chinese source text

Turn a written app specification into a runnable starter project using the requested technology stack. Generate implemented features, tests, configuration files, and setup instructions, then check the project for common omissions.

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• Turning a requirements document into a starter project
• Creating a task API with FastAPI and SQLite
• Building a recipe-sharing website with Next.js

Choose a web application stack, generate a starter project, and assess existing code quality. Uses team size, release frequency, audience, and budget to guide recommendations, with reports covering security, complexity, dependencies, and prioritized improvements.

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• Generating a starter project for a web application
• Choosing a technology stack within team and budget constraints
• Reviewing code security and complexity

Evaluate an incoming FastMCP issue and its linked pull requests. Check whether the reported behavior is a supported bug, assess the proposed contribution, and recommend assigning the contributor, declining, or seeking a specific maintainer decision.

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• Deciding whether to assign an external contributor
• Assessing a pull request closed by contribution checks
• Checking whether reported behavior is a supported bug

Writes, reviews, and debugs property-based tests, which generate inputs to check general rules. Helps test conversions, parsers, normalization, and state invariants, assess whether existing assertions are meaningful, and distinguish faulty rules from implementation bugs.

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• Testing encode/decode round trips across generated inputs
• Checking normalization and sorting properties
• Reviewing whether existing property tests assert meaningful rules

Plan or run ATAC-seq analysis to identify accessible DNA regions. Check sequencing quality, align reads, detect peaks, combine evidence across replicate samples, and prepare comparisons between conditions with quality summaries and visualization tracks.

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• Check the quality of ATAC-seq samples
• Detect accessible DNA regions from FASTQ or BAM files
• Build consensus peaks across replicate samples

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

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• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences

Analyze biological data in Python, from DNA, RNA, and protein sequences to evolutionary trees and microbial communities. Read and convert files, compare diversity within and between samples, and test differences between groups.

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• Processing DNA, RNA, and protein sequences
• Building and comparing evolutionary trees
• Comparing microbial community diversity

Find related protein sequences and generate multiple sequence alignments, which line up corresponding positions across sequences. Use NVIDIA's hosted API or a local Docker service for standard searches, paired complex alignments, and local structural template searches.

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• Generate protein alignments in A3M or FASTA format
• Create paired alignments for protein complexes
• Search PDB70 for structural templates locally

dontbesilent 慢就是快。帮创业者找到看起来更慢但长期更快的方法，用摩擦建造资产。 触发方式：/dbs-slowisfast、/慢就是快、「有没有更慢的方法」「我是不是太快了」 Slow-is-fast diagnosis. Help entrepreneurs find seemingly slower methods that build assets through friction. Trigger: /dbs-slowisfast, "is there a slower way", "am I going too fast"

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Fix "backend exists on a draft version but isn't reachable from the new Compute deploy" bugs after `fastly compute publish`. Use when: (1) You ran `fastly service backend create --autoclone` (or any dashboard/CLI change that creates a draft version) but did not activate that draft, (2) You then ran `fastly compute publish` which appeared successful but your backend / ACL / header / dictionary change is missing from the active version, (3) Compute code returns backend-not-found errors or misroutes requests, (4) You're surprised to see your draft version number "skipped over" in the version chain. Root cause: `fastly compute publish` clones from the currently-active version, not from the latest draft, so dashboard changes made in an un-activated draft get stranded.

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Fix "Requested backend named 'X' does not exist" or TLS alert errors in Fastly Compute@Edge. Use when: (1) Backend works locally but fails in production with "backend does not exist", (2) TLS alert received (alert_id=0) errors when calling external APIs from Fastly, (3) Backends defined in fastly.toml aren't available after deployment. The fastly.toml [[backends]] section only applies during initial service creation - existing services require backends to be added via CLI or API with proper SSL/SNI configuration.

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FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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