为 FastGPT 新增、修改或审查自动系统升级脚本及其注册信息。涉及系统迁移、升级任务、启动迁移、migration registry、checkpoint、全量重跑、独立模式、进度或失败数据时使用；普通业务更新和未接入自动升级框架的一次性手工清洗不使用。

Work on Composio documentation content, Fumadocs configuration, changelogs, docs automation prompts, docs decisions, ADR-style records, and docs review guidance. Use for files under docs/, documentation workflows, or requests to record or update a docs decision. Do not use for SDK runtime changes unless docs are the main deliverable.

Conducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.

Manages Zotero reference libraries using the pyzotero Python client: retrieves, creates, updates, and deletes items, collections, tags, and attachments via the Zotero Web API v3 or local API. Applies when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.

Apply the firm's KYC/AML rules grid to a parsed onboarding record — assign a risk rating, list every rule outcome with the rule cited, and flag what's missing or escalation-worthy. Use after kyc-doc-parse; this skill decides nothing, it scores and routes.

Build macroeconomic and rates dashboards combining macro indicators, yield curves, inflation breakevens, and swap rates. Use when monitoring macro conditions, analyzing yield curve shape, decomposing real vs nominal rates, assessing policy rate expectations, or evaluating financial conditions.

Official GSAP skill for gsap.utils — clamp, mapRange, normalize, interpolate, random, snap, toArray, wrap, pipe. Use when the user asks about gsap.utils, clamp, mapRange, random, snap, toArray, wrap, or helper utilities in GSAP.

Focused interactive tutor for the Reverse Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to read a compiled program: file formats, disassembly, control flow, lifting, evidence, proof, calling conventions, compiler idioms, linking, types, debugging, bytecode, diffing, symbolic execution, and AI-assisted analysis. Teaches one lesson per invocation and records evidence in REVERSE-ENGINEERING-LEARNING.md.

Writes a long technical manual that explains one subject at one pinned version, from its purpose to each request and response. Every listing comes from an offline capture, every claim traces to a ranked source, and an audit must report TOTAL 0. Use it for "write a manual", "write a 101 manual for <subject>", "manual.json", "add a manual section", "build a capture kit", "audit the manual", or "pin a spec version and document it".

Writes a long technical manual that explains one subject at one pinned version, from its purpose to each request and response. Every listing comes from an offline capture, every claim traces to a ranked source, and an audit must report TOTAL 0. Use it for "write a manual", "write a 101 manual for <subject>", "manual.json", "add a manual section", "build a capture kit", "audit the manual", or "pin a spec version and document it".

Focused interactive tutor for the Model Context Protocol (MCP) path in AI Engineering from Scratch. Start or resume this route when a learner wants to build, secure, debug, verify, or operate MCP clients, servers, transports, gateways, registries, or conformance gates. Teaches one lesson per invocation and records wire evidence in MCP-LEARNING.md.

Focused interactive tutor for the Model Context Protocol (MCP) path in AI Engineering from Scratch. Start or resume this route when a learner wants to build, secure, debug, verify, or operate MCP clients, servers, transports, gateways, registries, or conformance gates. Teaches one lesson per invocation and records wire evidence in MCP-LEARNING.md.

Focused interactive tutor for the Agent Skills Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to create, discover, invoke, secure, evaluate, package, or port Agent Skills. Teaches one lesson per invocation and records evidence in AGENT-SKILLS-LEARNING.md.

Interactive quiz that maps your AI/ML knowledge to a starting point in the 542-lesson, 20-phase AI Engineering from Scratch curriculum. Trigger phrases: "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge", "placement test", "skip ahead"

Understand the capabilities and restrictions of each Remotion Studio mode. Use when implementing or reviewing Studio actions, permissions, availability, or UI across interactive, detached, deployed, and browser-based Studios.

Perform a read-only, defect-first review of a specified code change and return every actionable finding. Use when another agent delegates review of uncommitted changes, a base-branch diff, a commit, or custom review instructions.

Review, improve, rewrite, author, or plan Storybook documentation in /docs. Use this when asked to review docs, improve a page, rewrite documentation, draft new docs, or advise on docs strategy.

Selects bounded, graph-informed source slices with Trailmark and delegates focused code analysis or patch-proposal work to a smaller subagent. Use when offloading function-, class-, caller-, callee-, call-path-, entrypoint-, or line-focused code tasks to constrained or locally hosted models without exposing the full repository.

Scans a codebase for security vulnerabilities using CodeQL's interprocedural data flow and taint tracking analysis. Triggers on "run codeql", "codeql scan", "build codeql database", "SAST scan", "taint analysis", "dataflow analysis", or "find vulnerabilities in this repo". Covers Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, and Swift. Supports "run all" (security-and-quality + security-experimental) and "important only" (high-precision) scan modes, and creates data extension models for project-specific sources and sinks. For fast single-file pattern matching, or when no build is available for a compiled language, use the semgrep skill; to parse SARIF that already exists rather than produce it, use the sarif-parsing skill.

Builds and deploys compiled Airflow Java SDK bundles so workers can run them. Use when the user wants to package a JVM task bundle into a JAR, asks about the `org.apache.airflow.sdk` Gradle plugin, `./gradlew bundle`, the Maven shade/BOM setup, fat vs thin JARs, the logging integration artifacts (JPL, SLF4J, Log4j 2, JUL), preview/snapshot builds, or getting the JAR onto an Airflow worker (Docker, Kubernetes, or Astro). For the task code see authoring-java-sdk-tasks; for the Airflow coordinator settings see configuring-airflow-language-sdks.