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Found 7,078 Skills
Automatically detects story settings (genres, time periods, themes) based on keywords and activates corresponding knowledge bases - works silently in the background to provide relevant writing guidance without user intervention
Searches for homologous protein sequences using MMseqs2 (fast, default) or BLAST (comprehensive, fallback). Trigger this whenever the user provides a protein sequence or FASTA file and asks to find homologues, sequence matches, or wants to infer protein function based on sequence similarity, but not when the user wants to infer protein function based on structural similarity.
Use when a Luma / 拾光 / 拾光智能体 / 拾光工具 agent needs content research, topic discovery, keyword tables, persona-based search, or Excel-friendly research outputs for short-video planning.
Catalyst NoSQL — key-value table store with typed items built via NoSQLItem builder. Partition key required. Trigger on 'NoSQL', 'nosql.table', 'insertItems', 'fetchItem', 'updateItems', 'deleteItems', 'queryTable', 'NoSQLItem', 'document storage', 'flexible schema', or 'Catalyst document database'. Do NOT use when you need SQL joins, ZCQL queries, or a fixed relational schema — use catalyst-datastore instead.
Provisions Firebase project and Firestore database. Automatically configures .firebaserc, firebase.json, and secure firestore.rules using local rules references, and guides on billing or Blaze-plan error escalations.
Use when upgrading an existing TypeScript codebase from Inngest SDK v3 to v4, or when fixing mixed v3/v4 API usage. Covers detecting current SDK usage, moving triggers into createFunction options, replacing EventSchemas with eventType/staticSchema, moving serve options to the client, updating realtime imports, rewriting step.invoke string IDs, checkpointing/serverless runtime settings, Connect option changes, and verification.
Create and fill .agents/qa-project-context.md with the project's tech stack, test frameworks, CI/CD pipeline, environments, quality goals, risk areas, team structure, and conventions. This is the one file every other QA skill reads first, so they skip redundant discovery and give context-aware advice. Use when: "set up QA context," "configure testing," "initialize project," first use of any QA skill. Not for: bootstrapping a brand-new project's QA end-to-end — use qa-start (which calls this skill as its first step). Related: qa-start, risk-based-testing, test-strategy, qa-metrics, playwright-automation.
Investigate a topic against preserved sources and write a provisional research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate-notes` skill, after a decision lands.
Promote existing research into a canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` skill.
Integrate App Builder Database Storage (@adobe/aio-lib-db) into an Adobe Commerce app and scaffold a runtime action that reads and writes documents. Use when the user wants persistent, queryable storage backing a Commerce app — either from a web action (HTTP-invokable) or from an event/webhook handler. Requires a base app initialized with commerce-app-init.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.
Event deduplication with canonical selection, reputation scoring, and hash-based grouping for multi-source data aggregation. Handles both ID-based and content-based deduplication.