Loading...
Loading...
Found 11,811 Skills
Whatfix integration. Manage data, records, and automate workflows. Use when the user wants to interact with Whatfix data.
INVOKE THIS SKILL for Arize Prompt Hub and `ax prompts` workflows: author or import templates and save (Workflows A–B), label/promote (C), or list/get/edit/delete/duplicate (D). Use when the user mentions ax prompts, Prompt Hub, creating/editing/saving a prompt, `{variable}` placeholders, or production/staging labels. For improving prompt text using traces or eval scores, use arize-prompt-optimization. For running experiments, use arize-experiment.
Railway integration. Manage data, records, and automate workflows. Use when the user wants to interact with Railway data.
TextAnywhere integration. Manage data, records, and automate workflows. Use when the user wants to interact with TextAnywhere data.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing academic-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Searches for and retrieves existing visual media (images, logos, icons, photos, graphics, banners, thumbnails, hero images, backgrounds) from sources such as Salesforce CMS, Data 360 or any other source. Use this skill ANY TIME a user request involves finding, searching, getting, fetching, retrieving, grab, looking up, locating media. NEVER call search_media_cms_channels, search_electronic_media tools directly — always go through this skill first. This skill must be activated before any tool is used for media search or retrieval, without exception. Takes PRIORITY and activates FIRST when ANY media search/retrieval is mentioned, regardless of what else happens with the media afterward. Triggers for requests like "search for logo", "find hero image", "get company logo", "locate icons", "fetch background image", "retrieve product photos". Handles the search and source selection workflow. Does not apply when the request is about brand search, to generate NEW images with AI, or edit existing images.
Ingest raw context the user pastes or points at — a ticket, a design doc, meeting notes, a spec, a URL, referenced files/paths — and have an agent READ and UNDERSTAND all of it, then synthesize a well-formed feature brief (goal, scope, constraints, and load-bearing unknowns) that feeds sdd-clarify and the sdd-feature-flow harness. Use at the very start of a feature when you have source material instead of a one-line goal, or whenever the user says "here's the context" / "read this" / dumps a ticket or doc.
Optional AI SDLC architecture workflow. Use when an AI assistant needs to define system boundaries, components, interfaces, architectural constraints, alternatives, decisions, tradeoffs, risks, or validation for a feature and produce routed human and machine artifacts linked to requirements and durable decisions. Supports `--quick-flow` for focused design and `--full-flow` for strict decision, risk, and validation coverage.
AI SDLC controlled change-workspace and specification-delta workflow. Use when an AI assistant needs to create or validate an isolated proposal workspace, author and validate requirement deltas, preview canonical changes, or apply and archive an explicitly approved change with rollback evidence. Supports `--quick-flow` for assumption-driven drafts and `--full-flow` for strict owner, target, evidence, and authority checks.
Use after PRFAQ and BRD creation to run a strict final quality review, identify gaps or contradictions, and assign a readiness score before design or development starts. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
AI SDLC code review workflow. Use when an AI assistant is asked to review a diff, PR, branch, commit, staged changes, or completed implementation against SDD requirements, tests, API contracts, security, and scope discipline. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Use when PRFAQ, BRD, PRD, product brief, workflow, or equivalent initiative artifacts exist and you need to review them for planning gaps, unclear scope, weak priorities, missing actors, and backlog-blocking ambiguity before decomposing work. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.