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Found 6,136 Skills
Router and overview for the Cargo CLI agent skills. Explains the eleven skills (one outcome skill cargo-gtm + ten capability skills), the UUID flow between them, async polling, end-to-end use cases (enrich one record, enrich and sync to CRM, AI lead scoring, custom workflow, error monitoring, fresh-workspace bootstrap, segment export, GTM context authoring), and common gotchas (`conjonction` spelling, run vs batch, model-uuid vs segment-uuid). Load first whenever working with the Cargo CLI, when unsure which sub-skill applies, when stitching multiple sub-skills together, when bootstrapping a workspace, or when the user asks about Cargo skills in general.
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.
Transition the Linear issues linked to the current branch through their workflow states (In Progress / In Review / Done) — resolve live state IDs by team name, extract issue IDs from the branch, and apply the transition idempotently. Use when starting work on an issue, when a PR opens or updates, during branch cleanup, or whenever a branch's Linear issues need their state synced. Resolves state IDs by team name (not key — keys go stale on rename), reads the team name and issue-ID prefixes from config.json, and skips any issue already at or past the target state.
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, add supporting capabilities, or become orchestration. The user does not need to mention Agently explicitly.
AI SDLC Conventional Commit workflow. Use when an AI assistant drafts, validates, reviews, or fixes commit messages in this repository, especially when commits must include SDD spec references, validation summaries, or safe conventional commit subjects. 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.
AI SDLC resumable task-runtime workflow. Use when an AI assistant needs to start or resume a versioned delivery run, select dependency-ready work, enforce step, failure, and token budgets, retry safely, persist exact stop reasons, recover state from an append-only journal, or require commit evidence at task boundaries. Supports `--quick-flow` for deterministic local runs and `--full-flow` for strict transition review.
AI SDLC declarative workflow planning. Use when an AI assistant needs to validate a versioned workflow, plan typed dependency steps, evaluate bounded conditions, enforce approval gates, attach deterministic hooks, detect cycles, or create safe dependency waves with sequential fallback when host concurrency or isolation is unavailable. Supports `--quick-flow` and `--full-flow`.
Route any dropped-in input — idea, spec path, file path, PR or issue, stack trace, bug report, or bare `/cheese` — to the right workflow skill. Use as the unified entry point — phrases include "/cheese", "what should I do with this", "help me get started", "route this", or any opening message that does not already name a downstream skill.
Comet Native workflow. Used when the user explicitly calls /comet-native, requests to start or resume a Native change, or the entry route leads to Native.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes AI coding assistant sessions and generates an HTML report with workflow insights. Use this skill when the user asks to analyze sessions, generate a report, view usage patterns, check statistics, or review their coding workflow. Trigger phrases include "analyze my sessions", "generate a report", "show my stats", "how have I been using you", "session insights", "세션 분석", "리포트 생성", "사용 패턴".