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Found 6,131 Skills
Timekit integration. Manage data, records, and automate workflows. Use when the user wants to interact with Timekit data.
Flespi integration. Manage data, records, and automate workflows. Use when the user wants to interact with Flespi data.
Slope integration. Manage data, records, and automate workflows. Use when the user wants to interact with Slope data.
Primarily the agent's internal-thinking skill — invoke it silently to model a problem, identify trade-offs, and decide what to do, BEFORE asking the user anything or dispatching another skill. Workflow skills call `/culture` as their step-1 reasoning pass; the agent does not surface the dialogue. Only treat this as a user-facing skill when the user has explicitly opted out of writes — phrases like "no writes", "just rubber-duck this", "let's only talk", "/culture". In the user-facing path the output is conversation; the only sanctioned artifact is an opt-in `.cheese/notes/<slug>.md` handoff slug at session end if the user asks for notes. Culture never writes to production code, never commits, never opens PRs. If the dialogue reveals real work, recommend `/mold` (fuzzy → spec) or `/cook` (clear ask → code) and stop. Before `/mold` or `/cook`.
Kandy integration. Manage data, records, and automate workflows. Use when the user wants to interact with Kandy data.
Router skill for LLMQuant portfolio workflows. Use when the user needs company profiles, thesis tracking, theme research, watchlist monitoring, or alert management.
Cross River integration. Manage data, records, and automate workflows. Use when the user wants to interact with Cross River data.
Given event_candidates YAML (output from discover-event-surfaces), generates a concrete instrumentation plan for priority-3 (critical) events. Acts as a Software Architect: discovers existing analytics patterns in the codebase, reads the hinted files to determine what variables are in scope, designs minimal chart-useful properties, and identifies the exact insertion point for each tracking call. Outputs a structured JSON trackingPlan. Use this as step 3 of the analytics instrumentation workflow, after discover-event-surfaces. Trigger whenever a user has event_candidates and wants to generate tracking code, asks "instrument these events", "generate tracking plan", "add analytics for these events", "where should I put the tracking calls", or any request to turn event candidates into concrete implementation guidance.
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.