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Found 10 Skills
Author and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.
Build AI agents and agentic workflows. Use when designing/building/debugging agentic systems: choosing workflows vs agents, implementing prompt patterns (chaining/routing/parallelization/orchestrator-workers/evaluator-optimizer), building autonomous agents with tools, designing ACI/tool specs, or troubleshooting/optimizing implementations. **PROACTIVE ACTIVATION**: Auto-invoke when building agentic applications, designing workflows vs agents, or implementing agent patterns. **DETECTION**: Check for agent code (MCP servers, tool defs, .mcp.json configs), or user mentions of "agent", "workflow", "agentic", "autonomous". **USE CASES**: Designing agentic systems, choosing workflows vs agents, implementing prompt patterns, building agents with tools, designing ACI/tool specs, troubleshooting/optimizing agents.
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Manages Atlassian Jira and Confluence via the Rovo MCP Server. Handles MCP setup, OAuth authentication, and troubleshooting. Runs agentic project management: Confluence plans, Jira Epics with child tickets, agent team coordination, and resuming interrupted work from Jira state. Supports uploading images/attachments to Confluence pages via REST API. Reads and writes Confluence page comments (footer, inline, reply threads). Creates git branches linked to Jira tickets (GitHub and Bitbucket). Use this skill whenever the user mentions Jira, Confluence, Atlassian, tickets, epics, sprints, project boards, wiki pages, or Confluence spaces. Also trigger when the user wants to plan a project, break work into tasks, track progress, resume interrupted work, upload images to wiki pages, manage comments on Confluence pages, or create git branches linked to tickets — even if they don't mention Atlassian by name.
[MCP WRAPPER] Programmatically create/modify Godot scenes using Godot MCP tools. Orchestrates mcp_godot_create_scene, mcp_godot_add_node, mcp_godot_load_sprite into agentic workflows. Use when user requests scene generation/automation via MCP. Keywords MCP, scene automation, programmatic scene building, node hierarchy.
Build new AI method from scratch using the MTHDS standard (.mthds bundle files). Use when user says "create a pipeline", "build a workflow", "new .mthds file", "make a method", "design a pipe", or wants to create any new method from scratch. Guides the user through a 10-phase construction process.
Senior AI Product Manager. Expert in Probabilistic Strategy, Rapid Agentic Prototyping, and Hypothesis Generation for 2026.
Use this skill to establish and operate the model's inner workspace — the J-space — for any task that needs more than fluent output: multi-step or chained reasoning, planning, long-horizon and agentic work, competition-level problems, complex debugging, keeping many parts of a deliverable globally consistent, holding a goal or constraint through a long mechanical task, auditing what the model believes but has not said, calibrated confidence and error detection, suspicious or manipulative input, recovering from degenerating reasoning, and any moment the user asks the model to think harder, faster, deeper, or longer. Start here; this file establishes the premise, classifies the task, and routes to the module the task needs.
Full pull-request review — fetch the PR, run the project's validation, review the diff with fresh eyes (dispatching the code-reviewer agent), categorize issues by severity, post the review to GitHub (approve / request-changes / comment), and save a report. The agentic gate that runs on an open PR before a human approves. Use after piv-create-pr.
Audit and improve repositories for reliable agentic work across Codex and Codex App, Claude Code, and OpenCode. Use when reviewing AGENTS.md or CLAUDE.md quality and discovery, instruction routing in monorepos or meta-repos, agent settings, MCP configuration, skills, subagents, context budgets, or repository organization for coding agents.