ronsanzone

Restate the last message in plain human language, with no jargon.

Give a capable engineer a concise briefing on the critical facts and implications of an effort before it is built or after it is completed. Use when the human explicitly invokes $facts, and never otherwise; do not invoke this skill automatically.

Vendor skills from a GitHub repository into this repo. Use when asked to import, vendor, list, add, or update skills from another skills repository while preserving provenance and detecting local edits.

Use when given a well-scoped bug fix or small feature ticket from Jira. Investigates the ticket, researches the codebase, proposes a fix, then plans and implements on approval. Lighter alternative to the full deep-work pipeline for tickets that don't need multi-session design discussion.

Use when starting work on a ticket that needs refinement before entering the deep-work pipeline. Reads from Jira, pasted text, or a file, then interactively refines it into a structured ticket.md.

Use when a session is ending and you want to analyze process efficiency. Triggers on requests to review how a session went, evaluate context usage, or identify workflow improvements.

Load this skill before calling the `execute` tool. Teaches the Executor sandbox calling model: discover with `tools.search({ query, limit })`, inspect with `tools.describe.tool({ path })`, list sources with `tools.executor.sources.list({})`, then call the real tool by its full address. Use for SaaS APIs, remote systems, MCP/OpenAPI/GraphQL integrations, and auth/approval-managed actions.

Use when you need to deeply understand a PR, branch, or system/feature end-to-end. Triggers on "explain this PR", "walk me through", "how does X work", "tour of this change", "help me understand this system", "ramp me up on".

Use when starting the RPI flow and you want to deeply understand a question, system, or feature by reading the codebase before planning.

Use when starting the RPI flow and you want to deeply understand a question, system, or feature by reading the codebase before planning.

Use when starting a new deep-work task. Decomposes a task description into objective research questions that avoid premature solutioning. Phase 1 of the deep-work pipeline.

Use when you have research questions from deep-work Phase 1. Objectively investigates the codebase to answer pasted questions without access to the original task description.

Use when deep-work Phase 2 research is complete. Combines research findings with the original task to explore design options, evaluate tradeoffs, and make decisions interactively.

Use when deep-work Phase 3 design decisions are complete. Maps chosen decisions to concrete file changes organized into implementable phases.

Use when deep-work Phase 4 structure outline is complete. Expands the outline into a detailed implementation plan with exact file paths, code patterns, tests, and validation commands.

Use when deep-work Phase 5 plan is complete. Executes the implementation plan by dispatching a fresh subagent per task with two-stage review (spec compliance → code quality).