Execute a complete spec-to-PR pipeline from a Linear issue: auto-detect or create the Linear project for the current repo, fetch or create the spec issue inside that project, create a git branch, plan the implementation, execute tasks sequentially with TDD, commit, run a codebase readiness review, fix quality issues, commit fixes, and open a pull request with the Linear issue linked. Use this skill whenever the user says "implement this spec", "execute the Linear issue", "build this feature end-to-end", "turn this ticket into a PR", "take this issue to done", "implement and PR", "complete this Linear task", or references a Linear issue ID (e.g. "ENG-123", "PLT-42") and wants it shipped. Also trigger when asked to "implement from spec and review before PR" or "run the full pipeline on this issue." This skill chains: project detection/creation → issue resolution → branch creation → planning → TDD implementation → commit → readiness review → quality fixes → commit → PR.
Generate planning artifacts (plan.md, tasks.md) from a spec in specs/NNNN-name/ format and sync the tasks to a Linear Project — creating the project if it doesn't exist yet. Use this skill whenever the user wants to upload a spec to Linear, sync tasks from a spec to Linear, create a Linear project from a spec, generate a plan and tasks from a spec and push to Linear, or says things like "take this spec to Linear", "create Linear tasks for spec NNNN", "push the spec to Linear", "upload tasks from spec", "sync spec to Linear project", or "create the project in Linear if it doesn't exist". Also trigger when the user mentions an existing specs/NNNN-name/ directory and wants it tracked in Linear, even if they don't say "SDD" or "sync" explicitly.
Drive a full improvement cycle for a codebase: analyze the repo with Greptile (AI codebase search that returns cited file:line evidence) to surface concrete improvement opportunities, score and rank them with an auditable weighted formula (IQS — impact, reach, alignment, effort, risk, confidence), scaffold the chosen improvement into a new numbered spec that matches the project's spec format, then hand the spec to the spec-ensemble-driver skill to design and implement each section. Use this whenever the user wants to "find improvements and implement them", "analyze the code and turn the best idea into a spec", run an improvement/refactor/tech-debt pass that ends in shipped code, generate a new spec from a codebase audit, use Greptile to understand or audit a codebase, or chain codebase analysis → spec generation → implementation. Trigger on requests to audit/analyze a codebase for improvements, prioritize tech debt or enhancements by score, spin up a new spec from findings, run a Greptile-powered codebase ana
Evaluate a codebase's quality, security, and agent-automation readiness using a structured 8-pillar scorecard. Use this skill whenever someone asks to: review code quality, score a repo for agent readiness, run a readiness or maturity assessment, audit CI/CD pipelines, check security posture, evaluate engineering health, or assess "how good is this codebase." Also trigger when asked "what should I fix first," "what's the scorecard," or "how do I improve this repo." Produces a scored report across 8 pillars with concrete evidence, an overall maturity level (1–5), and a ranked fix list ordered by ROI. Always runs real commands (typecheck, lint, test, audit) rather than just reading config files — the delta between what config declares and what actually passes is where the bugs hide.
Generates a high-quality architecture.md file for any codebase using spec-driven development and Mermaid diagrams. Use this skill whenever the user wants to document system architecture, generate an architecture.md, create a codebase map, explain how their project is structured, produce technical documentation for new contributors, or visualize component relationships. Also trigger when the user says "document the architecture", "create architecture docs", "write an architecture overview", "add diagrams to the docs", "explain the codebase structure", or "generate system diagrams". This skill produces benchmark-quality output modeled after the rust-analyzer architecture.md — with Bird's Eye View, Code Map, Architecture Invariants, API Boundaries, Mermaid diagrams, and Cross-Cutting Concerns sections.
Drive spec-driven development section-by-section using a multi-model ensemble. For each spec requirement (§), fan the same design prompt out to OpenAI, Gemini, and OpenRouter, score every proposal with the Weighted Ensemble Quality Score (WEQS — six SDD dimensions, 0–100), implement the highest-scoring proposal, present the ranked results, and checkpoint for the user's approval before advancing to the next §. Use this whenever the user wants to work through a spec/PRD/requirements doc with multiple AI models, get an ensemble or "second opinion" from GPT/Gemini/OpenRouter on a spec section, rank competing design proposals by quality, run WEQS (or AQS) scoring on a spec, or "implement the best proposal for this section and move on." Trigger on mentions of ensemble review, multi-model spec work, WEQS/AQS scoring, OpenRouter/OpenAI/Gemini consultation on a spec, or section-by-section spec implementation — even if the user doesn't name the tool explicitly.