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Evaluate code against a product requirements document, scoring implementation and tests separately for each acceptance criterion. Produce an evidence-backed final grade, identify gaps, and compare implementations using a fixed checklist and scoring method.
Create an implementation plan grounded in code relationships and verified source files, with proposed changes, test scenarios, risks, and a structured handoff for an implementation agent. It can also revisit and strengthen an existing plan.
Turn a feature idea into a detailed implementation plan through research, interviews, and reviews by Gemini and Codex. Produce self-contained work sections with dependencies and acceptance criteria, plus files for supported automated implementation workflows.
Review an OpenSpec implementation against its specifications, tasks, and design decisions. The skill checks for missing work, uncovered scenarios, and inconsistencies, then produces a prioritized report with concrete recommendations and code references where available.
Build a Solution Blueprint for a Dynamics 365 Finance and Supply Chain Management implementation through guided interviews. Define scope and architecture, compare options, and document decisions, their rationale, assumptions, and unresolved questions across working sessions.
Turn feature requests, refactoring work, package upgrades, or infrastructure changes into structured Markdown implementation plans. Each plan defines concrete tasks, dependencies, affected files, and validation criteria for execution by AI agents or people.
Turn a feature requirements document into a detailed Markdown implementation plan following Epoch’s monorepo structure. Cover frontend components, APIs, database design, security, performance, and deployment, with Mermaid diagrams showing system architecture and data relationships.
Carry agreed implementation work through a separate Git worktree, an isolated working directory. After checks and review, it prepares one local commit, a clean branch, and a pull request draft for handoff without publishing them.
Generates cryptographic test vectors, inputs with expected outcomes, by deliberately changing implementation code and finding changes existing tests miss. Verifies new vectors across independent implementations and compares detection rates before and after adding them.
Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to cursor-implementation-loop, this plugin's kernel skill, with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to cursor-implementation-loop. Requires this plugin's cursor-implementation-loop skill; they ship together.
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Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to implementation-loop with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to implementation-loop. Requires implementation-loop and an authenticated backend CLI to execute.
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Turn unclear feature requests or high-impact changes into acceptance criteria: observable conditions for judging completion. Define scope, assumptions, risks, and verification methods, or review an existing specification so another agent or team can implement it with clearer requirements.
Check project dependency versions and consult the relevant official documentation before writing framework-specific code. Produce implementations with source links, identify deprecated APIs, and flag unverified approaches or conflicts between documentation and existing project patterns.
Create a phased implementation plan for a feature or complex development task. It investigates documentation and existing examples, identifies supported APIs, and records concrete tasks, references, and verification checks for each phase.
Carry out a phased implementation plan with subagents assigned to implementation, verification, pattern checks, and code review. Each phase must pass verification before changes are committed and pushed, with context prepared for the next phase.
Turn a feature request into a minimal implementation plan before coding. Identify which files need changes, explain why, define the smallest required interface, and flag broader effects or expanded permissions for the implementer.
Implement pending tasks from an OpenSpec change using its specifications and design documents. The skill updates task checkboxes, confirms progress, supports resuming unfinished work, and pauses when requirements or implementation issues need clarification.
Compare an implementation with its OpenSpec specifications, tasks, and design decisions before archiving the change. Get a report on completion, requirement coverage, and consistency, with prioritized issues, actionable recommendations, and explicit limits where evidence is missing.
Inspect Figma screens to gather details for implementation, including component variants, spacing, shared design settings, and image assets. It helps an agent understand layouts and design libraries before turning a design into code.
Manage coding work from issue intake through implementation, review, deployment, and acceptance checks. Track each completion requirement against fresh evidence, with defined retry limits and explicit escalation when progress needs human input.
Turn Airflow Improvement Proposals into practical guides checked against current code, or proposed user stories for design review before implementation. Organize use cases, distinguish verified examples from speculative code, and capture specific design questions.
Turn an understood task into an ordered implementation plan in Codewhale. Clarify the goal and constraints, identify which steps depend on others, and record verification methods, risks, and open questions before broader implementation begins.
Review source code for MCP, a protocol connecting AI applications to tools. Check authentication, input validation, unsafe execution, and other security risks, then report findings with file and line references and separate manual follow-ups.
Turn implementation plan phases into GitHub Issues with structured titles, descriptions, and labels. Check existing issues first, then create or update one issue per phase using feature or chore templates where available.