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Turn identified threats and business needs into specific, testable security requirements. Define priorities, explain the rationale, and develop security user stories, acceptance criteria, and test cases linked to the risks they address.
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Design and implement interfaces around the intended audience and constraints. Plan visual hierarchy, spacing, and interface states, support different screen sizes and accessibility needs, then check the appearance against agreed acceptance criteria.
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Create structured Markdown specifications that AI agents can read and use. Organize requirements, constraints, interfaces, acceptance criteria, and testing plans into a self-contained document with clear definitions and examples.
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Compare specification requirements with the codebase and create GitHub issues for unmet requirements. Check existing issues for duplicates, then include requirement IDs, implementation guidance, context, and acceptance criteria in each new issue.
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Create a feature PRD, a product requirements document, from a parent Epic. Turns a feature idea into user stories, explicit requirements, acceptance criteria, and scope boundaries that engineering teams can use to prepare a technical specification.
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Retrieve GitHub Copilot usage for an organization or enterprise through the GitHub CLI and REST API. Summarize active users, suggestion acceptance, and chat activity, with aggregate results or per-user tables for selected dates.
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Turns implementation requests into concise work plans with file references, testable acceptance criteria, risks, and verification steps. It can clarify vague requirements through focused questions, generate a plan directly, or review an existing plan.
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Run a persistent research workflow with a defined goal and explicit acceptance checks. It saves progress and finishes only when a recorded result passes either a validation script or an architect review of the research output.
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Turn large product requirements into user stories with acceptance criteria, effort estimates, and priorities. The toolkit supports backlog management, sprint planning based on team capacity, and tracking how much work the team completes over time.
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Track candidates from first outreach to offer acceptance, and review time spent at each stage, conversion rates, and recruiting sources. Use these measures to understand hiring progress and where candidates drop out.
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Turn small game changes into concise specifications with clear reasons, rules, and acceptance criteria. Check existing design documents and earlier specifications, then document tuning adjustments, minor behavior changes, or small features before implementation.
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Turn a named NemoClaw issue into an implementation plan with clear ownership, scope, completion criteria, and validation evidence. For larger changes, define independently useful steps, their dependencies, and delivery order.
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Use an English working contract in Trae to address repeated failures or unverified completion. It separates execution, self-review, and acceptance, guiding agents through small checks and delivering results with evidence, residual risks, and confirmation needs.
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Organize Jira issues, plan sprints, and define workflows with clear ownership and acceptance criteria. Get help writing JQL, Jira's query language, to find stalled work, review workloads, and support project reporting.
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Design loops that improve an agent’s own prompts, workflows, or execution code from failure records. Define bounded edits, preserve candidate histories, and accept changes through independent tests that check improvements and regressions.
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Review a GitHub pull request or branch and receive a concise summary of its purpose, design choices, and notable code. Check linked issue acceptance and highlight evidence-based concerns through their observable impact on users.
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Automate LinkedIn messages, connection acceptance, and feed posts with practical browser workflows. The skill explains visual targeting, image uploads, delivery checks, and when to stop if login prompts or account restrictions appear.
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Review code changes with an independent agent from a different model provider. After tests pass, check a fixed diff against acceptance criteria, send blocking issues back to the implementer, and repeat until review passes.
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Check implemented features through conversational acceptance testing, confirming behavior from the user's perspective. Record each result, retain testing progress, and diagnose reported problems to prepare verified repair plans for a subsequent execution step.
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Clarify what a development phase should deliver before discussing implementation. Through structured questions and ambiguity scores, the skill produces SPEC.md with testable requirements, explicit scope boundaries, and acceptance criteria for judging completion.
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Audit acceptance tests, which check whether features meet user needs, and verification records across project phases. Compare outstanding items with the codebase, flag outdated documentation, and produce a prioritized plan for human testing.
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Routes quality checks to the appropriate skill, covering code review and fixes, debugging, security checks, acceptance testing, AI response evaluation, and interface reviews. It also directs requests for validating phase outputs or reviewing plans across AI systems.
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Split large feature requests or user stories, descriptions of what users need, into smaller deliverable pieces. Eight patterns guide the breakdown, with acceptance criteria and checks that each piece provides value and can be developed and tested independently.
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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.
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