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Verify that existing work meets its acceptance conditions using the smallest sufficient set of checks. Reuse results that still match the repository state, report outcomes precisely, and stop once the evidence is complete without adding unrelated work.
Turn a feature idea into a structured specification before coding. Review requirements, system connections, constraints, and failure scenarios, define testable acceptance criteria, and save the specification in memory for later development phases.
Review an AI team's work against its acceptance criteria using collected evidence. Match requirements to task results or artifacts, distinguish verified facts from agent claims, and identify uncertainty or missing work before recommending completion.
Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.
Evaluate job offers by separating guaranteed pay, conditional bonuses, and estimated equity value. Compare terms, develop an evidence-based counteroffer, draft emails and call scripts, rehearse recruiter conversations, and prepare deadline extensions, acceptance, or decline messages.
Turn a Codewhale bug or feature idea into an actionable GitHub issue with evidence, reproduction steps, and testable acceptance criteria. Check related work, select existing labels and a milestone, and return the filed issue with supporting evidence.
Turn a feature idea or user problem into a structured specification or product requirements document. Define goals, scope, priorities, success measures, and acceptance criteria, with phased delivery when the request is too large.
Plan features before coding by defining requirements, acceptance criteria, and scope. Validate the specification, generate starter tests from expected behaviors, and guide implementation and review so each delivered feature can be checked against its agreed requirements.
Turn user stories and acceptance criteria into detailed test scenarios. Define objectives, starting conditions, user roles, actions, and observable results, including invalid inputs and boundary cases, so a quality assurance team can carry out the checks.
Break a feature into job stories that describe the user's situation, motivation, and desired outcome. Add six to eight observable acceptance criteria per story, defining how success will be checked, and include links to designs or prototypes.
Review a completed development task against its acceptance criteria, check differences from design and architecture documents, and prompt code review. After approval, update the task's status and identify the next story to work on.
Break a large game development feature into implementation tasks with clear acceptance criteria. Each task carries relevant requirements and design decisions, with test specifications added when the selected review mode includes a QA review.
Use this design reference when planning centrally managed rules for outbound network traffic from a sandbox, an isolated execution environment. It covers design and acceptance gates, but does not itself enforce network restrictions.
Turn rough feature ideas into a clear SPEC.md through focused questions. Define target users, business rules, current scope, and acceptance criteria based on observable results, giving developers or AI agents concrete requirements to implement.
Translate user needs into concise stories that explain who benefits, what they want, and why. Add Gherkin acceptance criteria—preconditions, an action, and an expected result—to support development discussions and testable definitions of success.
Compare a Linear issue and its acceptance criteria with implementation evidence. Identify gaps, retain links to tests and pull requests, and update progress only when the relevant implementation, review, or deployment milestone is supported.
Use when reviewing whether an OpenSpec change's execution goal, acceptance criteria, and implementation or verification tasks are aligned. This should trigger for requests such as Review this OpenSpec change with ATDD; Check acceptance criteria against tasks; Find acceptance criteria without task coverage; Detect tasks that diverge from the execution goal; Explain what is missing, vague, ambiguous, partial, absent, or divergent in this OpenSpec change. Part of Plinth Toolkit
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Write acceptance speeches for awards and honors. TRIGGERS - Use when user needs help with acceptance-speech related tasks.
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Write clear acceptance criteria for features. TRIGGERS - Use when user needs help with acceptance-criteria related tasks.
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Generate a User Acceptance Testing plan with scenarios and sign-off criteria. Use when the user says "UAT plan", "acceptance testing", "user testing plan", "how do we test this with users", "test scenarios for business validation", "sign-off criteria", "go/no-go for release", "acceptance test cases", "validate with stakeholders" - even if they don't explicitly say "UAT".
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Use this skill when designing the human-executable UAT test cases that prove a Salesforce feature works for a specific persona — the per-persona scripts with preconditions, data setup, permission setup, ordered steps, and pass/fail evidence that close out an RTM row. Trigger keywords: UAT test case salesforce, user acceptance test script salesforce, manual test salesforce feature, UAT script per persona, UAT precondition data setup, UAT permission set assignment for testing, negative path UAT case. NOT for high-level UAT planning, defect classification, regression strategy, or sign-off (use admin/uat-and-acceptance-criteria). NOT for the Given/When/Then technique used to write the AC the case is derived from (use admin/acceptance-criteria-given-when-then). NOT for Apex test method generation (use agents/test-class-generator). NOT for test data factory patterns (use templates/apex/tests/TestDataFactory.cls). NOT for performance or load testing.
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Use this skill when authoring INVEST-compliant Salesforce user stories from gathered requirements: shaping the As-A/I-Want/So-That stem, writing Given-When-Then acceptance criteria, sizing complexity (S/M/L/XL), splitting stories that are too large for a sprint, and emitting handoff metadata that names the next downstream agent. Trigger keywords: user story, INVEST, story splitting, story sizing, As-A I-Want So-That, story handoff, recommended_agents, backlog item shape. NOT for requirements elicitation or stakeholder interviews (use admin/requirements-gathering-for-sf). NOT for the Given-When-Then technique itself in depth (use admin/acceptance-criteria-given-when-then). NOT for UAT test case design (use admin/uat-test-case-design). NOT for backlog prioritization (use admin/moscow-prioritization-for-sf-backlog).
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Write clear acceptance criteria for features. TRIGGERS - Use when user needs help with acceptance-criteria related tasks.
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Write acceptance speeches for awards and honors. TRIGGERS - Use when user needs help with acceptance-speech related tasks.
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