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Verify work before reporting it as complete by running fresh checks and reading their results. Match each claim to evidence, including test output, build results, bug reproduction, code changes, and requirement checks.
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Turn a technical choice into a structured debate between different AI model families. Assign explicit positions, launch the discussion, and summarize agreement, disagreement, and a recommendation supported by the strongest argument that survives the debate.
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Turn an agreed plan into small work tickets, each covering a complete, testable feature across the necessary layers. Identify dependencies and available starting points so people or future agent sessions can pick up clearly bounded work.
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Turn screenshot-based UI feedback into code fixes. Identify layout, styling, component, and state issues, trace their causes, agree on the scope of changes, and verify visual consistency across screen sizes and interaction states.
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Develop behavior changes by first observing an expected test failure, then making the smallest passing implementation. Refactor with tests passing, and document preserved behavior and measured runtime whenever tests are consolidated.
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Turn a multi-step feature into an implementation plan that another engineer can follow without prior context. The plan includes exact file paths, complete code, test commands, expected results, and small, verifiable steps.
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Compare estimated AI model costs for a task using its complexity and expected text volume. Get a suitable low-cost recommendation, a cheaper but riskier alternative, and a premium option, with explicit assumptions behind the estimates.
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Sort incoming issues and pull requests, verify their claims, and assign clear triage states. Record missing information, decisions needed, and actionable handoff notes so maintainers can see which items are ready for work.
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Compare technical choices through structured discussions among available AI providers. Use independent reviews or rounds of cross-critique to examine risks and trade-offs, then receive a synthesis of agreement, disagreement, recommended decisions, and concrete next steps.
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Read project state and roadmap files to show the current phase, progress, blockers, and suggested next actions. Summarize recent work across sessions and suggest lasting project knowledge to add to CLAUDE.md.
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Assess whether a multi-agent workflow justifies its coordination costs. Examine handoffs, lost context, and distinct contributions, then compare the setup with one capable agent to recommend keeping, simplifying, or restructuring it.
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Analyze reference articles and social posts to uncover structure, persuasive techniques, and writing patterns. Turn the findings into reusable templates, opening patterns, and interview questions for planning new content.
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Restore project files from a previous Git checkpoint after previewing affected files and receiving explicit confirmation. Create a safety tag before restoration, preserve recorded lessons, and keep the current branch and commit history in place.
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Create a product requirements document for a new feature or product. Clarify users, problems, goals, and constraints, then organize prioritized requirements, acceptance criteria, implementation phases, and risks into a structured document for AI-assisted development.
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Test one risky assumption through a prototype with an agreed deadline, hypothesis, and success signal. Record observations and the source revision, then return a keep, discard, or inconclusive verdict with the status of each artifact.
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Record the goal, success criteria, boundaries, and constraints of a complex task in a persistent document. Define human and AI responsibilities, consult the record during work, and check the final result against the original intent.
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Examine an existing plan, decision, or design through one question at a time, each with a suggested answer. Clarify assumptions, contradictions, and dependencies, then summarize agreed decisions, needed changes, and unresolved blockers.
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Evaluate feedback from reviewers, CI bots, or other AI agents against the actual code and requirements. Apply valid fixes one at a time, verify each change, and explain disagreements using evidence from callers, tests, or existing conventions.
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Bring project Markdown documentation into line with code changes. Update factual references such as paths, versions, and counts, check consistency and document links, and reflect the documentation work in the pull request description.
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Turn a rough request into a structured AI prompt with a clear goal, output format, and constraints. For complex work, add smaller tasks, separate creator and reviewer roles, and explicit checks for accuracy and uncertainty.
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Investigate reproducible bugs by recreating symptoms, testing one possible cause at a time, and checking fixes in both a simplified test case and the original scenario. Retry limits keep unsuccessful fixes from continuing unchecked.
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Gather advice from multiple language models assigned different roles, then combine their proposals and critiques into a recommendation. Set budget and depth limits while retaining disagreements, minority views, and risks for decisions or implementation planning.
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Compare two to four viable approaches against your requirements and constraints. See each option’s benefits, drawbacks, effort, risk, and ease of reversal, alongside a reasoned recommendation and next steps for the option you choose.
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Detects whether a task concerns software development or research and strategy by examining the request and project files. Workflow skills can use the result to choose agents, output formats, and review criteria suited to the work.
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