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 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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Explore ideas and working methods through focused conversation, one question at a time. Identify recurring patterns, surprising contradictions, and distinctive approaches, then turn discoveries into named concepts and a documented session summary.
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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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Organize multi-step work into tasks, track dependencies and status, and save progress checkpoints. Record completed work, unfinished steps, and decision context so an agent can review the saved state and continue in a later session.
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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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Verify code changes before committing or shipping by running tests, builds, and checks against requirements. Use fresh command output to support completion reports, confirm bug fixes, and check work reported by other agents.
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Research technical, market, or academic topics through multiple available AI providers working in parallel. Combine their findings into a report that preserves disagreements, identifies gaps, attributes sources, and recommends next steps with priorities.
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Interpret the saved results of a completed AI council review. Count verdicts, check whether enough participants responded and whether models are independent, surface substantive dissent, and explain what action the outcome supports.
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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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Start the interactive Claude Octopus setup wizard to install and authenticate providers, configure RTK token optimization, choose a work mode, and complete onboarding. This skill directs configuration requests to the existing setup process.
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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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Check which AI providers Octopus can use before running a workflow. Get a compact table of availability, authentication, selected models, and billing categories, followed by prioritized fixes for detected problems.
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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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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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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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Estimate remaining workflow costs from the average spend of completed steps. Show actual spending and projected totals in the status line, warn about configured budget overruns, and suggest a budget profile when estimated costs are high.
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Audit changed code by tracing execution paths and matching them to existing tests. Show coverage gaps and test quality, then generate tests for high-priority gaps using the project's current framework, helpers, and naming conventions.
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Reviews code for common security weaknesses, authentication problems, exposed credentials, and unsafe configuration. It adjusts review depth to the changes and offers an adversarial workflow, where defensive and attacking perspectives guide fixes and subsequent verification.
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Keep a persistent record of project bugs and blockers in a local Markdown file. List issues by severity and category, inspect individual records, and save resolution notes so problems and their fixes remain available across sessions.
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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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Research complex topics across multiple sources using available external AI providers, then combine the findings. Choose the depth, focus, and output format to receive a detailed report, concise summary, comparison table, or recommendations.
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Review a pull request or feature in two stages: first check implementation against recorded requirements, then examine code quality. Identify unfinished code, logic and security issues, and changes needed before merging in a combined report.
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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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