Ship cleaner code with an AI pair-programmer that never sleeps.
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This prompt empowers users to systematically diagnose and resolve code issues, ensuring effective problem-solving and implementation of impactful solutions.
This prompt empowers personalized coding instruction, guiding learners through tailored lessons that build confidence and competence step by step.
This prompt empowers teams to systematically analyze failures, fostering accountability and continuous improvement through detailed documentation and structured inquiry.
This prompt streamlines the translation of product requirements into actionable database structures, enhancing project clarity and execution efficiency.
This prompt streamlines incident analysis by systematically uncovering root causes and identifying targeted corrective actions to prevent future failures.
Stay updated on influential leaders' latest insights, empowering you to make informed decisions and strategically navigate your industry.
Identify and eliminate ineffective meetings, optimize schedules, and enhance productivity through actionable insights derived from your calendar data.
This prompt empowers you to strategically identify risks and uncertainties, ensuring a smoother coding process and minimizing costly pivots.
This prompt guides users in navigating complex organizational dynamics to achieve successful decision-making outcomes.
Transform vague aspirations into actionable SMART goals, driving focused progress and measurable success in your personal and professional life.
Diagnose complex failures methodically, uncover root causes with Five Whys, craft targeted fixes, and verify success via actionable metrics and instrumentation.
This prompt enables users to refine vague AI prompts, resulting in clearer, more effective outputs tailored to specific needs and contexts.
This prompt streamlines project delivery by organizing tasks into manageable sprints, ensuring timely completion and transparent communication with clients.
This prompt explains that the agent connects to external systems via MCP (Model Context Protocol) servers.
This prompt will interview you and help you pick the right agent—will work for you personally or for your team as a whole
This prompt describes an agent workflow built on a visual canvas with drag‑and‑drop nodes. Fill in the template to outline each node’s role, data flow, and handoffs.
This prompt defines your agent’s safety boundaries through clear, enforceable rules. Complete the template to specify allowed actions, prohibited behaviors, and escalation paths.
This prompt centralizes agent logic and control flow in the API layer. It defines the agent—not individual chat turns—as the fundamental unit.
This prompt guides the design of test cases for your agent. Define inputs, expected outputs, edge conditions, and pass/fail criteria to validate behavior end-to-end.
This prompt defines when and how humans should intervene in the agent’s workflow. Specify triggers, roles, and decision points for review, approval, and override.
This prompt guides you to write a complete agent specification by filling in the provided template. Document goals, capabilities, tools, interfaces, safety rules, and evaluation criteria for end-to-end clarity.
This prompt outlines recovery strategies for each potential failure point. Specify detection signals, fallback actions, and escalation steps for quick, safe resolution.
This prompt helps translate an existing SOP or flow into a ChatGPT agent design. It maps each step to agent roles, tools, and handoffs for reliable execution.
This assessment evaluates qualifications for AI Research Scientist roles requiring PhD, publications, and deep mathematical expertise. It provides honest verdict on research background with premium compensation expectations (up to $489K at Meta) and 2-4 year timeline for PhD candidates.
This assessment determines readiness for Deep Learning Engineer positions by evaluating neural network expertise, framework mastery, and production deployment experience. It delivers qualification verdict with premium salary expectations ($130K-$270K) and 6-24 month roadmap.
This prompt evaluates technical docs on setup clarity, code example accuracy, troubleshooting coverage, architecture explanation, and API documentation completeness. It catches incomplete docs and missing examples with specific improvements for developer usability.
This assessment evaluates your qualifications for Machine Learning Engineer roles through 8 targeted questions about Python proficiency, ML framework experience, and production deployment. It provides honest qualification verdict, personalized roadmap, and salary expectations based on 2025 market data.
This assessment determines your readiness for Computer Vision Engineer positions by evaluating ML background, vision-specific experience, and domain applications. It delivers qualification status, specific skill gaps, and 3-18 month development timeline.
This assessment evaluates qualifications for Data Annotator positions requiring attention to detail and basic computer skills. It provides truly entry-level path starting at $40K-$60K with progression to $120K+ specialist roles through stepping-stone strategy.
This assessment evaluates your qualifications for Natural Language Processing Engineer roles through questions about ML foundations, NLP frameworks, and language model experience. It provides realistic timeline, skill gaps, and salary ranges based on post-ChatGPT market demand.
This comprehensive evaluation framework assesses API designs against your team's coding standards and provides specific fixes for each issue found. It reviews endpoint structure, authentication patterns, error handling, documentation completeness, and performance considerations with concrete remediation guidance.
This systematic investigation tool maps incident timelines, identifies contributing factors, and produces prevention measures based on your specific system architecture. It conducts comprehensive post-mortem analysis with focus on systemic issues rather than individual blame, generating actionable remediation plans.
This framework takes your backlog, team capacity, and roadmap to generate a prioritized technical debt plan with effort estimates and business impact. It structures systematic evaluation of debt items, considers team velocity constraints, and produces actionable prioritization aligned with strategic roadmap goals.
Template for software development decomposing tasks into analysis design implementation testing and documentation with explicit code quality requirements error handling validation and edge case testing.
This creates strategic engineering memos for leadership by structuring context, strategic goals, technical vision, organizational changes, investment requirements, and risk mitigation. It produces business-aware documentation that connects technical decisions to company outcomes.
Automated SoD (Segregation of Duties) conflict detection, orphaned account identification, and stale permission cleanup. Requires critical access attestation before export and prevents completion with unresolved SoD conflicts.
Real-time incident tracking with SLO monitoring, MTTR calculation, severity-based post-mortem requirements, and action assignment. Validates oncall IC assignment, tracks comms owner for customer-facing incidents, and requires P2+ post-mortems.
Monitors data freshness, null rates, duplicate detection, and semantic drift across tables with threshold validation. Includes freshness SLA checks, automated duplicate detection bypass with reasoning, and drift baseline comparison for production data health.
This produces conversational technical specs by structuring project context, technical approach, scope boundaries, key decisions with tradeoffs, and open questions. It creates pragmatic collaborative documentation that balances implementation speed with technical clarity.
This conversational prompt interviews you through 16 strategic questions to design a complete memory system for AI-assisted work. It produces a comprehensive architecture document including storage maps, lifecycle rules, retrieval patterns, verification protocols, and a phased implementation roadmap.
This structures comprehensive technical design documentation by capturing problem, proposed solution, architecture, alternatives considered, implementation plan, and testing strategy. It produces thorough balanced documentation with technical depth for proper evaluation of tradeoffs.
This transforms messy project materials into clean AI-optimized briefs through structured questioning that separates confirmed facts from assumptions. It creates concise documentation with verified data, clear scope boundaries, and explicit constraints ready for AI context windows.
Multi-function launch go/no-go gate with evidence requirements, staged rollout validation, and mandatory approval workflows. Tests confirm all evidence present, thresholds met, rollback plans in place, and required role approvals obtained before launch clearance.
This structures technical decision documentation for engineering teams and leadership by capturing problem statement, current system, proposed solution, tradeoffs, and implementation plan. It produces RFCs with technical depth while keeping executive summaries accessible.
This designs reusable retrieval patterns across your entire task spectrum by mapping work modes and information needs through conversational analysis. It outputs a comprehensive retrieval strategy matrix showing task type, retrieval approach, verification needs, and mode-based patterns for planning, execution, and review.
This guides you through building three permanent memory documents via conversational interview covering Profile & Preferences, Work Playbooks, and References. It extracts your core work knowledge into portable markdown files that work across any AI tool.