Bug Triage Assistant
Convert scattered bug notes, Slack messages, or user complaints into structured engineering tasks with reproduction steps, severity, and root cause hypothesis.
Turn a rough feature idea into a structured requirement: problem statement, acceptance criteria, and what's explicitly out of scope.
Feature requests usually arrive as solutions: 'we should add X.' This workflow forces a problem-first structure — what is the user actually unable to do, and how would we know when that's fixed? The output is a scoped requirement with testable acceptance criteria, not a wish list item.
Describe the feature idea
Paste the feature request, Slack message, or call notes. Include who requested it and why if known.
Review the problem statement
The problem statement is the most important output. If it describes a solution instead of a problem, correct it before proceeding.
Validate acceptance criteria
Each criterion should be testable by a QA engineer or user. Replace subjective criteria ('easy', 'fast') with measurable ones.
Resolve open questions before handoff
Open questions left unanswered become mid-sprint surprises. Assign each one to a person or decision point.
It forces a problem-first shape. The Problem Statement section demands the format "[User type] cannot [do X] because [constraint]. This causes [impact]" and explicitly says do not describe the solution. The whole prompt also bars implementation details and technology choices, so a request that arrives as "add feature X" gets reframed into the user problem underneath it.
The Acceptance Criteria section requires a numbered list of verifiable conditions and explicitly bans subjective phrasing like "users can easily..." or "the system is fast...". Paired with the Out of Scope and Open Questions sections, it produces criteria a QA engineer can check pass or fail. The system-prompt-generator outputs this prompt; you run it and still validate each criterion yourself.
When there's no user problem to structure yet. notFor rules out highly technical infrastructure or architecture decisions, requirements that depend entirely on user research that hasn't happened, and regulatory compliance specs needing legal review. Its Non-Functional Requirements section touches compliance constraints, but that's a placeholder for review — not a substitute for legal sign-off.
Convert scattered bug notes, Slack messages, or user complaints into structured engineering tasks with reproduction steps, severity, and root cause hypothesis.
Turn AI into a structured pull request reviewer that catches risky changes, flags maintainability issues, and suggests missing test coverage.
Configure AI to answer support questions within your actual policy boundaries — not generic best-guess answers.
A complete 'act as a product manager' role prompt — the perspective, responsibilities, and decision criteria a real PM brings, not just the job title.
A reusable AI agent task template with variables for objective, context, available tools, constraints, success criteria, failure handling, and output format.
Formats fuzzy agent instructions into a structured prompt with objective, available tools, constraints, success criteria, and failure handling.
Build structured system prompts from role, tone, constraints, and model target.
The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed.
The full path to an AI document processing system — define the use case, design the intake pipeline, extract fields from unstructured documents, classify and route them, pin the output contract, evaluate accuracy, then ship it monitored.
The full path to an AI content moderation system — define the policy and label taxonomy, extract signals from user content, classify it against policy, emit structured decisions, evaluate false positives and negatives, wire enforcement and review queues, review abuse risks, then ship.
The full path to an AI research assistant — define its scope, organize the source corpus, ground responses in references, extract key facts, synthesize findings, check groundedness, then validate it for use.
The full path to an AI meeting assistant — define the use case, turn transcripts into structured notes, extract decisions and action items, classify follow-ups, write a shareable summary, evaluate accuracy, then ready it for the team.
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.
The full path to an applicant tracking system — model jobs, candidates, and hiring stages, generate job descriptions and screening prompts, parse résumés into structured data, design the hiring API, set roles, review security, then ship.
The full path to a booking and reservation system — model resources, availability, and reservations, design the booking API, set customer accounts, wire calendar and notification integrations, design the UI, review security, then ship.
The full path to an inventory management system — model products, stock, locations, and movements, design the inventory API, set roles, plan supplier and POS integrations, design the UI, review data-integrity risks, then ship.
The full path to an internal admin panel — model the operational data it manages, design the management API, define RBAC and admin permissions, design the tables and forms, review risky admin actions, then ship to trusted internal users.
The full path to a two-sided platform — define the buyer-and-seller requirements, model the data, design the API, build roles and permissions, wire integrations, design the UI, then test, secure, and ship it.
The full path to a CRM that fits your sales process — define the contacts, deals, and pipeline, model the data that ties them together, then build the roles, integrations, and pipeline UI, and ship.
The full path to a store you own end to end — model the catalog and orders, design the storefront and checkout, add customer accounts and payments, then secure it, test it, and ship.
Turn a fuzzy business need into requirements a team can build from — interrogate the need into concrete requirements, shape them as user stories, and write the PRD.